A new algorithm for epilepsy seizure onset detection and spread estimation from EEG signals

NASA Astrophysics Data System (ADS)

Quintero-Rincón, Antonio; Pereyra, Marcelo; D'Giano, Carlos; Batatia, Hadj; Risk, Marcelo

2016-04-01

Appropriate diagnosis and treatment of epilepsy is a main public health issue. Patients suffering from this disease often exhibit different physical characterizations, which result from the synchronous and excessive discharge of a group of neurons in the cerebral cortex. Extracting this information using EEG signals is an important problem in biomedical signal processing. In this work we propose a new algorithm for seizure onset detection and spread estimation in epilepsy patients. The algorithm is based on a multilevel 1-D wavelet decomposition that captures the physiological brain frequency signals coupled with a generalized gaussian model. Preliminary experiments with signals from 30 epilepsy crisis and 11 subjects, suggest that the proposed methodology is a powerful tool for detecting the onset of epilepsy seizures with his spread across the brain.

Patient-Specific Seizure Detection in Long-Term EEG Using Signal-Derived Empirical Mode Decomposition (EMD)-based Dictionary Approach.

PubMed

Kaleem, Muhammad; Gurve, Dharmendra; Guergachi, Aziz; Krishnan, Sridhar

2018-06-25

The objective of the work described in this paper is development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings. Approach: A novel patient-specific seizure detection approach based on signal-derived Empirical Mode Decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches. Main results: The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability of the approach for seizure detection in long-term multi-channel EEG recordings is discussed. Significance: The proposed approach describes a computationally efficient method for automatic seizure detection in long-term multi-channel EEG recordings. The method does not rely on hand-engineered features, as are required in traditional approaches. Furthermore, the approach is suitable for scenarios where the dictionary once formed and trained can be used for automatic seizure detection of newly recorded data, making the approach suitable for long-term multi-channel EEG recordings. © 2018 IOP Publishing Ltd.

A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models

PubMed Central

Wong, Stephen; Gardner, Andrew B.; Krieger, Abba M.; Litt, Brian

2007-01-01

Responsive, implantable stimulation devices to treat epilepsy are now in clinical trials. New evidence suggests that these devices may be more effective when they deliver therapy before seizure onset. Despite years of effort, prospective seizure prediction, which could improve device performance, remains elusive. In large part, this is explained by lack of agreement on a statistical framework for modeling seizure generation and a method for validating algorithm performance. We present a novel stochastic framework based on a three-state hidden Markov model (HMM) (representing interictal, preictal, and seizure states) with the feature that periods of increased seizure probability can transition back to the interictal state. This notion reflects clinical experience and may enhance interpretation of published seizure prediction studies. Our model accommodates clipped EEG segments and formalizes intuitive notions regarding statistical validation. We derive equations for type I and type II errors as a function of the number of seizures, duration of interictal data, and prediction horizon length and we demonstrate the model’s utility with a novel seizure detection algorithm that appeared to predicted seizure onset. We propose this framework as a vital tool for designing and validating prediction algorithms and for facilitating collaborative research in this area. PMID:17021032

Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm

PubMed Central

Nanthini, B. Suguna; Santhi, B.

2017-01-01

Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully. Materials and Methods: The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases. Results: The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier. Conclusion: Relevant features using GA give better accuracy performance for seizure detection. PMID:28781480

Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy.

PubMed

Ramgopal, Sriram; Thome-Souza, Sigride; Jackson, Michele; Kadish, Navah Ester; Sánchez Fernández, Iván; Klehm, Jacquelyn; Bosl, William; Reinsberger, Claus; Schachter, Steven; Loddenkemper, Tobias

2014-08-01

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy. Copyright © 2014. Published by Elsevier Inc.

Automatic Seizure Detection in Rats Using Laplacian EEG and Verification with Human Seizure Signals

PubMed Central

Feltane, Amal; Boudreaux-Bartels, G. Faye; Besio, Walter

2012-01-01

Automated detection of seizures is still a challenging problem. This study presents an approach to detect seizure segments in Laplacian electroencephalography (tEEG) recorded from rats using the tripolar concentric ring electrode (TCRE) configuration. Three features, namely, median absolute deviation, approximate entropy, and maximum singular value were calculated and used as inputs into two different classifiers: support vector machines and adaptive boosting. The relative performance of the extracted features on TCRE tEEG was examined. Results are obtained with an overall accuracy between 84.81 and 96.51%. In addition to using TCRE tEEG data, the seizure detection algorithm was also applied to the recorded EEG signals from Andrzejak et al. database to show the efficiency of the proposed method for seizure detection. PMID:23073989

Automatic identification of epileptic seizures from EEG signals using linear programming boosting.

PubMed

Hassan, Ahnaf Rashik; Subasi, Abdulhamit

2016-11-01

Computerized epileptic seizure detection is essential for expediting epilepsy diagnosis and research and for assisting medical professionals. Moreover, the implementation of an epilepsy monitoring device that has low power and is portable requires a reliable and successful seizure detection scheme. In this work, the problem of automated epilepsy seizure detection using singe-channel EEG signals has been addressed. At first, segments of EEG signals are decomposed using a newly proposed signal processing scheme, namely complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Six spectral moments are extracted from the CEEMDAN mode functions and train and test matrices are formed afterward. These matrices are fed into the classifier to identify epileptic seizures from EEG signal segments. In this work, we implement an ensemble learning based machine learning algorithm, namely linear programming boosting (LPBoost) to perform classification. The efficacy of spectral features in the CEEMDAN domain is validated by graphical and statistical analyses. The performance of CEEMDAN is compared to those of its predecessors to further inspect its suitability. The effectiveness and the appropriateness of LPBoost are demonstrated as opposed to the commonly used classification models. Resubstitution and 10 fold cross-validation error analyses confirm the superior algorithm performance of the proposed scheme. The algorithmic performance of our epilepsy seizure identification scheme is also evaluated against state-of-the-art works in the literature. Experimental outcomes manifest that the proposed seizure detection scheme performs better than the existing works in terms of accuracy, sensitivity, specificity, and Cohen's Kappa coefficient. It can be anticipated that owing to its use of only one channel of EEG signal, the proposed method will be suitable for device implementation, eliminate the onus of clinicians for analyzing a large bulk of data manually, and expedite epilepsy diagnosis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Patient-Specific Early Seizure Detection from Scalp EEG

PubMed Central

Minasyan, Georgiy R.; Chatten, John B.; Chatten, Martha Jane; Harner, Richard N.

2010-01-01

Objective Develop a method for automatic detection of seizures prior to or immediately after clinical onset using features derived from scalp EEG. Methods This detection method is patient-specific. It uses recurrent neural networks and a variety of input features. For each patient we trained and optimized the detection algorithm for two cases: 1) during the period immediately preceding seizure onset, and 2) during the period immediately following seizure onset. Continuous scalp EEG recordings (duration 15 – 62 h, median 25 h) from 25 patients, including a total of 86 seizures, were used in this study. Results Pre-onset detection was successful in 14 of the 25 patients. For these 14 patients, all of the testing seizures were detected prior to seizure onset with a median pre-onset time of 51 sec and false positive rate was 0.06/h. Post-onset detection had 100% sensitivity, 0.023/hr false positive rate and median delay of 4 sec after onset. Conclusions The unique results of this study relate to pre-onset detection. Significance Our results suggest that reliable pre-onset seizure detection may be achievable for a significant subset of epilepsy patients without use of invasive electrodes. PMID:20461014

Sensor integration of multiple tripolar concentric ring electrodes improves pentylenetetrazole-induced seizure onset detection in rats.

PubMed

Makeyev, Oleksandr; Ding, Quan; Kay, Steven M; Besio, Walter G

2012-01-01

As epilepsy affects approximately one percent of the world population, electrical stimulation of the brain has recently shown potential for additive seizure control therapy. Previously, we applied noninvasive transcranial focal stimulation via tripolar concentric ring electrodes on the scalp of rats after inducing seizures with pentylenetetrazole. We developed a system to detect seizures and automatically trigger the stimulation and evaluated the system on the electrographic activity from rats. In this preliminary study we propose and validate a novel seizure onset detection algorithm based on exponentially embedded family. Unlike the previously proposed approach it integrates the data from multiple electrodes allowing an improvement of the detector performance.

Detection of convulsive seizures using surface electromyography.

PubMed

Beniczky, Sándor; Conradsen, Isa; Wolf, Peter

2018-06-01

Bilateral (generalized) tonic-clonic seizures (TCS) increase the risk of sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. In sleep, TCS often remain unnoticed, which can result in suboptimal treatment decisions. There is a need for automated detection of these major epileptic seizures, using wearable devices. Quantitative surface electromyography (EMG) changes are specific for TCS and characterized by a dynamic evolution of low- and high-frequency signal components. Algorithms targeting increase in high-frequency EMG signals constitute biomarkers of TCS; they can be used both for seizure detection and for differentiating TCS from convulsive nonepileptic seizures. Two large-scale, blinded, prospective studies demonstrated the accuracy of wearable EMG devices for detecting TCS with high sensitivity (76%-100%). The rate of false alarms (0.7-2.5/24 h) needs further improvement. This article summarizes the pathophysiology of muscle activation during convulsive seizures and reviews the published evidence on the accuracy of EMG-based seizure detection. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

Peri-ictal ECG changes in childhood epilepsy: implications for detection systems.

PubMed

Jansen, Katrien; Varon, Carolina; Van Huffel, Sabine; Lagae, Lieven

2013-10-01

Early detection of seizures could reduce associated morbidity and mortality and improve the quality of life of patients with epilepsy. In this study, the aim was to investigate whether ictal tachycardia is present in focal and generalized epileptic seizures in children. We sought to predict in which type of seizures tachycardia can be identified before actual seizure onset. Electrocardiogram segments in 80 seizures were analyzed in time and frequency domains before and after the onset of epileptic seizures on EEG. These ECG parameters were analyzed to find the most informative ones that can be used for seizure detection. The algorithm of Leutmezer et al. was used to find the temporal relationship between the change in heart rate and seizure onset. In the time domain, the mean RR shows a significant difference before compared to after onset of the seizure in focal seizures. This can be observed in temporal lobe seizures as well as frontal lobe seizures. Calculation of mean RR interval has a high specificity for detection of ictal heart rate changes. Preictal heart rate changes are observed in 70% of the partial seizures. Ictal heart rate changes are present only in partial seizures in this childhood epilepsy study. The changes can be observed in temporal lobe seizures as well as in frontal lobe seizures. Heart rate changes precede seizure onset in 70% of the focal seizures, making seizure detection and closed-loop systems a possible therapeutic alternative in the population of children with refractory epilepsy. © 2013.

Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection.

PubMed

Bogaarts, J G; Hilkman, D M W; Gommer, E D; van Kranen-Mastenbroek, V H J M; Reulen, J P H

2016-12-01

Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential. Recently, the median decaying memory (MDM) approach was determined to be the best method of normalization. MDM uses a sliding baseline buffer of EEG epochs to calculate feature normalization constants. However, while this method does include non-seizure EEG epochs, it also includes EEG activity that can have a detrimental effect on the normalization and subsequent seizure detection performance. In this study, EEG data that is to be incorporated into the baseline buffer are automatically selected based on a novelty detection algorithm (Novelty-MDM). Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity-specificity ROC curve. This evaluation compares three different EEG normalization methods, namely a fixed baseline buffer (FB), the median decaying memory (MDM) approach, and our novelty median decaying memory (Novelty-MDM) method. It is demonstrated that MDM did not improve overall performance compared to FB (p < 0.27), partly because seizure like episodes were included in the baseline. More importantly, Novelty-MDM significantly outperforms both FB (p = 0.015) and MDM (p = 0.0065).

A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG

PubMed Central

Chen, Duo; Wan, Suiren; Xiang, Jing; Bao, Forrest Sheng

2017-01-01

In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has been widely used in computer-aided signal analysis of epileptic electroencephalography (EEG), such as the detection of seizures. One of the important hurdles in the applications of DWT is the settings of DWT, which are chosen empirically or arbitrarily in previous works. The objective of this study aimed to develop a framework for automatically searching the optimal DWT settings to improve accuracy and to reduce computational cost of seizure detection. To address this, we developed a method to decompose EEG data into 7 commonly used wavelet families, to the maximum theoretical level of each mother wavelet. Wavelets and decomposition levels providing the highest accuracy in each wavelet family were then searched in an exhaustive selection of frequency bands, which showed optimal accuracy and low computational cost. The selection of frequency bands and features removed approximately 40% of redundancies. The developed algorithm achieved promising performance on two well-tested EEG datasets (accuracy >90% for both datasets). The experimental results of the developed method have demonstrated that the settings of DWT affect its performance on seizure detection substantially. Compared with existing seizure detection methods based on wavelet, the new approach is more accurate and transferable among datasets. PMID:28278203

Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique

NASA Astrophysics Data System (ADS)

Tieng, Quang M.; Anbazhagan, Ashwin; Chen, Min; Reutens, David C.

2017-12-01

Objective. Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. Approach. A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. Main result. The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. Significance. The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures.

Automatic seizure detection in SEEG using high frequency activities in wavelet domain.

PubMed

Ayoubian, L; Lacoma, H; Gotman, J

2013-03-01

Existing automatic detection techniques show high sensitivity and moderate specificity, and detect seizures a relatively long time after onset. High frequency (80-500 Hz) activity has recently been shown to be prominent in the intracranial EEG of epileptic patients but has not been used in seizure detection. The purpose of this study is to investigate if these frequencies can contribute to seizure detection. The system was designed using 30 h of intracranial EEG, including 15 seizures in 15 patients. Wavelet decomposition, feature extraction, adaptive thresholding and artifact removal were employed in training data. An EMG removal algorithm was developed based on two features: Lack of correlation between frequency bands and energy-spread in frequency. Results based on the analysis of testing data (36 h of intracranial EEG, including 18 seizures) show a sensitivity of 72%, a false detection of 0.7/h and a median delay of 5.7 s. Missed seizures originated mainly from seizures with subtle or absent high frequencies or from EMG removal procedures. False detections were mainly due to weak EMG or interictal high frequency activities. The system performed sufficiently well to be considered for clinical use, despite the exclusive use of frequencies not usually considered in clinical interpretation. High frequencies have the potential to contribute significantly to the detection of epileptic seizures. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

Automatic seizure detection in SEEG using high frequency activities in wavelet domain

PubMed Central

Ayoubian, L.; Lacoma, H.; Gotman, J.

2015-01-01

Existing automatic detection techniques show high sensitivity and moderate specificity, and detect seizures a relatively long time after onset. High frequency (80–500 Hz) activity has recently been shown to be prominent in the intracranial EEG of epileptic patients but has not been used in seizure detection. The purpose of this study is to investigate if these frequencies can contribute to seizure detection. The system was designed using 30 h of intracranial EEG, including 15 seizures in 15 patients. Wavelet decomposition, feature extraction, adaptive thresholding and artifact removal were employed in training data. An EMG removal algorithm was developed based on two features: Lack of correlation between frequency bands and energy-spread in frequency. Results based on the analysis of testing data (36 h of intracranial EEG, including 18 seizures) show a sensitivity of 72%, a false detection of 0.7/h and a median delay of 5.7 s. Missed seizures originated mainly from seizures with subtle or absent high frequencies or from EMG removal procedures. False detections were mainly due to weak EMG or interictal high frequency activities. The system performed sufficiently well to be considered for clinical use, despite the exclusive use of frequencies not usually considered in clinical interpretation. High frequencies have the potential to contribute significantly to the detection of epileptic seizures. PMID:22647836

The design and hardware implementation of a low-power real-time seizure detection algorithm

NASA Astrophysics Data System (ADS)

Raghunathan, Shriram; Gupta, Sumeet K.; Ward, Matthew P.; Worth, Robert M.; Roy, Kaushik; Irazoqui, Pedro P.

2009-10-01

Epilepsy affects more than 1% of the world's population. Responsive neurostimulation is emerging as an alternative therapy for the 30% of the epileptic patient population that does not benefit from pharmacological treatment. Efficient seizure detection algorithms will enable closed-loop epilepsy prostheses by stimulating the epileptogenic focus within an early onset window. Critically, this is expected to reduce neuronal desensitization over time and lead to longer-term device efficacy. This work presents a novel event-based seizure detection algorithm along with a low-power digital circuit implementation. Hippocampal depth-electrode recordings from six kainate-treated rats are used to validate the algorithm and hardware performance in this preliminary study. The design process illustrates crucial trade-offs in translating mathematical models into hardware implementations and validates statistical optimizations made with empirical data analyses on results obtained using a real-time functioning hardware prototype. Using quantitatively predicted thresholds from the depth-electrode recordings, the auto-updating algorithm performs with an average sensitivity and selectivity of 95.3 ± 0.02% and 88.9 ± 0.01% (mean ± SEα = 0.05), respectively, on untrained data with a detection delay of 8.5 s [5.97, 11.04] from electrographic onset. The hardware implementation is shown feasible using CMOS circuits consuming under 350 nW of power from a 250 mV supply voltage from simulations on the MIT 180 nm SOI process.

Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder.

PubMed

Jeppesen, J; Beniczky, S; Fuglsang Frederiksen, A; Sidenius, P; Johansen, P

2017-07-01

Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG seems promising for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. We used the portable CE marked ePatch® heart monitor to record the ECG of 14 patients, who were enrolled in the videoEEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients were used as training set of data for the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Qand S-peaks can create in the tachogram, which causes error in short-term HRVanalysis. The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979%) and positive predictive value (P+ = 99.976%), which was comparable with a previously published QRS-detection algorithm for the ePatch® ECG device, when testing the same dataset. The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy, creating the possibility for real-time seizure detection for these patients.

Control of epileptic seizures in WAG/Rij rats by means of brain-computer interface

NASA Astrophysics Data System (ADS)

Makarov, Vladimir V.; Maksimenko, Vladimir A.; van Luijtelaar, Gilles; Lüttjohann, Annika; Hramov, Alexander E.

2018-02-01

The main issue of epileptology is the elimination of epileptic events. This can be achieved by a system that predicts the emergence of seizures in conjunction with a system that interferes with the process that leads to the onset of seizure. The prediction of seizures remains, for the present, unresolved in the absence epilepsy, due to the sudden onset of seizures. We developed an algorithm for predicting seizures in real time, evaluated it and implemented it into an online closed-loop brain stimulation system designed to prevent typical for the absence of epilepsy of spike waves (SWD) in the genetic rat model. The algorithm correctly predicts more than 85% of the seizures and the rest were successfully detected. Unlike the old beliefs that SWDs are unpredictable, current results show that they can be predicted and that the development of systems for predicting and preventing closed-loop capture is a feasible step on the way to intervention to achieve control and freedom from epileptic seizures.

A prospective, multicenter study of cardiac-based seizure detection to activate vagus nerve stimulation.

PubMed

Boon, Paul; Vonck, Kristl; van Rijckevorsel, Kenou; El Tahry, Riem; Elger, Christian E; Mullatti, Nandini; Schulze-Bonhage, Andreas; Wagner, Louis; Diehl, Beate; Hamer, Hajo; Reuber, Markus; Kostov, Hrisimir; Legros, Benjamin; Noachtar, Soheyl; Weber, Yvonne G; Coenen, Volker A; Rooijakkers, Herbert; Schijns, Olaf E M G; Selway, Richard; Van Roost, Dirk; Eggleston, Katherine S; Van Grunderbeek, Wim; Jayewardene, Amara K; McGuire, Ryan M

2015-11-01

This study investigates the performance of a cardiac-based seizure detection algorithm (CBSDA) that automatically triggers VNS (NCT01325623). Thirty-one patients with drug resistant epilepsy were evaluated in an epilepsy monitoring unit (EMU) to assess algorithm performance and near-term clinical benefit. Long-term efficacy and safety were evaluated with combined open and closed-loop VNS. Sixty-six seizures (n=16 patients) were available from the EMU for analysis. In 37 seizures (n=14 patients) a ≥ 20% heart rate increase was found and 11 (n=5 patients) were associated with ictal tachycardia (iTC, 55% or 35 bpm heart rate increase, minimum of 100 bpm). Multiple CBSDA settings achieved a sensitivity of ≥ 80%. False positives ranged from 0.5 to 7.2/h. 27/66 seizures were stimulated within ± 2 min of seizure onset. In 10/17 of these seizures, where triggered VNS overlapped with ongoing seizure activity, seizure activity stopped during stimulation. Physician-scored seizure severity (NHS3-scale) showed significant improvement for complex partial seizures (CPS) at EMU discharge and through 12 months (p<0.05). Patient-scored seizure severity (total SSQ score) showed significant improvement at 3 and 6 months. Quality of life (total QOLIE-31-P score) showed significant improvement at 12 months. The responder rate (≥ 50% reduction in seizure frequency) at 12 months was 29.6% (n=8/27). Safety profiles were comparable to prior VNS trials. The investigated CBSDA has a high sensitivity and an acceptable specificity for triggering VNS. Despite the moderate effects on seizure frequency, combined open- and closed-loop VNS may provide valuable improvements in seizure severity and QOL in refractory epilepsy patients. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Toward a noninvasive automatic seizure control system in rats with transcranial focal stimulations via tripolar concentric ring electrodes

PubMed Central

Makeyev, Oleksandr; Liu, Xiang; Luna-Munguía, Hiram; Rogel-Salazar, Gabriela; Mucio-Ramirez, Samuel; Liu, Yuhong; Sun, Yan L.; Kay, Steven M.; Besio, Walter G.

2012-01-01

Epilepsy affects approximately one percent of the world population. Antiepileptic drugs are ineffective in approximately 30% of patients and have side effects. We are developing a noninvasive, or minimally invasive, transcranial focal electrical stimulation system through our novel tripolar concentric ring electrodes to control seizures. In this study we demonstrate feasibility of an automatic seizure control system in rats with pentylenetetrazole-induced seizures through single and multiple stimulations. These stimulations are automatically triggered by a real-time electrographic seizure activity detector based on a disjunctive combination of detections from a cumulative sum algorithm and a generalized likelihood ratio test. An average seizure onset detection accuracy of 76.14% was obtained for the test set (n = 13). Detection of electrographic seizure activity was accomplished in advance of the early behavioral seizure activity in 76.92% of the cases. Automatically triggered stimulation significantly (p = 0.001) reduced the electrographic seizure activity power in the once stimulated group compared to controls in 70% of the cases. To the best of our knowledge this is the first closed-loop automatic seizure control system based on noninvasive electrical brain stimulation using tripolar concentric ring electrode electrographic seizure activity as feedback. PMID:22772373

Toward a noninvasive automatic seizure control system in rats with transcranial focal stimulations via tripolar concentric ring electrodes.

PubMed

Makeyev, Oleksandr; Liu, Xiang; Luna-Munguía, Hiram; Rogel-Salazar, Gabriela; Mucio-Ramirez, Samuel; Liu, Yuhong; Sun, Yan L; Kay, Steven M; Besio, Walter G

2012-07-01

Epilepsy affects approximately 1% of the world population. Antiepileptic drugs are ineffective in approximately 30% of patients and have side effects. We are developing a noninvasive, or minimally invasive, transcranial focal electrical stimulation system through our novel tripolar concentric ring electrodes to control seizures. In this study, we demonstrate feasibility of an automatic seizure control system in rats with pentylenetetrazole-induced seizures through single and multiple stimulations. These stimulations are automatically triggered by a real-time electrographic seizure activity detector based on a disjunctive combination of detections from a cumulative sum algorithm and a generalized likelihood ratio test. An average seizure onset detection accuracy of 76.14% was obtained for the test set (n = 13). Detection of electrographic seizure activity was accomplished in advance of the early behavioral seizure activity in 76.92% of the cases. Automatically triggered stimulation significantly (p = 0.001) reduced the electrographic seizure activity power in the once stimulated group compared to controls in 70% of the cases. To the best of our knowledge this is the first closed-loop automatic seizure control system based on noninvasive electrical brain stimulation using tripolar concentric ring electrode electrographic seizure activity as feedback.

Inclusion of temporal priors for automated neonatal EEG classification

NASA Astrophysics Data System (ADS)

Temko, Andriy; Stevenson, Nathan; Marnane, William; Boylan, Geraldine; Lightbody, Gordon

2012-08-01

The aim of this paper is to use recent advances in the clinical understanding of the temporal evolution of seizure burden in neonates with hypoxic ischemic encephalopathy to improve the performance of automated detection algorithms. Probabilistic weights are designed from temporal locations of neonatal seizure events relative to time of birth. These weights are obtained by fitting a skew-normal distribution to the temporal seizure density and introduced into the probabilistic framework of the previously developed neonatal seizure detector. The results are validated on the largest available clinical dataset, comprising 816.7 h. By exploiting these priors, the receiver operating characteristic area is increased by 23% (relative) reaching 96.74%. The number of false detections per hour is decreased from 0.45 to 0.25, while maintaining the correct detection of seizure burden at 70%.

The effects of lossy compression on diagnostically relevant seizure information in EEG signals.

PubMed

Higgins, G; McGinley, B; Faul, S; McEvoy, R P; Glavin, M; Marnane, W P; Jones, E

2013-01-01

This paper examines the effects of compression on EEG signals, in the context of automated detection of epileptic seizures. Specifically, it examines the use of lossy compression on EEG signals in order to reduce the amount of data which has to be transmitted or stored, while having as little impact as possible on the information in the signal relevant to diagnosing epileptic seizures. Two popular compression methods, JPEG2000 and SPIHT, were used. A range of compression levels was selected for both algorithms in order to compress the signals with varying degrees of loss. This compression was applied to the database of epileptiform data provided by the University of Freiburg, Germany. The real-time EEG analysis for event detection automated seizure detection system was used in place of a trained clinician for scoring the reconstructed data. Results demonstrate that compression by a factor of up to 120:1 can be achieved, with minimal loss in seizure detection performance as measured by the area under the receiver operating characteristic curve of the seizure detection system.

Bursts of seizures in long-term recordings of human focal epilepsy

PubMed Central

Karoly, Philippa J.; Nurse, Ewan S.; Freestone, Dean R.; Ung, Hoameng; Cook, Mark J.; Boston, Ray

2017-01-01

Summary Objective We report on temporally clustered seizures detected from continuous long-term ambulatory human electroencephalographic data. The objective was to investigate short-term seizure clustering, which we have termed bursting, and consider implications for patient care, seizure prediction, and evaluating therapies. Methods Chronic ambulatory intracranial EEG data collected for the purpose of seizure prediction were annotated to identify seizure events. A detection algorithm was used to identify bursts of events. Burst events were compared to non-burst events to evaluate event dispersion, duration and dynamics. Results Bursts of seizures were present in six of fifteen patients, and detections were consistent over long term monitoring (> 2 years). Seizures within bursts are highly overdispersed compared to non-burst seizures. There was a complicated relationship between bursts and clinical seizures, although bursts were associated with multi-modal distributions of seizure duration, and poorer predictive outcomes. For three subjects, bursts demonstrated distinctive pre-ictal dynamics compared to clinical seizures. Significance We have previously hypothesized that there are distinct physiological pathways underlying short and long duration seizures. Here we show that burst seizures fall almost exclusively within the short population of seizure durations; however, a short duration was not sufficient to induce or imply bursting. We can therefore conclude that in addition to distinct mechanisms underlying seizure duration, there are separate factors regulating bursts of seizures. We show that bursts were a robust phenomenon in our patient cohort, which were consistent with overdispersed seizure rates, suggesting long-memory dynamics. PMID:28084639

A hierarchical approach for online temporal lobe seizure detection in long-term intracranial EEG recordings

NASA Astrophysics Data System (ADS)

Liang, Sheng-Fu; Chen, Yi-Chun; Wang, Yu-Lin; Chen, Pin-Tzu; Yang, Chia-Hsiang; Chiueh, Herming

2013-08-01

Objective. Around 1% of the world's population is affected by epilepsy, and nearly 25% of patients cannot be treated effectively by available therapies. The presence of closed-loop seizure-triggered stimulation provides a promising solution for these patients. Realization of fast, accurate, and energy-efficient seizure detection is the key to such implants. In this study, we propose a two-stage on-line seizure detection algorithm with low-energy consumption for temporal lobe epilepsy (TLE). Approach. Multi-channel signals are processed through independent component analysis and the most representative independent component (IC) is automatically selected to eliminate artifacts. Seizure-like intracranial electroencephalogram (iEEG) segments are fast detected in the first stage of the proposed method and these seizures are confirmed in the second stage. The conditional activation of the second-stage signal processing reduces the computational effort, and hence energy, since most of the non-seizure events are filtered out in the first stage. Main results. Long-term iEEG recordings of 11 patients who suffered from TLE were analyzed via leave-one-out cross validation. The proposed method has a detection accuracy of 95.24%, a false alarm rate of 0.09/h, and an average detection delay time of 9.2 s. For the six patients with mesial TLE, a detection accuracy of 100.0%, a false alarm rate of 0.06/h, and an average detection delay time of 4.8 s can be achieved. The hierarchical approach provides a 90% energy reduction, yielding effective and energy-efficient implementation for real-time epileptic seizure detection. Significance. An on-line seizure detection method that can be applied to monitor continuous iEEG signals of patients who suffered from TLE was developed. An IC selection strategy to automatically determine the most seizure-related IC for seizure detection was also proposed. The system has advantages of (1) high detection accuracy, (2) low false alarm, (3) short detection latency, and (4) energy-efficient design for hardware implementation.

Low-complexity image processing for real-time detection of neonatal clonic seizures.

PubMed

Ntonfo, Guy Mathurin Kouamou; Ferrari, Gianluigi; Raheli, Riccardo; Pisani, Francesco

2012-05-01

In this paper, we consider a novel low-complexity real-time image-processing-based approach to the detection of neonatal clonic seizures. Our approach is based on the extraction, from a video of a newborn, of an average luminance signal representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., the limbs), by evaluating the periodicity of the extracted average luminance signal it is possible to detect the presence of a clonic seizure. The periodicity is investigated, through a hybrid autocorrelation-Yin estimation technique, on a per-window basis, where a time window is defined as a sequence of consecutive video frames. While processing is first carried out on a single window basis, we extend our approach to interlaced windows. The performance of the proposed detection algorithm is investigated, in terms of sensitivity and specificity, through receiver operating characteristic curves, considering video recordings of newborns affected by neonatal seizures.

LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

PubMed

Zhang, Tao; Chen, Wanzhong

2017-08-01

Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

Particle swarm optimization algorithm based parameters estimation and control of epileptiform spikes in a neural mass model

NASA Astrophysics Data System (ADS)

Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan

2016-07-01

This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design.

Detection of subtle nocturnal motor activity from 3-D accelerometry recordings in epilepsy patients.

PubMed

Nijsen, Tamara M E; Cluitmans, Pierre J M; Arends, Johan B A M; Griep, Paul A M

2007-11-01

This paper presents a first step towards reliable detection of nocturnal epileptic seizures based on 3-D accelerometry (ACM) recordings. The main goal is to distinguish between data with and without subtle nocturnal motor activity, thus reducing the amount of data that needs further (more complex) analysis for seizure detection. From 15 ACM signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space, a linear threshold function is used for classification. For training and testing, the algorithm ACM data along with video data is used from nocturnal registrations in seven mentally retarded patients with severe epilepsy. Per patient, the algorithm detected 100% of the periods of motor activity that are marked in video recordings and the ACM signals by experts. From all the detections, 43%-89% was correct (mean =65%). We were able to reduce the amount of data that need to be analyzed considerably. The results show that our approach can be used for detection of subtle nocturnal motor activity. Furthermore, our results indicate that our algorithm is robust for fluctuations across patients. Consequently, there is no need for training the algorithm for each new patient.

Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform.

PubMed

Samiee, Kaveh; Kovács, Petér; Gabbouj, Moncef

2015-02-01

A system for epileptic seizure detection in electroencephalography (EEG) is described in this paper. One of the challenges is to distinguish rhythmic discharges from nonstationary patterns occurring during seizures. The proposed approach is based on an adaptive and localized time-frequency representation of EEG signals by means of rational functions. The corresponding rational discrete short-time Fourier transform (DSTFT) is a novel feature extraction technique for epileptic EEG data. A multilayer perceptron classifier is fed by the coefficients of the rational DSTFT in order to separate seizure epochs from seizure-free epochs. The effectiveness of the proposed method is compared with several state-of-art feature extraction algorithms used in offline epileptic seizure detection. The results of the comparative evaluations show that the proposed method outperforms competing techniques in terms of classification accuracy. In addition, it provides a compact representation of EEG time-series.

Safe and sound? A systematic literature review of seizure detection methods for personal use.

PubMed

Jory, Caryn; Shankar, Rohit; Coker, Deborah; McLean, Brendan; Hanna, Jane; Newman, Craig

2016-03-01

The study aims to review systematically the quality of evidence supporting seizure detection devices. The unpredictable nature of seizures is distressing and disabling for sufferers and carers. If a seizure can be reliably detected then the patient or carer could be alerted. It could help prevent injury and death. A literature search was completed. Forty three of 120 studies found using relevant search terms were suitable for systematic review which was done applying pre-agreed criteria using PRISMA guidelines. The papers identified and reviewed were those that could have potential for everyday use of patients in a domestic setting. Studies involving long term use of scalp electrodes to record EEG were excluded on the grounds of unacceptable restriction of daily activities. Most of the devices focused on changes in movement and/or physiological signs and were dependent on an algorithm to determine cut off points. No device was able to detect all seizures and there was an issue with both false positives and missed seizures. Many of the studies involved relatively small numbers of cases or report on only a few seizures. Reports of seizure alert dogs are also considered. Seizure detection devices are at a relatively early stage of development and as yet there are no large scale studies or studies that compare the effectiveness of one device against others. The issue of false positive detection rates is important as they are disruptive for both the patient and the carer. Nevertheless, the development of seizure detection devices offers great potential in the management of epilepsy. Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Unsupervised EEG analysis for automated epileptic seizure detection

NASA Astrophysics Data System (ADS)

Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

2016-07-01

Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

Wavelet-based Gaussian-mixture hidden Markov model for the detection of multistage seizure dynamics: A proof-of-concept study

PubMed Central

2011-01-01

Background Epilepsy is a common neurological disorder characterized by recurrent electrophysiological activities, known as seizures. Without the appropriate detection strategies, these seizure episodes can dramatically affect the quality of life for those afflicted. The rationale of this study is to develop an unsupervised algorithm for the detection of seizure states so that it may be implemented along with potential intervention strategies. Methods Hidden Markov model (HMM) was developed to interpret the state transitions of the in vitro rat hippocampal slice local field potentials (LFPs) during seizure episodes. It can be used to estimate the probability of state transitions and the corresponding characteristics of each state. Wavelet features were clustered and used to differentiate the electrophysiological characteristics at each corresponding HMM states. Using unsupervised training method, the HMM and the clustering parameters were obtained simultaneously. The HMM states were then assigned to the electrophysiological data using expert guided technique. Minimum redundancy maximum relevance (mRMR) analysis and Akaike Information Criterion (AICc) were applied to reduce the effect of over-fitting. The sensitivity, specificity and optimality index of chronic seizure detection were compared for various HMM topologies. The ability of distinguishing early and late tonic firing patterns prior to chronic seizures were also evaluated. Results Significant improvement in state detection performance was achieved when additional wavelet coefficient rates of change information were used as features. The final HMM topology obtained using mRMR and AICc was able to detect non-ictal (interictal), early and late tonic firing, chronic seizures and postictal activities. A mean sensitivity of 95.7%, mean specificity of 98.9% and optimality index of 0.995 in the detection of chronic seizures was achieved. The detection of early and late tonic firing was validated with experimental intracellular electrical recordings of seizures. Conclusions The HMM implementation of a seizure dynamics detector is an improvement over existing approaches using visual detection and complexity measures. The subjectivity involved in partitioning the observed data prior to training can be eliminated. It can also decipher the probabilities of seizure state transitions using the magnitude and rate of change wavelet information of the LFPs. PMID:21504608

Low-Power, 8-Channel EEG Recorder and Seizure Detector ASIC for a Subdermal Implantable System.

PubMed

Do Valle, Bruno G; Cash, Sydney S; Sodini, Charles G

2016-12-01

EEG remains the mainstay test for the diagnosis and treatment of patients with epilepsy. Unfortunately, ambulatory EEG systems are far from ideal for patients who have infrequent seizures. These systems only last up to 3 days and if a seizure is not captured during the recordings, a definite diagnosis of the patient's condition cannot be given. This work aims to address this need by proposing a subdermal implantable, eight-channel EEG recorder and seizure detector that has two modes of operation: diagnosis and seizure counting. In the diagnosis mode, EEG is continuously recorded until a number of seizures are recorded. In the seizure counting mode, the system uses a low-power algorithm to track the number of seizures a patient has, providing doctors with a reliable count to help determine medication efficacy or other clinical endpoint. An ASIC that implements the EEG recording and seizure detection algorithm was designed and fabricated in a 0.18 μm CMOS process. The ASIC includes eight EEG channels and is designed to minimize the system's power and size. The result is a power-efficient analog front end that requires 2.75 μW per channel in diagnosis mode and 0.84 μW per channel in seizure counting mode. Both modes have an input referred noise of approximately 1.1 μVrms.

Objective quantification of seizure frequency and treatment success via long-term outpatient video-EEG monitoring: a feasibility study.

PubMed

Stefan, H; Kreiselmeyer, G; Kasper, B; Graf, W; Pauli, E; Kurzbuch, K; Hopfengärtner, R

2011-03-01

A reliable method for the estimation of seizure frequency and severity is indispensable in assessing the efficacy of drug treatment in epilepsies. These quantities are usually deduced from subjective patient reports, which may cause considerable problems due to insufficient or false descriptions of seizures and their frequency. We present data from two difficult-to-treat patients with intractable epilepsy. Pat. 1 has had an unknown number of CP seizures. Here, a prolonged outpatient video-EEG monitoring over 160 h and 137 h (over an interval of three months) was performed with an automated seizure detection method. Pat. 2 suffered exclusively from nocturnal seizures originating from the frontal lobe. In this case, an objective quantification of the efficacy of drug treatment over a time period of 22 weeks was established. For the reliable quantification of seizures, a prolonged outpatient video/video-EEG monitoring was appended after a short-term inpatient monitoring period. Patient 1: The seizure detection algorithm was capable of detecting 10 out of 11 seizures. The number of false-positive events was <0.03/h. It was clearly demonstrated that the patient showed more seizures than originally reported. Patient 2: The add-on medication of lacosamide led to a significant reduction in seizure frequency and to a marked decrease in the mean duration of seizures. The severity of seizures was reduced from numerous hypermotoric seizures to few mild, head-turning seizures. Outpatient monitoring may be helpful to guide treatment for severe epilepsies and offers the possibility to more reliably quantify the efficacy of treatment in the long-term, even over several months. Copyright © 2010 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

PubMed

Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

2017-01-01

Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

Epileptic seizure onset detection based on EEG and ECG data fusion.

PubMed

Qaraqe, Marwa; Ismail, Muhammad; Serpedin, Erchin; Zulfi, Haneef

2016-05-01

This paper presents a novel method for seizure onset detection using fused information extracted from multichannel electroencephalogram (EEG) and single-channel electrocardiogram (ECG). In existing seizure detectors, the analysis of the nonlinear and nonstationary ECG signal is limited to the time-domain or frequency-domain. In this work, heart rate variability (HRV) extracted from ECG is analyzed using a Matching-Pursuit (MP) and Wigner-Ville Distribution (WVD) algorithm in order to effectively extract meaningful HRV features representative of seizure and nonseizure states. The EEG analysis relies on a common spatial pattern (CSP) based feature enhancement stage that enables better discrimination between seizure and nonseizure features. The EEG-based detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. Two fusion systems are adopted. In the first system, EEG-based and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an override option that allows for the EEG-based decision to override the fusion-based decision in the event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6s, and a specificity of 99.91% for the MAJ fusion case. Copyright © 2016 Elsevier Inc. All rights reserved.

Ambulatory REACT: real-time seizure detection with a DSP microprocessor.

PubMed

McEvoy, Robert P; Faul, Stephen; Marnane, William P

2010-01-01

REACT (Real-Time EEG Analysis for event deteCTion) is a Support Vector Machine based technology which, in recent years, has been successfully applied to the problem of automated seizure detection in both adults and neonates. This paper describes the implementation of REACT on a commercial DSP microprocessor; the Analog Devices Blackfin®. The primary aim of this work is to develop a prototype system for use in ambulatory or in-ward automated EEG analysis. Furthermore, the complexity of the various stages of the REACT algorithm on the Blackfin processor is analysed; in particular the EEG feature extraction stages. This hardware profile is used to select a reduced, platform-aware feature set, in order to evaluate the seizure classification accuracy of a lower-complexity, lower-power REACT system.

An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

PubMed

Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

2016-02-01

Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

NASA Astrophysics Data System (ADS)

Mesbah, Mostefa; Balakrishnan, Malarvili; Colditz, Paul B.; Boashash, Boualem

2012-12-01

This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation.

PubMed

Yuan, Shasha; Zhou, Weidong; Wu, Qi; Zhang, Yanli

2016-05-01

Epileptic seizure detection plays an important role in the diagnosis of epilepsy and reducing the massive workload of reviewing electroencephalography (EEG) recordings. In this work, a novel algorithm is developed to detect seizures employing log-Euclidean Gaussian kernel-based sparse representation (SR) in long-term EEG recordings. Unlike the traditional SR for vector data in Euclidean space, the log-Euclidean Gaussian kernel-based SR framework is proposed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Since the Riemannian manifold is nonlinear, the log-Euclidean Gaussian kernel function is applied to embed it into a reproducing kernel Hilbert space (RKHS) for performing SR. The EEG signals of all channels are divided into epochs and the SPD matrices representing EEG epochs are generated by covariance descriptors. Then, the testing samples are sparsely coded over the dictionary composed by training samples utilizing log-Euclidean Gaussian kernel-based SR. The classification of testing samples is achieved by computing the minimal reconstructed residuals. The proposed method is evaluated on the Freiburg EEG dataset of 21 patients and shows its notable performance on both epoch-based and event-based assessments. Moreover, this method handles multiple channels of EEG recordings synchronously which is more speedy and efficient than traditional seizure detection methods.

Rapidly Learned Identification of Epileptic Seizures from Sonified EEG

PubMed Central

Loui, Psyche; Koplin-Green, Matan; Frick, Mark; Massone, Michael

2014-01-01

Sonification refers to a process by which data are converted into sound, providing an auditory alternative to visual display. Currently, the prevalent method for diagnosing seizures in epilepsy is by visually reading a patient’s electroencephalogram (EEG). However, sonification of the EEG data provides certain advantages due to the nature of human auditory perception. We hypothesized that human listeners will be able to identify seizures from EEGs using the auditory modality alone, and that accuracy of seizure identification will increase after a short training session. Here, we describe an algorithm that we have used to sonify EEGs of both seizure and non-seizure activity, followed by a training study in which subjects listened to short clips of sonified EEGs and determined whether each clip was of seizure or normal activity, both before and after a short training session. Results show that before training subjects performed at chance level in differentiating seizures from non-seizures, but there was a significant improvement of accuracy after the training session. After training, subjects successfully distinguished seizures from non-seizures using the auditory modality alone. Further analyses using signal detection theory demonstrated improvement in sensitivity and reduction in response bias as a result of training. This study demonstrates the potential of sonified EEGs to be used for the detection of seizures. Future studies will attempt to increase accuracy using novel training and sonification modifications, with the goals of managing, predicting, and ultimately controlling seizures using sonification as a possible biofeedback-based intervention for epilepsy. PMID:25352802

Forecasting seizures in dogs with naturally occurring epilepsy.

PubMed

Howbert, J Jeffry; Patterson, Edward E; Stead, S Matt; Brinkmann, Ben; Vasoli, Vincent; Crepeau, Daniel; Vite, Charles H; Sturges, Beverly; Ruedebusch, Vanessa; Mavoori, Jaideep; Leyde, Kent; Sheffield, W Douglas; Litt, Brian; Worrell, Gregory A

2014-01-01

Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), low-gamma (30-70 Hz), and high-gamma (70-180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring.

Forecasting Seizures in Dogs with Naturally Occurring Epilepsy

PubMed Central

Stead, S. Matt; Brinkmann, Ben; Vasoli, Vincent; Crepeau, Daniel; Vite, Charles H.; Sturges, Beverly; Ruedebusch, Vanessa; Mavoori, Jaideep; Leyde, Kent; Sheffield, W. Douglas; Litt, Brian; Worrell, Gregory A.

2014-01-01

Seizure forecasting has the potential to create new therapeutic strategies for epilepsy, such as providing patient warnings and delivering preemptive therapy. Progress on seizure forecasting, however, has been hindered by lack of sufficient data to rigorously evaluate the hypothesis that seizures are preceded by physiological changes, and are not simply random events. We investigated seizure forecasting in three dogs with naturally occurring focal epilepsy implanted with a device recording continuous intracranial EEG (iEEG). The iEEG spectral power in six frequency bands: delta (0.1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), low-gamma (30–70 Hz), and high-gamma (70–180 Hz), were used as features. Logistic regression classifiers were trained to discriminate labeled pre-ictal and inter-ictal data segments using combinations of the band spectral power features. Performance was assessed on separate test data sets via 10-fold cross-validation. A total of 125 spontaneous seizures were detected in continuous iEEG recordings spanning 6.5 to 15 months from 3 dogs. When considering all seizures, the seizure forecasting algorithm performed significantly better than a Poisson-model chance predictor constrained to have the same time in warning for all 3 dogs over a range of total warning times. Seizure clusters were observed in all 3 dogs, and when the effect of seizure clusters was decreased by considering the subset of seizures separated by at least 4 hours, the forecasting performance remained better than chance for a subset of algorithm parameters. These results demonstrate that seizures in canine epilepsy are not randomly occurring events, and highlight the feasibility of long-term seizure forecasting using iEEG monitoring. PMID:24416133

Interictal to Ictal Phase Transition in a Small-World Network

NASA Astrophysics Data System (ADS)

Nemzer, Louis; Cravens, Gary; Worth, Robert

Real-time detection and prediction of seizures in patients with epilepsy is essential for rapid intervention. Here, we perform a full Hodgkin-Huxley calculation using n 50 in silico neurons configured in a small-world network topology to generate simulated EEG signals. The connectivity matrix, constructed using a Watts-Strogatz algorithm, admits randomized or deterministic entries. We find that situations corresponding to interictal (non-seizure) and ictal (seizure) states are separated by a phase transition that can be influenced by congenital channelopathies, anticonvulsant drugs, and connectome plasticity. The interictal phase exhibits scale-free phenomena, as characterized by a power law form of the spectral power density, while the ictal state suffers from pathological synchronization. We compare the results with intracranial EEG data and show how these findings may be used to detect or even predict seizure onset. Along with the balance of excitatory and inhibitory factors, the network topology plays a large role in determining the overall characteristics of brain activity. We have developed a new platform for testing the conditions that contribute to the phase transition between non-seizure and seizure states.

[Portable Epileptic Seizure Monitoring Intelligent System Based on Android System].

PubMed

Liang, Zhenhu; Wu, Shufeng; Yang, Chunlin; Jiang, Zhenzhou; Yu, Tao; Lu, Chengbiao; Li, Xiaoli

2016-02-01

The clinical electroencephalogram (EEG) monitoring systems based on personal computer system can not meet the requirements of portability and home usage. The epilepsy patients have to be monitored in hospital for an extended period of time, which imposes a heavy burden on hospitals. In the present study, we designed a portable 16-lead networked monitoring system based on the Android smart phone. The system uses some technologies including the active electrode, the WiFi wireless transmission, the multi-scale permutation entropy (MPE) algorithm, the back-propagation (BP) neural network algorithm, etc. Moreover, the software of Android mobile application can realize the processing and analysis of EEG data, the display of EEG waveform and the alarm of epileptic seizure. The system has been tested on the mobile phones with Android 2. 3 operating system or higher version and the results showed that this software ran accurately and steadily in the detection of epileptic seizure. In conclusion, this paper provides a portable and reliable solution for epileptic seizure monitoring in clinical and home applications.

Towards acute pediatric status epilepticus intervention teams: Do we need "Seizure Codes"?

PubMed

Stredny, Coral M; Abend, Nicholas S; Loddenkemper, Tobias

2018-05-01

To identify areas of treatment delay and barriers to care in pediatric status epilepticus, review ongoing quality improvement initiatives, and provide suggestions for further innovations to improve and standardize these patient care processes. Narrative review of current status epilepticus management algorithms, anti-seizure medication administration and outcomes associated with delays, and initiatives to improve time to treatment. Articles reviewing or reporting quality improvement initiatives were identified through a PubMed search with keywords "status epilepticus," "quality improvement," "guideline adherence," and/or "protocol;" references of included articles were also reviewed. Rapid initiation and escalation of status epilepticus treatment has been associated with shortened seizure duration and more favorable outcomes. Current evidence-based guidelines for management of status epilepticus propose medication algorithms with suggested times for each management step. However, time to antiseizure medication administration for pediatric status epilepticus remains delayed in both the pre- and in-hospital settings. Barriers to timely treatment include suboptimal preventive care, inaccurate seizure detection, infrequent or restricted use of home rescue medications by caregivers and pre-hospital emergency personnel, delayed summoning and arrival of emergency personnel, and use of inappropriately dosed medications. Ongoing quality improvement initiatives in the pre- and in-hospital settings targeting these barriers are reviewed. Improved preventive care, seizure detection, and rescue medication education may advance pre-hospital management, and we propose the use of acute status epilepticus intervention teams to initiate and incorporate in-hospital interventions as time-sensitive "Seizure Code" emergencies. Copyright © 2018 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Seizures in the elderly: development and validation of a diagnostic algorithm.

PubMed

Dupont, Sophie; Verny, Marc; Harston, Sandrine; Cartz-Piver, Leslie; Schück, Stéphane; Martin, Jennifer; Puisieux, François; Alecu, Cosmin; Vespignani, Hervé; Marchal, Cécile; Derambure, Philippe

2010-05-01

Seizures are frequent in the elderly, but their diagnosis can be challenging. The objective of this work was to develop and validate an expert-based algorithm for the diagnosis of seizures in elderly people. A multidisciplinary group of neurologists and geriatricians developed a diagnostic algorithm using a combination of selected clinical, electroencephalographical and radiological criteria. The algorithm was validated by multicentre retrospective analysis of data of patients referred for specific symptoms and classified by the experts as epileptic patients or not. The algorithm was applied to all the patients, and the diagnosis provided by the algorithm was compared to the clinical diagnosis of the experts. Twenty-nine clinical, electroencephalographical and radiological criteria were selected for the algorithm. According to criteria combination, seizures were classified in four levels of diagnosis: certain, highly probable, possible or improbable. To validate the algorithm, the medical records of 269 elderly patients were analyzed (138 with epileptic seizures, 131 with non-epileptic manifestations). Patients were mainly referred for a transient focal deficit (40%), confusion (38%), unconsciousness (27%). The algorithm best classified certain and probable seizures versus possible and improbable seizures, with 86.2% sensitivity and 67.2% specificity. Using logistical regression, 2 simplified models were developed, the first with 13 criteria (Se 85.5%, Sp 90.1%), and the second with 7 criteria only (Se 84.8%, Sp 88.6%). In conclusion, the present study validated the use of a revised diagnostic algorithm to help diagnosis epileptic seizures in the elderly. A prospective study is planned to further validate this algorithm. Copyright 2010 Elsevier B.V. All rights reserved.

Spatiotemporal Mapping of Interictal Spike Propagation: A Novel Methodology Applied to Pediatric Intracranial EEG Recordings

PubMed Central

Tomlinson, Samuel B.; Bermudez, Camilo; Conley, Chiara; Brown, Merritt W.; Porter, Brenda E.; Marsh, Eric D.

2016-01-01

Synchronized cortical activity is implicated in both normative cognitive functioning and many neurologic disorders. For epilepsy patients with intractable seizures, irregular synchronization within the epileptogenic zone (EZ) is believed to provide the network substrate through which seizures initiate and propagate. Mapping the EZ prior to epilepsy surgery is critical for detecting seizure networks in order to achieve postsurgical seizure control. However, automated techniques for characterizing epileptic networks have yet to gain traction in the clinical setting. Recent advances in signal processing and spike detection have made it possible to examine the spatiotemporal propagation of interictal spike discharges across the epileptic cortex. In this study, we present a novel methodology for detecting, extracting, and visualizing spike propagation and demonstrate its potential utility as a biomarker for the EZ. Eighteen presurgical intracranial EEG recordings were obtained from pediatric patients ultimately experiencing favorable (i.e., seizure-free, n = 9) or unfavorable (i.e., seizure-persistent, n = 9) surgical outcomes. Novel algorithms were applied to extract multichannel spike discharges and visualize their spatiotemporal propagation. Quantitative analysis of spike propagation was performed using trajectory clustering and spatial autocorrelation techniques. Comparison of interictal propagation patterns revealed an increase in trajectory organization (i.e., spatial autocorrelation) among Sz-Free patients compared with Sz-Persist patients. The pathophysiological basis and clinical implications of these findings are considered. PMID:28066315

Study on localization of epileptic focus based on causality analysis

NASA Astrophysics Data System (ADS)

Shan, Shaojie; Li, Hanjun; Tang, Xiaoying

2018-05-01

In this paper, we considered that the ECoG signal contain abundant pathological information, which can be used for the localization of epileptic focus before epileptic seizures in 1-2 mins. In order to validate this hypothesis, cutting the ECoG into three stages: before seizure, seizure and after seizure, then through using Granger causality algorithm, PSI causality algorithm, Transfer Entropy causality algorithm at different stages of epilepsy ECoG, we were able to do the causality analysis of ECoG data. The results have shown that there is significant difference with the causality value of the epileptic focus area in before seizure, seizure and after seizure. An increase is in the causality value of each channel during epileptic seizure. After epileptic seizure, the causality between the channels showed a downward trend, but the difference was not obvious. The difference of the causality provides a reliable technical method to assist the clinical diagnosis of epileptic focus.

Automatic detection of rhythmic and periodic patterns in critical care EEG based on American Clinical Neurophysiology Society (ACNS) standardized terminology.

PubMed

Fürbass, F; Hartmann, M M; Halford, J J; Koren, J; Herta, J; Gruber, A; Baumgartner, C; Kluge, T

2015-09-01

Continuous EEG from critical care patients needs to be evaluated time efficiently to maximize the treatment effect. A computational method will be presented that detects rhythmic and periodic patterns according to the critical care EEG terminology (CCET) of the American Clinical Neurophysiology Society (ACNS). The aim is to show that these detected patterns support EEG experts in writing neurophysiological reports. First of all, three case reports exemplify the evaluation procedure using graphically presented detections. Second, 187 hours of EEG from 10 critical care patients were used in a comparative trial study. For each patient the result of a review session using the EEG and the visualized pattern detections was compared to the original neurophysiology report. In three out of five patients with reported seizures, all seizures were reported correctly. In two patients, several subtle clinical seizures with unclear EEG correlation were missed. Lateralized periodic patterns (LPD) were correctly found in 2/2 patients and EEG slowing was correctly found in 7/9 patients. In 8/10 patients, additional EEG features were found including LPDs, EEG slowing, and seizures. The use of automatic pattern detection will assist in review of EEG and increase efficiency. The implementation of bedside surveillance devices using our detection algorithm appears to be feasible and remains to be confirmed in further multicenter studies. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features.

PubMed

Li, Yang; Cui, Weigang; Luo, Meilin; Li, Ke; Wang, Lina

2018-01-25

The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.

Seizure Control in a Computational Model Using a Reinforcement Learning Stimulation Paradigm.

PubMed

Nagaraj, Vivek; Lamperski, Andrew; Netoff, Theoden I

2017-11-01

Neuromodulation technologies such as vagus nerve stimulation and deep brain stimulation, have shown some efficacy in controlling seizures in medically intractable patients. However, inherent patient-to-patient variability of seizure disorders leads to a wide range of therapeutic efficacy. A patient specific approach to determining stimulation parameters may lead to increased therapeutic efficacy while minimizing stimulation energy and side effects. This paper presents a reinforcement learning algorithm that optimizes stimulation frequency for controlling seizures with minimum stimulation energy. We apply our method to a computational model called the epileptor. The epileptor model simulates inter-ictal and ictal local field potential data. In order to apply reinforcement learning to the Epileptor, we introduce a specialized reward function and state-space discretization. With the reward function and discretization fixed, we test the effectiveness of the temporal difference reinforcement learning algorithm (TD(0)). For periodic pulsatile stimulation, we derive a relation that describes, for any stimulation frequency, the minimal pulse amplitude required to suppress seizures. The TD(0) algorithm is able to identify parameters that control seizures quickly. Additionally, our results show that the TD(0) algorithm refines the stimulation frequency to minimize stimulation energy thereby converging to optimal parameters reliably. An advantage of the TD(0) algorithm is that it is adaptive so that the parameters necessary to control the seizures can change over time. We show that the algorithm can converge on the optimal solution in simulation with slow and fast inter-seizure intervals.

A low computation cost method for seizure prediction.

PubMed

Zhang, Yanli; Zhou, Weidong; Yuan, Qi; Wu, Qi

2014-10-01

The dynamic changes of electroencephalograph (EEG) signals in the period prior to epileptic seizures play a major role in the seizure prediction. This paper proposes a low computation seizure prediction algorithm that combines a fractal dimension with a machine learning algorithm. The presented seizure prediction algorithm extracts the Higuchi fractal dimension (HFD) of EEG signals as features to classify the patient's preictal or interictal state with Bayesian linear discriminant analysis (BLDA) as a classifier. The outputs of BLDA are smoothed by a Kalman filter for reducing possible sporadic and isolated false alarms and then the final prediction results are produced using a thresholding procedure. The algorithm was evaluated on the intracranial EEG recordings of 21 patients in the Freiburg EEG database. For seizure occurrence period of 30 min and 50 min, our algorithm obtained an average sensitivity of 86.95% and 89.33%, an average false prediction rate of 0.20/h, and an average prediction time of 24.47 min and 39.39 min, respectively. The results confirm that the changes of HFD can serve as a precursor of ictal activities and be used for distinguishing between interictal and preictal epochs. Both HFD and BLDA classifier have a low computational complexity. All of these make the proposed algorithm suitable for real-time seizure prediction. Copyright © 2014 Elsevier B.V. All rights reserved.

Detection of seizures from small samples using nonlinear dynamic system theory.

PubMed

Yaylali, I; Koçak, H; Jayakar, P

1996-07-01

The electroencephalogram (EEG), like many other biological phenomena, is quite likely governed by nonlinear dynamics. Certain characteristics of the underlying dynamics have recently been quantified by computing the correlation dimensions (D2) of EEG time series data. In this paper, D2 of the unbiased autocovariance function of the scalp EEG data was used to detect electrographic seizure activity. Digital EEG data were acquired at a sampling rate of 200 Hz per channel and organized in continuous frames (duration 2.56 s, 512 data points). To increase the reliability of D2 computations with short duration data, raw EEG data were initially simplified using unbiased autocovariance analysis to highlight the periodic activity that is present during seizures. The D2 computation was then performed from the unbiased autocovariance function of each channel using the Grassberger-Procaccia method with Theiler's box-assisted correlation algorithm. Even with short duration data, this preprocessing proved to be computationally robust and displayed no significant sensitivity to implementation details such as the choices of embedding dimension and box size. The system successfully identified various types of seizures in clinical studies.

A Functional-Genetic Scheme for Seizure Forecasting in Canine Epilepsy.

PubMed

Bou Assi, Elie; Nguyen, Dang K; Rihana, Sandy; Sawan, Mohamad

2018-06-01

The objective of this work is the development of an accurate seizure forecasting algorithm that considers brain's functional connectivity for electrode selection. We start by proposing Kmeans-directed transfer function, an adaptive functional connectivity method intended for seizure onset zone localization in bilateral intracranial EEG recordings. Electrodes identified as seizure activity sources and sinks are then used to implement a seizure-forecasting algorithm on long-term continuous recordings in dogs with naturally-occurring epilepsy. A precision-recall genetic algorithm is proposed for feature selection in line with a probabilistic support vector machine classifier. Epileptic activity generators were focal in all dogs confirming the diagnosis of focal epilepsy in these animals while sinks spanned both hemispheres in 2 of 3 dogs. Seizure forecasting results show performance improvement compared to previous studies, achieving average sensitivity of 84.82% and time in warning of 0.1. Achieved performances highlight the feasibility of seizure forecasting in canine epilepsy. The ability to improve seizure forecasting provides promise for the development of EEG-triggered closed-loop seizure intervention systems for ambulatory implantation in patients with refractory epilepsy.

Sensitivity of quantitative EEG for seizure identification in the intensive care unit.

PubMed

Haider, Hiba A; Esteller, Rosana; Hahn, Cecil D; Westover, M Brandon; Halford, Jonathan J; Lee, Jong W; Shafi, Mouhsin M; Gaspard, Nicolas; Herman, Susan T; Gerard, Elizabeth E; Hirsch, Lawrence J; Ehrenberg, Joshua A; LaRoche, Suzette M

2016-08-30

To evaluate the sensitivity of quantitative EEG (QEEG) for electrographic seizure identification in the intensive care unit (ICU). Six-hour EEG epochs chosen from 15 patients underwent transformation into QEEG displays. Each epoch was reviewed in 3 formats: raw EEG, QEEG + raw, and QEEG-only. Epochs were also analyzed by a proprietary seizure detection algorithm. Nine neurophysiologists reviewed raw EEGs to identify seizures to serve as the gold standard. Nine other neurophysiologists with experience in QEEG evaluated the epochs in QEEG formats, with and without concomitant raw EEG. Sensitivity and false-positive rates (FPRs) for seizure identification were calculated and median review time assessed. Mean sensitivity for seizure identification ranged from 51% to 67% for QEEG-only and 63%-68% for QEEG + raw. FPRs averaged 1/h for QEEG-only and 0.5/h for QEEG + raw. Mean sensitivity of seizure probability software was 26.2%-26.7%, with FPR of 0.07/h. Epochs with the highest sensitivities contained frequent, intermittent seizures. Lower sensitivities were seen with slow-frequency, low-amplitude seizures and epochs with rhythmic or periodic patterns. Median review times were shorter for QEEG (6 minutes) and QEEG + raw analysis (14.5 minutes) vs raw EEG (19 minutes; p = 0.00003). A panel of QEEG trends can be used by experts to shorten EEG review time for seizure identification with reasonable sensitivity and low FPRs. The prevalence of false detections confirms that raw EEG review must be used in conjunction with QEEG. Studies are needed to identify optimal QEEG trend configurations and the utility of QEEG as a screening tool for non-EEG personnel. This study provides Class II evidence that QEEG + raw interpreted by experts identifies seizures in patients in the ICU with a sensitivity of 63%-68% and FPR of 0.5 seizures per hour. © 2016 American Academy of Neurology.

Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain.

PubMed

Tauste Campo, Adrià; Principe, Alessandro; Ley, Miguel; Rocamora, Rodrigo; Deco, Gustavo

2018-04-01

Epileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide crossvalidated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial electroencephalography (iEEG) recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable ("degenerate"), and this phase is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to be particularly associated with the activity of the resected regions in patients with validated postsurgical outcome. Our approach characterizes preseizure network dynamics as a cascade of 2 sequential events providing new insights into seizure prediction and control.

Classification of ictal and seizure-free HRV signals with focus on lateralization of epilepsy.

PubMed

Behbahani, Soroor; Dabanloo, Nader Jafarnia; Nasrabadi, Ali Motie; Dourado, Antonio

2016-01-01

Epileptic onsets often affect the autonomic function of the body during a seizure, whether it is in ictal, interictal or post-ictal periods. The different effects of localization and lateralization of seizures on heart rate variability (HRV) emphasize the importance of autonomic function changes in epileptic patients. On the other hand, the detection of seizures is of primary interests in evaluating the epileptic patients. In the current paper, we analyzed the HRV signal to develop a reliable offline seizure-detection algorithm to focus on the effects of lateralization on HRV. We assessed the HRV during 5-min segments of continuous electrocardiogram (ECG) recording with a total number of 170 seizures occurred in 16 patients, composed of 86 left-sided and 84 right-sided focus seizures. Relatively high and low-frequency components of the HRV were computed using spectral analysis. Poincaré parameters of each heart rate time series considered as non-linear features. We fed these features to the Support Vector Machines (SVMs) to find a robust classification method to classify epileptic and non-epileptic signals. Leave One Out Cross-Validation (LOOCV) approach was used to demonstrate the consistency of the classification results. Our obtained classification accuracy confirms that the proposed scheme has a potential in classifying HRV signals to epileptic and non-epileptic classes. The accuracy rates for right-sided and left-sided focus seizures were obtained as 86.74% and 79.41%, respectively. The main finding of our study is that the patients with right-sided focus epilepsy showed more reduction in parasympathetic activity and more increase in sympathetic activity. It can be a marker of impaired vagal activity associated with increased cardiovascular risk and arrhythmias. Our results suggest that lateralization of the seizure onset zone could exert different influences on heart rate changes. A right-sided seizure would cause an ictal tachycardia whereas a left-sided seizure would result in an ictal bradycardia.

Predicting Epileptic Seizures in Advance

PubMed Central

Moghim, Negin; Corne, David W.

2014-01-01

Epilepsy is the second most common neurological disorder, affecting 0.6–0.8% of the world's population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs) are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredictable nature of seizures poses risks for the individual with epilepsy. It is clearly desirable to find more effective ways of preventing seizures for such patients. The automatic detection of oncoming seizures, before their actual onset, can facilitate timely intervention and hence minimize these risks. In addition, advance prediction of seizures can enrich our understanding of the epileptic brain. In this study, drawing on the body of work behind automatic seizure detection and prediction from digitised Invasive Electroencephalography (EEG) data, a prediction algorithm, ASPPR (Advance Seizure Prediction via Pre-ictal Relabeling), is described. ASPPR facilitates the learning of predictive models targeted at recognizing patterns in EEG activity that are in a specific time window in advance of a seizure. It then exploits advanced machine learning coupled with the design and selection of appropriate features from EEG signals. Results, from evaluating ASPPR independently on 21 different patients, suggest that seizures for many patients can be predicted up to 20 minutes in advance of their onset. Compared to benchmark performance represented by a mean S1-Score (harmonic mean of Sensitivity and Specificity) of 90.6% for predicting seizure onset between 0 and 5 minutes in advance, ASPPR achieves mean S1-Scores of: 96.30% for prediction between 1 and 6 minutes in advance, 96.13% for prediction between 8 and 13 minutes in advance, 94.5% for prediction between 14 and 19 minutes in advance, and 94.2% for prediction between 20 and 25 minutes in advance. PMID:24911316

New algorithms for processing time-series big EEG data within mobile health monitoring systems.

PubMed

Serhani, Mohamed Adel; Menshawy, Mohamed El; Benharref, Abdelghani; Harous, Saad; Navaz, Alramzana Nujum

2017-10-01

Recent advances in miniature biomedical sensors, mobile smartphones, wireless communications, and distributed computing technologies provide promising techniques for developing mobile health systems. Such systems are capable of monitoring epileptic seizures reliably, which are classified as chronic diseases. Three challenging issues raised in this context with regard to the transformation, compression, storage, and visualization of big data, which results from a continuous recording of epileptic seizures using mobile devices. In this paper, we address the above challenges by developing three new algorithms to process and analyze big electroencephalography data in a rigorous and efficient manner. The first algorithm is responsible for transforming the standard European Data Format (EDF) into the standard JavaScript Object Notation (JSON) and compressing the transformed JSON data to decrease the size and time through the transfer process and to increase the network transfer rate. The second algorithm focuses on collecting and storing the compressed files generated by the transformation and compression algorithm. The collection process is performed with respect to the on-the-fly technique after decompressing files. The third algorithm provides relevant real-time interaction with signal data by prospective users. It particularly features the following capabilities: visualization of single or multiple signal channels on a smartphone device and query data segments. We tested and evaluated the effectiveness of our approach through a software architecture model implementing a mobile health system to monitor epileptic seizures. The experimental findings from 45 experiments are promising and efficiently satisfy the approach's objectives in a price of linearity. Moreover, the size of compressed JSON files and transfer times are reduced by 10% and 20%, respectively, while the average total time is remarkably reduced by 67% through all performed experiments. Our approach successfully develops efficient algorithms in terms of processing time, memory usage, and energy consumption while maintaining a high scalability of the proposed solution. Our approach efficiently supports data partitioning and parallelism relying on the MapReduce platform, which can help in monitoring and automatic detection of epileptic seizures. Copyright © 2017 Elsevier B.V. All rights reserved.

The circadian profile of epilepsy improves seizure forecasting.

PubMed

Karoly, Philippa J; Ung, Hoameng; Grayden, David B; Kuhlmann, Levin; Leyde, Kent; Cook, Mark J; Freestone, Dean R

2017-08-01

It is now established that epilepsy is characterized by periodic dynamics that increase seizure likelihood at certain times of day, and which are highly patient-specific. However, these dynamics are not typically incorporated into seizure prediction algorithms due to the difficulty of estimating patient-specific rhythms from relatively short-term or unreliable data sources. This work outlines a novel framework to develop and assess seizure forecasts, and demonstrates that the predictive power of forecasting models is improved by circadian information. The analyses used long-term, continuous electrocorticography from nine subjects, recorded for an average of 320 days each. We used a large amount of out-of-sample data (a total of 900 days for algorithm training, and 2879 days for testing), enabling the most extensive post hoc investigation into seizure forecasting. We compared the results of an electrocorticography-based logistic regression model, a circadian probability, and a combined electrocorticography and circadian model. For all subjects, clinically relevant seizure prediction results were significant, and the addition of circadian information (combined model) maximized performance across a range of outcome measures. These results represent a proof-of-concept for implementing a circadian forecasting framework, and provide insight into new approaches for improving seizure prediction algorithms. The circadian framework adds very little computational complexity to existing prediction algorithms, and can be implemented using current-generation implant devices, or even non-invasively via surface electrodes using a wearable application. The ability to improve seizure prediction algorithms through straightforward, patient-specific modifications provides promise for increased quality of life and improved safety for patients with epilepsy. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Automated seizure detection systems and their effectiveness for each type of seizure.

PubMed

Ulate-Campos, A; Coughlin, F; Gaínza-Lein, M; Fernández, I Sánchez; Pearl, P L; Loddenkemper, T

2016-08-01

Epilepsy affects almost 1% of the population and most of the approximately 20-30% of patients with refractory epilepsy have one or more seizures per month. Seizure detection devices allow an objective assessment of seizure frequency and a treatment tailored to the individual patient. A rapid recognition and treatment of seizures through closed-loop systems could potentially decrease morbidity and mortality in epilepsy. However, no single detection device can detect all seizure types. Therefore, the choice of a seizure detection device should consider the patient-specific seizure semiologies. This review of the literature evaluates seizure detection devices and their effectiveness for different seizure types. Our aim is to summarize current evidence, offer suggestions on how to select the most suitable seizure detection device for each patient and provide guidance to physicians, families and researchers when choosing or designing seizure detection devices. Further, this review will guide future prospective validation studies. Copyright © 2016. Published by Elsevier Ltd.

Postoperative seizure outcome-guided machine learning for interictal electrocorticography in neocortical epilepsy.

PubMed

Park, Seong-Cheol; Chung, Chun Kee

2018-06-01

The objective of this study was to introduce a new machine learning guided by outcome of resective epilepsy surgery defined as the presence/absence of seizures to improve data mining for interictal pathological activities in neocortical epilepsy. Electrocorticographies for 39 patients with medically intractable neocortical epilepsy were analyzed. We separately analyzed 38 frequencies from 0.9 to 800 Hz including both high-frequency activities and low-frequency activities to select bands related to seizure outcome. An automatic detector using amplitude-duration-number thresholds was used. Interictal electrocorticography data sets of 8 min for each patient were selected. In the first training data set of 20 patients, the automatic detector was optimized to best differentiate the seizure-free group from not-seizure-free-group based on ranks of resection percentages of activities detected using a genetic algorithm. The optimization was validated in a different data set of 19 patients. There were 16 (41%) seizure-free patients. The mean follow-up duration was 21 ± 11 mo (range, 13-44 mo). After validation, frequencies significantly related to seizure outcome were 5.8, 8.4-25, 30, 36, 52, and 75 among low-frequency activities and 108 and 800 Hz among high-frequency activities. Resection for 5.8, 8.4-25, 108, and 800 Hz activities consistently improved seizure outcome. Resection effects of 17-36, 52, and 75 Hz activities on seizure outcome were variable according to thresholds. We developed and validated an automated detector for monitoring interictal pathological and inhibitory/physiological activities in neocortical epilepsy using a data-driven approach through outcome-guided machine learning. NEW & NOTEWORTHY Outcome-guided machine learning based on seizure outcome was used to improve detections for interictal electrocorticographic low- and high-frequency activities. This method resulted in better separation of seizure outcome groups than others reported in the literature. The automatic detector can be trained without human intervention and no prior information. It is based only on objective seizure outcome data without relying on an expert's manual annotations. Using the method, we could find and characterize pathological and inhibitory activities.

Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks

NASA Astrophysics Data System (ADS)

Gollas, Frank; Tetzlaff, Ronald

2009-05-01

Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio-temporal autoregressive filter models are considered, for a prediction of EEG signal values. Thus Signal features values for successive, short, quasi stationary segments of brain electrical activity can be obtained, with the objective of detecting distinct changes prior to impending epileptic seizures. Furthermore long term recordings gained during presurgical diagnostics in temporal lobe epilepsy are analyzed and the predictive performance of the extracted features is evaluated statistically. Therefore a Receiver Operating Characteristic analysis is considered, assessing the distinguishability between distributions of supposed preictal and interictal periods.

Spatio-temporal coupling of EEG signals in epilepsy

NASA Astrophysics Data System (ADS)

Senger, Vanessa; Müller, Jens; Tetzlaff, Ronald

2011-05-01

Approximately 1% of the world's population suffer from epileptic seizures throughout their lives that mostly come without sign or warning. Thus, epilepsy is the most common chronical disorder of the neurological system. In the past decades, the problem of detecting a pre-seizure state in epilepsy using EEG signals has been addressed in many contributions by various authors over the past two decades. Up to now, the goal of identifying an impending epileptic seizure with sufficient specificity and reliability has not yet been achieved. Cellular Nonlinear Networks (CNN) are characterized by local couplings of dynamical systems of comparably low complexity. Thus, they are well suited for an implementation as highly parallel analogue processors. Programmable sensor-processor realizations of CNN combine high computational power comparable to tera ops of digital processors with low power consumption. An algorithm allowing an automated and reliable detection of epileptic seizure precursors would be a"huge step" towards the vision of an implantable seizure warning device that could provide information to patients and for a time/event specific treatment directly in the brain. Recent contributions have shown that modeling of brain electrical activity by solutions of Reaction-Diffusion-CNN as well as the application of a CNN predictor taking into account values of neighboring electrodes may contribute to the realization of a seizure warning device. In this paper, a CNN based predictor corresponding to a spatio-temporal filter is applied to multi channel EEG data in order to identify mutual couplings for different channels which lead to a enhanced prediction quality. Long term EEG recordings of different patients are considered. Results calculated for these recordings with inter-ictal phases as well as phases with seizures will be discussed in detail.

Comparative sensitivity of quantitative EEG (QEEG) spectrograms for detecting seizure subtypes.

PubMed

Goenka, Ajay; Boro, Alexis; Yozawitz, Elissa

2018-02-01

To assess the sensitivity of Persyst version 12 QEEG spectrograms to detect focal, focal with secondarily generalized, and generalized onset seizures. A cohort of 562 seizures from 58 patients was analyzed. Successive recordings with 2 or more seizures during continuous EEG monitoring for clinical indications in the ICU or EMU between July 2016 and January 2017 were included. Patient ages ranged from 5 to 64 years (mean = 36 years). There were 125 focal seizures, 187 secondarily generalized and 250 generalized seizures from 58 patients analyzed. Seizures were identified and classified independently by two epileptologists. A correlate to the seizure pattern in the raw EEG was sought in the QEEG spectrograms in 4-6 h EEG epochs surrounding the identified seizures. A given spectrogram was interpreted as indicating a seizure, if at the time of a seizure it showed a visually significant departure from the pre-event baseline. Sensitivities for seizure detection using each spectrogram were determined for each seizure subtype. Overall sensitivities of the QEEG spectrograms for detecting seizures ranged from 43% to 72%, with highest sensitivity (402/562,72%) by the seizure detection trend. The asymmetry spectrogram had the highest sensitivity for detecting focal seizures (117/125,94%). The FFT spectrogram was most sensitive for detecting secondarily generalized seizures (158/187, 84%). The seizure detection trend was the most sensitive for generalized onset seizures (197/250,79%). Our study suggests that different seizure types have specific patterns in the Persyst QEEG spectrograms. Identifying these patterns in the EEG can significantly increase the sensitivity for seizure identification. Copyright © 2018 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Deep Recurrent Neural Networks for seizure detection and early seizure detection systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Talathi, S. S.

Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizuremore » detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.« less

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

PubMed

Feng, Lichen; Li, Zunchao; Wang, Yuanfa

2018-02-01

Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module. The FE module performs the three-level Daubechies discrete wavelet transform to fit the physiological bands of the electroencephalogram (EEG) signal and extracts the time-frequency domain features reflecting the nonstationary signal properties. The SVM module integrates the modified sequential minimal optimization algorithm with the table-driven-based Gaussian kernel to enable efficient on-chip learning. The presented design is verified on an Altera Cyclone II field-programmable gate array and tested using the two publicly available EEG datasets. Experiment results show that the designed VLSI system improves the detection accuracy and training efficiency.

Exploring the time-frequency content of high frequency oscillations for automated identification of seizure onset zone in epilepsy.

PubMed

Liu, Su; Sha, Zhiyi; Sencer, Altay; Aydoseli, Aydin; Bebek, Nerse; Abosch, Aviva; Henry, Thomas; Gurses, Candan; Ince, Nuri Firat

2016-04-01

High frequency oscillations (HFOs) in intracranial electroencephalography (iEEG) recordings are considered as promising clinical biomarkers of epileptogenic regions in the brain. The aim of this study is to improve and automatize the detection of HFOs by exploring the time-frequency content of iEEG and to investigate the seizure onset zone (SOZ) detection accuracy during the sleep, awake and pre-ictal states in patients with epilepsy, for the purpose of assisting the localization of SOZ in clinical practice. Ten-minute iEEG segments were defined during different states in eight patients with refractory epilepsy. A three-stage algorithm was implemented to detect HFOs in these segments. First, an amplitude based initial detection threshold was used to generate a large pool of HFO candidates. Then distinguishing features were extracted from the time and time-frequency domain of the raw iEEG and used with a Gaussian mixture model clustering to isolate HFO events from other activities. The spatial distribution of HFO clusters was correlated with the seizure onset channels identified by neurologists in seven patient with good surgical outcome. The overlapping rates of localized channels and seizure onset locations were high in all states. The best result was obtained using the iEEG data during sleep, achieving a sensitivity of 81%, and a specificity of 96%. The channels with maximum number of HFOs identified epileptogenic areas where the seizures occurred more frequently. The current study was conducted using iEEG data collected in realistic clinical conditions without channel pre-exclusion. HFOs were investigated with novel features extracted from the entire frequency band, and were correlated with SOZ in different states. The results indicate that automatic HFO detection with unsupervised clustering methods exploring the time-frequency content of raw iEEG can be efficiently used to identify the epileptogenic zone with an accurate and efficient manner.

Early seizure detection in an animal model of temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

Talathi, Sachin S.; Hwang, Dong-Uk; Ditto, William; Carney, Paul R.

2007-11-01

The performance of five seizure detection schemes, i.e., Nonlinear embedding delay, Hurst scaling, Wavelet Scale, autocorrelation and gradient of accumulated energy, in their ability to detect EEG seizures close to the seizure onset time were evaluated to determine the feasibility of their application in the development of a real time closed loop seizure intervention program (RCLSIP). The criteria chosen for the performance evaluation were, high statistical robustness as determined through the predictability index, the sensitivity and the specificity of a given measure to detect an EEG seizure, the lag in seizure detection with respect to the EEG seizure onset time, as determined through visual inspection and the computational efficiency for each detection measure. An optimality function was designed to evaluate the overall performance of each measure dependent on the criteria chosen. While each of the above measures analyzed for seizure detection performed very well in terms of the statistical parameters, the nonlinear embedding delay measure was found to have the highest optimality index due to its ability to detect seizure very close to the EEG seizure onset time, thereby making it the most suitable dynamical measure in the development of RCLSIP in rat model with chronic limbic epilepsy.

Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

NASA Astrophysics Data System (ADS)

Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

2014-07-01

Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection technique is introduced in the task of epileptic seizure detection. The raw data are electroencephalography (EEG) signals. Using discrete wavelet transform, the biomedical signals were decomposed into several sets of wavelet coefficients. To reduce the dimension of these wavelet coefficients, a feature selection method that combines the strength of both filter and wrapper methods is proposed. Principal component analysis (PCA) is used as part of the filter method. As for wrapper method, the evolutionary harmony search (HS) algorithm is employed. This metaheuristic method aims at finding the best discriminating set of features from the original data. The obtained features were then used as input for an automated classifier, namely wavelet neural networks (WNNs). The WNNs model was trained to perform a binary classification task, that is, to determine whether a given EEG signal was normal or epileptic. For comparison purposes, different sets of features were also used as input. Simulation results showed that the WNNs that used the features chosen by the hybrid algorithm achieved the highest overall classification accuracy.

Wavelet based analysis of multi-electrode EEG-signals in epilepsy

NASA Astrophysics Data System (ADS)

Hein, Daniel A.; Tetzlaff, Ronald

2005-06-01

For many epilepsy patients seizures cannot sufficiently be controlled by an antiepileptic pharmacatherapy. Furthermore, only in small number of cases a surgical treatment may be possible. The aim of this work is to contribute to the realization of an implantable seizure warning device. By using recordings of electroenzephalographical(EEG) signals obtained from the department of epileptology of the University of Bonn we studied a recently proposed algorithm for the detection of parameter changes in nonlinear systems. Firstly, after calculating the crosscorrelation function between the signals of two electrodes near the epileptic focus, a wavelet-analysis follows using a sliding window with the so called Mexican-Hat wavelet. Then the Shannon-Entropy of the wavelet-transformed data has been determined providing the information content on a time scale in subject to the dilation of the wavelet-transformation. It shows distinct changes at the seizure onset for all dilations and for all patients.

Probability of detection of clinical seizures using heart rate changes.

PubMed

Osorio, Ivan; Manly, B F J

2015-08-01

Heart rate-based seizure detection is a viable complement or alternative to ECoG/EEG. This study investigates the role of various biological factors on the probability of clinical seizure detection using heart rate. Regression models were applied to 266 clinical seizures recorded from 72 subjects to investigate if factors such as age, gender, years with epilepsy, etiology, seizure site origin, seizure class, and data collection centers, among others, shape the probability of EKG-based seizure detection. Clinical seizure detection probability based on heart rate changes, is significantly (p<0.001) shaped by patients' age and gender, seizure class, and years with epilepsy. The probability of detecting clinical seizures (>0.8 in the majority of subjects) using heart rate is highest for complex partial seizures, increases with a patient's years with epilepsy, is lower for females than for males and is unrelated to the side of hemisphere origin. Clinical seizure detection probability using heart rate is multi-factorially dependent and sufficiently high (>0.8) in most cases to be clinically useful. Knowledge of the role that these factors play in shaping said probability will enhance its applicability and usefulness. Heart rate is a reliable and practical signal for extra-cerebral detection of clinical seizures originating from or spreading to central autonomic network structures. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value.

PubMed

Elahian, Bahareh; Yeasin, Mohammed; Mudigoudar, Basanagoud; Wheless, James W; Babajani-Feremi, Abbas

2017-10-01

Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (ECoG) recordings to serve as biomarkers to identify the seizure onset zone (SOZ). We computed the PLV between the phase of the amplitude of high gamma activity (80-150Hz) and the phase of lower frequency rhythms (4-30Hz) from ECoG recordings obtained from 10 patients with epilepsy (21 seizures). We extracted five features from the PLV and used a machine learning approach based on logistic regression to build a model that classifies electrodes as SOZ or non-SOZ. More than 96% of electrodes identified as the SOZ by our algorithm were within the resected area in six seizure-free patients. In four non-seizure-free patients, more than 31% of the identified SOZ electrodes by our algorithm were outside the resected area. In addition, we observed that the seizure outcome in non-seizure-free patients correlated with the number of non-resected SOZ electrodes identified by our algorithm. This machine learning approach, based on features extracted from the PLV, effectively identified electrodes within the SOZ. The approach has the potential to assist clinicians in surgical decision-making when pre-surgical intracranial recordings are utilized. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

PubMed

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

2017-09-27

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Implementation of an Evidence-Based Seizure Algorithm in Intellectual Disability Nursing: A Pilot Study

ERIC Educational Resources Information Center

Auberry, Kathy; Cullen, Deborah

2016-01-01

Based on the results of the Surrogate Decision-Making Self Efficacy Scale (Lopez, 2009a), this study sought to determine whether nurses working in the field of intellectual disability (ID) experience increased confidence when they implemented the American Association of Neuroscience Nurses (AANN) Seizure Algorithm during telephone triage. The…

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy

PubMed Central

Wagenaar, Joost; Abbot, Drew; Adkins, Phillip; Bosshard, Simone C.; Chen, Min; Tieng, Quang M.; He, Jialune; Muñoz-Almaraz, F. J.; Botella-Rocamora, Paloma; Pardo, Juan; Zamora-Martinez, Francisco; Hills, Michael; Wu, Wei; Korshunova, Iryna; Cukierski, Will; Vite, Charles; Patterson, Edward E.; Litt, Brian; Worrell, Gregory A.

2016-01-01

See Mormann and Andrzejak (doi:10.1093/brain/aww091) for a scientific commentary on this article.   Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 ± 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy. PMID:27034258

Exploring the capability of wireless near infrared spectroscopy as a portable seizure detection device for epilepsy patients.

PubMed

Jeppesen, Jesper; Beniczky, Sándor; Johansen, Peter; Sidenius, Per; Fuglsang-Frederiksen, Anders

2015-03-01

Near infrared spectroscopy (NIRS) has proved useful in measuring significant hemodynamic changes in the brain during epileptic seizures. The advance of NIRS-technology into wireless and portable devices raises the possibility of using the NIRS-technology for portable seizure detection. This study used NIRS to measure changes in oxygenated (HbO), deoxygenated (HbR), and total hemoglobin (HbT) at left and right side of the frontal lobe in 33 patients with epilepsy undergoing long-term video-EEG monitoring. Fifteen patients had 34 focal seizures (20 temporal-, 11 frontal-, 2 parietal-lobe, one unspecific) recorded and analyzed with NIRS. Twelve parameters consisting of maximum increase and decrease changes of HbO, HbR and HbT during seizures (1 min before- to 3 min after seizure-onset) for left and right side, were compared with the patients' own non-seizure periods (a 2-h period and a 30-min exercise-period). In both non-seizure periods 4 min moving windows with maximum overlapping were applied to find non-seizure maxima of the 12 parameters. Detection was defined as positive when seizure maximum change exceeded non-seizure maximum change. When analyzing the 12 parameters separately the positive seizure detection was in the range of 6-24%. The increase in hemodynamics was in general better at detecting seizures (15-24%) than the decrease in hemodynamics (6-18%) (P=0.02). NIRS did not seem to be a suitable technology for generic seizure detection given the device, settings, and methods used in this study. There are still several challenges to overcome before the NIRS-technology can be used as a home-monitoring seizure detection device. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Chaos Control of Epileptiform Bursting in the Brain

NASA Astrophysics Data System (ADS)

Slutzky, M. W.; Cvitanovic, P.; Mogul, D. J.

Epilepsy, defined as recurrent seizures, is a pathological state of the brain that afflicts over one percent of the world's population. Seizures occur as populations of neurons in the brain become overly synchronized. Although pharmacological agents are the primary treatment for preventing or reducing the incidence of these seizures, over 30% of epilepsy cases are not adequately helped by standard medical therapies. Several groups are exploring the use of electrical stimulation to terminate or prevent epileptic seizures. One experimental model used to test these algorithms is the brain slice where a select region of the brain is cut and kept viable in a well-oxygenated artificial cerebrospinal fluid. Under certain conditions, such slices may be made to spontaneously and repetitively burst, thereby providing an in vitro model of epilepsy. In this chapter, we discuss our efforts at applying chaos analysis and chaos control algorithms for manipulating this seizure-like behavior in a brain slice model. These techniques may provide a nonlinear control pathway for terminating or potentially preventing epileptic seizures in the whole brain.

EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.

PubMed

Wang, Lei; Long, Xi; Arends, Johan B A M; Aarts, Ronald M

2017-10-01

The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FD t /h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FD t /h of 1.4s). The proposed VGS-based features can help improve seizure detection for ID patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Amplitude Integrated Electroencephalography Compared With Conventional Video EEG for Neonatal Seizure Detection: A Diagnostic Accuracy Study.

PubMed

Rakshasbhuvankar, Abhijeet; Rao, Shripada; Palumbo, Linda; Ghosh, Soumya; Nagarajan, Lakshmi

2017-08-01

This diagnostic accuracy study compared the accuracy of seizure detection by amplitude-integrated electroencephalography with the criterion standard conventional video EEG in term and near-term infants at risk of seizures. Simultaneous recording of amplitude-integrated EEG (2-channel amplitude-integrated EEG with raw trace) and video EEG was done for 24 hours for each infant. Amplitude-integrated EEG was interpreted by a neonatologist; video EEG was interpreted by a neurologist independently. Thirty-five infants were included in the analysis. In the 7 infants with seizures on video EEG, there were 169 seizure episodes on video EEG, of which only 57 were identified by amplitude-integrated EEG. Amplitude-integrated EEG had a sensitivity of 33.7% for individual seizure detection. Amplitude-integrated EEG had an 86% sensitivity for detection of babies with seizures; however, it was nonspecific, in that 50% of infants with seizures detected by amplitude-integrated EEG did not have true seizures by video EEG. In conclusion, our study suggests that amplitude-integrated EEG is a poor screening tool for neonatal seizures.

Multi-modal intelligent seizure acquisition (MISA) system--a new approach towards seizure detection based on full body motion measures.

PubMed

Conradsen, Isa; Beniczky, Sandor; Wolf, Peter; Terney, Daniella; Sams, Thomas; Sorensen, Helge B D

2009-01-01

Many epilepsy patients cannot call for help during a seizure, because they are unconscious or because of the affection of their motor system or speech function. This can lead to injuries, medical complications and at worst death. An alarm system setting off at seizure onset could help to avoid hazards. Today no reliable alarm systems are available. A Multi-modal Intelligent Seizure Acquisition (MISA) system based on full body motion data seems as a good approach towards detection of epileptic seizures. The system is the first to provide a full body description for epilepsy applications. Three test subjects were used for this pilot project. Each subject simulated 15 seizures and in addition performed some predefined normal activities, during a 4-hour monitoring with electromyography (EMG), accelerometer, magnetometer and gyroscope (AMG), electrocardiography (ECG), electroencephalography (EEG) and audio and video recording. The results showed that a non-subject specific MISA system developed on data from the modalities: accelerometer (ACM), gyroscope and EMG is able to detect 98% of the simulated seizures and at the same time mistakes only 4 of the normal movements for seizures. If the system is individualized (subject specific) it is able to detect all simulated seizures with a maximum of 1 false positive. Based on the results from the simulated seizures and normal movements the MISA system seems to be a promising approach to seizure detection.

Crowdsourcing reproducible seizure forecasting in human and canine epilepsy.

PubMed

Brinkmann, Benjamin H; Wagenaar, Joost; Abbot, Drew; Adkins, Phillip; Bosshard, Simone C; Chen, Min; Tieng, Quang M; He, Jialune; Muñoz-Almaraz, F J; Botella-Rocamora, Paloma; Pardo, Juan; Zamora-Martinez, Francisco; Hills, Michael; Wu, Wei; Korshunova, Iryna; Cukierski, Will; Vite, Charles; Patterson, Edward E; Litt, Brian; Worrell, Gregory A

2016-06-01

SEE MORMANN AND ANDRZEJAK DOI101093/BRAIN/AWW091 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE  : Accurate forecasting of epileptic seizures has the potential to transform clinical epilepsy care. However, progress toward reliable seizure forecasting has been hampered by lack of open access to long duration recordings with an adequate number of seizures for investigators to rigorously compare algorithms and results. A seizure forecasting competition was conducted on kaggle.com using open access chronic ambulatory intracranial electroencephalography from five canines with naturally occurring epilepsy and two humans undergoing prolonged wide bandwidth intracranial electroencephalographic monitoring. Data were provided to participants as 10-min interictal and preictal clips, with approximately half of the 60 GB data bundle labelled (interictal/preictal) for algorithm training and half unlabelled for evaluation. The contestants developed custom algorithms and uploaded their classifications (interictal/preictal) for the unknown testing data, and a randomly selected 40% of data segments were scored and results broadcasted on a public leader board. The contest ran from August to November 2014, and 654 participants submitted 17 856 classifications of the unlabelled test data. The top performing entry scored 0.84 area under the classification curve. Following the contest, additional held-out unlabelled data clips were provided to the top 10 participants and they submitted classifications for the new unseen data. The resulting area under the classification curves were well above chance forecasting, but did show a mean 6.54 ± 2.45% (min, max: 0.30, 20.2) decline in performance. The kaggle.com model using open access data and algorithms generated reproducible research that advanced seizure forecasting. The overall performance from multiple contestants on unseen data was better than a random predictor, and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.media-1vid110.1093/brain/aww045_video_abstractaww045_video_abstract. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain.

An ultra low power feature extraction and classification system for wearable seizure detection.

PubMed

Page, Adam; Pramod Tim Oates, Siddharth; Mohsenin, Tinoosh

2015-01-01

In this paper we explore the use of a variety of machine learning algorithms for designing a reliable and low-power, multi-channel EEG feature extractor and classifier for predicting seizures from electroencephalographic data (scalp EEG). Different machine learning classifiers including k-nearest neighbor, support vector machines, naïve Bayes, logistic regression, and neural networks are explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. The input to each machine learning classifier is a 198 feature vector containing 9 features for each of the 22 EEG channels obtained over 1-second windows. All classifiers were able to obtain F1 scores over 80% and onset sensitivity of 100% when tested on 10 patients. Among five different classifiers that were explored, logistic regression (LR) proved to have minimum hardware complexity while providing average F-1 score of 91%. Both ASIC and FPGA implementations of logistic regression are presented and show the smallest area, power consumption, and the lowest latency when compared to the previous work.

Early Detection of Human Epileptic Seizures Based on Intracortical Local Field Potentials

PubMed Central

Park, Yun S.; Hochberg, Leigh R.; Eskandar, Emad N.; Cash, Sydney S.; Truccolo, Wilson

2014-01-01

The unpredictability of re-occurring seizures dramatically impacts the quality of life and autonomy of people with epilepsy. Reliable early seizure detection could open new therapeutic possibilities and thus substantially improve quality of life and autonomy. Though many seizure detection studies have shown the potential of scalp electroencephalogram (EEG) and intracranial EEG (iEEG) signals, reliable early detection of human seizures remains elusive in practice. Here, we examined the use of intracortical local field potentials (LFPs) recorded from 4×4-mm2 96-microelectrode arrays (MEA) for early detection of human epileptic seizures. We adopted a framework consisting of (1) sampling of intracortical LFPs; (2) denoising of LFPs with the Kalman filter; (3) spectral power estimation in specific frequency bands using 1-sec moving time windows; (4) extraction of statistical features, such as the mean, variance, and Fano factor (calculated across channels) of the power in each frequency band; and (5) cost-sensitive support vector machine (SVM) classification of ictal and interictal samples. We tested the framework in one-participant dataset, including 4 seizures and corresponding interictal recordings preceding each seizure. The participant was a 52-year-old woman suffering from complex partial seizures. LFPs were recorded from an MEA implanted in the participant’s left middle temporal gyrus. In this participant, spectral power in 0.3–10 Hz, 20–55 Hz, and 125–250 Hz changed significantly between ictal and interictal epochs. The examined seizure detection framework provided an event-wise sensitivity of 100% (4/4) and only one 20-sec-long false positive event in interictal recordings (likely an undetected subclinical event under further visual inspection), and a detection latency of 4.35 ± 2.21 sec (mean ± std) with respect to iEEG-identified seizure onsets. These preliminary results indicate that intracortical MEA recordings may provide key signals to quickly and reliably detect human seizures. PMID:24663490

Non-EEG seizure detection systems and potential SUDEP prevention: State of the art: Review and update.

PubMed

Van de Vel, Anouk; Cuppens, Kris; Bonroy, Bert; Milosevic, Milica; Jansen, Katrien; Van Huffel, Sabine; Vanrumste, Bart; Cras, Patrick; Lagae, Lieven; Ceulemans, Berten

2016-10-01

Detection of, and alarming for epileptic seizures is increasingly demanded and researched. Our previous review article provided an overview of non-invasive, non-EEG (electro-encephalography) body signals that can be measured, along with corresponding methods, state of the art research, and commercially available systems. Three years later, many more studies and devices have emerged. Moreover, the boom of smart phones and tablets created a new market for seizure detection applications. We performed a thorough literature review and had contact with manufacturers of commercially available devices. This review article gives an updated overview of body signals and methods for seizure detection, international research and (commercially) available systems and applications. Reported results of non-EEG based detection devices vary between 2.2% and 100% sensitivity and between 0 and 3.23 false detections per hour compared to the gold standard video-EEG, for seizures ranging from generalized to convulsive or non-convulsive focal seizures with or without loss of consciousness. It is particularly interesting to include monitoring of autonomic dysfunction, as this may be an important pathophysiological mechanism of SUDEP (sudden unexpected death in epilepsy), and of movement, as many seizures have a motor component. Comparison of research results is difficult as studies focus on different seizure types, timing (night versus day) and patients (adult versus pediatric patients). Nevertheless, we are convinced that the most effective seizure detection systems are multimodal, combining for example detection methods for movement and heart rate, and that devices should especially take into account the user's seizure types and personal preferences. Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Automated detection of epileptic ripples in MEG using beamformer-based virtual sensors

NASA Astrophysics Data System (ADS)

Migliorelli, Carolina; Alonso, Joan F.; Romero, Sergio; Nowak, Rafał; Russi, Antonio; Mañanas, Miguel A.

2017-08-01

Objective. In epilepsy, high-frequency oscillations (HFOs) are expressively linked to the seizure onset zone (SOZ). The detection of HFOs in the noninvasive signals from scalp electroencephalography (EEG) and magnetoencephalography (MEG) is still a challenging task. The aim of this study was to automate the detection of ripples in MEG signals by reducing the high-frequency noise using beamformer-based virtual sensors (VSs) and applying an automatic procedure for exploring the time-frequency content of the detected events. Approach. Two-hundred seconds of MEG signal and simultaneous iEEG were selected from nine patients with refractory epilepsy. A two-stage algorithm was implemented. Firstly, beamforming was applied to the whole head to delimitate the region of interest (ROI) within a coarse grid of MEG-VS. Secondly, a beamformer using a finer grid in the ROI was computed. The automatic detection of ripples was performed using the time-frequency response provided by the Stockwell transform. Performance was evaluated through comparisons with simultaneous iEEG signals. Main results. ROIs were located within the seizure-generating lobes in the nine subjects. Precision and sensitivity values were 79.18% and 68.88%, respectively, by considering iEEG-detected events as benchmarks. A higher number of ripples were detected inside the ROI compared to the same region in the contralateral lobe. Significance. The evaluation of interictal ripples using non-invasive techniques can help in the delimitation of the epileptogenic zone and guide placement of intracranial electrodes. This is the first study that automatically detects ripples in MEG in the time domain located within the clinically expected epileptic area taking into account the time-frequency characteristics of the events through the whole signal spectrum. The algorithm was tested against intracranial recordings, the current gold standard. Further studies should explore this approach to enable the localization of noninvasively recorded HFOs to help during pre-surgical planning and to reduce the need for invasive diagnostics.

Multiple sensor integration for seizure onset detection in human patients comparing conventional disc versus novel tripolar concentric ring electrodes.

PubMed

Makeyev, Oleksandr; Ding, Quan; Martínez-Juárez, Iris E; Gaitanis, John; Kay, Steven M; Besio, Walter G

2013-01-01

As epilepsy affects approximately one percent of the world population, electrical stimulation of the brain has recently shown potential for additive seizure control therapy. Closed-loop systems that apply electrical stimulation when seizure onset is automatically detected require high accuracy of automatic seizure detection based on electrographic brain activity. To improve this accuracy we propose to use noninvasive tripolar concentric ring electrodes that have been shown to have significantly better signal-to-noise ratio, spatial selectivity, and mutual information compared to conventional disc electrodes. The proposed detection methodology is based on integration of multiple sensors using exponentially embedded family (EEF). In this preliminary study it is validated on over 26.3 hours of data collected using both tripolar concentric ring and conventional disc electrodes concurrently each from 7 human patients with epilepsy including five seizures. For a cross-validation based group model EEF correctly detected 100% and 80% of seizures respectively with <0.76 and <1.56 false positive detections per hour respectively for the two electrode modalities. These results clearly suggest the potential of seizure onset detection based on data from tripolar concentric ring electrodes.

An 81.6 μW FastICA processor for epileptic seizure detection.

PubMed

Yang, Chia-Hsiang; Shih, Yi-Hsin; Chiueh, Herming

2015-02-01

To improve the performance of epileptic seizure detection, independent component analysis (ICA) is applied to multi-channel signals to separate artifacts and signals of interest. FastICA is an efficient algorithm to compute ICA. To reduce the energy dissipation, eigenvalue decomposition (EVD) is utilized in the preprocessing stage to reduce the convergence time of iterative calculation of ICA components. EVD is computed efficiently through an array structure of processing elements running in parallel. Area-efficient EVD architecture is realized by leveraging the approximate Jacobi algorithm, leading to a 77.2% area reduction. By choosing proper memory element and reduced wordlength, the power and area of storage memory are reduced by 95.6% and 51.7%, respectively. The chip area is minimized through fixed-point implementation and architectural transformations. Given a latency constraint of 0.1 s, an 86.5% area reduction is achieved compared to the direct-mapped architecture. Fabricated in 90 nm CMOS, the core area of the chip is 0.40 mm(2). The FastICA processor, part of an integrated epileptic control SoC, dissipates 81.6 μW at 0.32 V. The computation delay of a frame of 256 samples for 8 channels is 84.2 ms. Compared to prior work, 0.5% power dissipation, 26.7% silicon area, and 3.4 × computation speedup are achieved. The performance of the chip was verified by human dataset.

Evaluation of a novel median power spectrogram for seizure detection by non-neurophysiologists.

PubMed

Yan, Peter; Melman, Tamar; Yan, Sherry; Otgonsuren, Munkhzul; Grinspan, Zachary

2017-08-01

(1) To evaluate how well resident physicians use a novel EEG spectral analysis tool (the median power spectrogram; MPS) to detect seizures. (2) To assess the capability of the MPS to identify different seizure types. 120 EEG records from children with intractable seizures were converted to MPS by taking the median power across leads and using multi-taper spectral estimation. Twelve blinded neurology residents were trained to interpret the spectrogram with a five-minute video tutorial and post-test. Two residents independently assessed each set for presence of seizures. Their performance was compared to seizures identified using conventional EEG. Two blinded neurologists separately reviewed the EEGs and spectrograms to independently categorize the seizures. Their results were used to determine the spectrogram's capability to reveal seizures and visualize different seizure types for the user. Three key MPS features distinguished seizures from inter-ictal background: power difference relative to background, down-sloping resonance bands, and power in high frequencies. Using these features, residents identified seizures with 77% sensitivity and 72% specificity. 86% (51/59) of focal seizures and 81% (22/27) of generalized seizures were detected by at least one resident. Missed seizures included brief (<60s) seizures, tonic seizures, seizures with predominant delta (0-4Hz) activity, and seizures evident primarily in supplementary low temporal leads. The MPS is a novel qEEG modality that requires minimal training to interpret. It enables physicians without extensive neurophysiology training to identify seizures with sensitivity and specificity comparable to more complex multi-modal qEEG displays. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Seizure Forecasting and the Preictal State in Canine Epilepsy.

PubMed

Varatharajah, Yogatheesan; Iyer, Ravishankar K; Berry, Brent M; Worrell, Gregory A; Brinkmann, Benjamin H

2017-02-01

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state.

SEIZURE FORECASTING AND THE PREICTAL STATE IN CANINE EPILEPSY

PubMed Central

Varatharajah, Yogatheesan; Iyer, Ravishankar K.; Berry, Brent M.; Worrell, Gregory A.; Brinkmann, Benjamin H.

2017-01-01

The ability to predict seizures may enable patients with epilepsy to better manage their medications and activities, potentially reducing side effects and improving quality of life. Forecasting epileptic seizures remains a challenging problem, but machine learning methods using intracranial electroencephalographic (iEEG) measures have shown promise. A machine-learning-based pipeline was developed to process iEEG recordings and generate seizure warnings. Results support the ability to forecast seizures at rates greater than a Poisson random predictor for all feature sets and machine learning algorithms tested. In addition, subject-specific neurophysiological changes in multiple features are reported preceding lead seizures, providing evidence supporting the existence of a distinct and identifiable preictal state. PMID:27464854

Detection of Epileptic Seizure Event and Onset Using EEG

PubMed Central

Ahammad, Nabeel; Fathima, Thasneem; Joseph, Paul

2014-01-01

This study proposes a method of automatic detection of epileptic seizure event and onset using wavelet based features and certain statistical features without wavelet decomposition. Normal and epileptic EEG signals were classified using linear classifier. For seizure event detection, Bonn University EEG database has been used. Three types of EEG signals (EEG signal recorded from healthy volunteer with eye open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. Important features such as energy, entropy, standard deviation, maximum, minimum, and mean at different subbands were computed and classification was done using linear classifier. The performance of classifier was determined in terms of specificity, sensitivity, and accuracy. The overall accuracy was 84.2%. In the case of seizure onset detection, the database used is CHB-MIT scalp EEG database. Along with wavelet based features, interquartile range (IQR) and mean absolute deviation (MAD) without wavelet decomposition were extracted. Latency was used to study the performance of seizure onset detection. Classifier gave a sensitivity of 98.5% with an average latency of 1.76 seconds. PMID:24616892

Non-parametric early seizure detection in an animal model of temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

Talathi, Sachin S.; Hwang, Dong-Uk; Spano, Mark L.; Simonotto, Jennifer; Furman, Michael D.; Myers, Stephen M.; Winters, Jason T.; Ditto, William L.; Carney, Paul R.

2008-03-01

The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure. For the EEG data recorded with microwire electrode array at a sampling rate of 12 kHz, the wavelet scale measure exhibited better overall performance in terms of its ability to detect a seizure with high optimality index value and high statistics in terms of sensitivity and specificity.

Absence of early epileptiform abnormalities predicts lack of seizures on continuous EEG.

PubMed

Shafi, Mouhsin M; Westover, M Brandon; Cole, Andrew J; Kilbride, Ronan D; Hoch, Daniel B; Cash, Sydney S

2012-10-23

To determine whether the absence of early epileptiform abnormalities predicts absence of later seizures on continuous EEG monitoring of hospitalized patients. We retrospectively reviewed 242 consecutive patients without a prior generalized convulsive seizure or active epilepsy who underwent continuous EEG monitoring lasting at least 18 hours for detection of nonconvulsive seizures or evaluation of unexplained altered mental status. The findings on the initial 30-minute screening EEG, subsequent continuous EEG recordings, and baseline clinical data were analyzed. We identified early EEG findings associated with absence of seizures on subsequent continuous EEG. Seizures were detected in 70 (29%) patients. A total of 52 patients had their first seizure in the initial 30 minutes of continuous EEG monitoring. Of the remaining 190 patients, 63 had epileptiform discharges on their initial EEG, 24 had triphasic waves, while 103 had no epileptiform abnormalities. Seizures were later detected in 22% (n = 14) of studies with epileptiform discharges on their initial EEG, vs 3% (n = 3) of the studies without epileptiform abnormalities on initial EEG (p < 0.001). In the 3 patients without epileptiform abnormalities on initial EEG but with subsequent seizures, the first epileptiform discharge or electrographic seizure occurred within the first 4 hours of recording. In patients without epileptiform abnormalities during the first 4 hours of recording, no seizures were subsequently detected. Therefore, EEG features early in the recording may indicate a low risk for seizures, and help determine whether extended monitoring is necessary.

Absence of early epileptiform abnormalities predicts lack of seizures on continuous EEG

PubMed Central

Westover, M. Brandon; Cole, Andrew J.; Kilbride, Ronan D.; Hoch, Daniel B.; Cash, Sydney S.

2012-01-01

Objective: To determine whether the absence of early epileptiform abnormalities predicts absence of later seizures on continuous EEG monitoring of hospitalized patients. Methods: We retrospectively reviewed 242 consecutive patients without a prior generalized convulsive seizure or active epilepsy who underwent continuous EEG monitoring lasting at least 18 hours for detection of nonconvulsive seizures or evaluation of unexplained altered mental status. The findings on the initial 30-minute screening EEG, subsequent continuous EEG recordings, and baseline clinical data were analyzed. We identified early EEG findings associated with absence of seizures on subsequent continuous EEG. Results: Seizures were detected in 70 (29%) patients. A total of 52 patients had their first seizure in the initial 30 minutes of continuous EEG monitoring. Of the remaining 190 patients, 63 had epileptiform discharges on their initial EEG, 24 had triphasic waves, while 103 had no epileptiform abnormalities. Seizures were later detected in 22% (n = 14) of studies with epileptiform discharges on their initial EEG, vs 3% (n = 3) of the studies without epileptiform abnormalities on initial EEG (p < 0.001). In the 3 patients without epileptiform abnormalities on initial EEG but with subsequent seizures, the first epileptiform discharge or electrographic seizure occurred within the first 4 hours of recording. Conclusions: In patients without epileptiform abnormalities during the first 4 hours of recording, no seizures were subsequently detected. Therefore, EEG features early in the recording may indicate a low risk for seizures, and help determine whether extended monitoring is necessary. PMID:23054233

Seizure Forecasting from Idea to Reality. Outcomes of the My Seizure Gauge Epilepsy Innovation Institute Workshop

PubMed Central

French, Jaqueline A.; Fureman, Brandy E.

2017-01-01

Abstract The Epilepsy Innovation Institute (Ei2) is a new research program of the Epilepsy Foundation designed to be an innovation incubator for epilepsy. Ei2 research areas are selected based on community surveys that ask people impacted by epilepsy what they would like researchers to focus on. In their 2016 survey, unpredictability was selected as a top issue regardless of seizure frequency or severity. In response to this need, Ei2 launched the My Seizure Gauge challenge, with the end goal of creating a personalized seizure advisory system device. Prior to moving forward, Ei2 convened a diverse group of stakeholders from people impacted by epilepsy and clinicians, to device developers and data scientists, to basic science researchers and regulators, for a state of the science assessment on seizure forecasting. From the discussions, it was clear that we are at an exciting crossroads. With the advances in bioengineering, we can utilize digital markers, wearables, and biosensors as parameters for a seizure-forecasting algorithm. There are also over a thousand individuals who have been implanted with ambulatory intracranial EEG recording devices. Pairing up peripheral measurements to brain states could identify new relationships and insights. Another key component is the heterogeneity of the relationships indicating that pooling findings across groups is suboptimal, and that data collection will need to be done on longer time scales to allow for individualization of potential seizure-forecasting algorithms. PMID:29291239

Prospective multi-center study of an automatic online seizure detection system for epilepsy monitoring units.

PubMed

Fürbass, F; Ossenblok, P; Hartmann, M; Perko, H; Skupch, A M; Lindinger, G; Elezi, L; Pataraia, E; Colon, A J; Baumgartner, C; Kluge, T

2015-06-01

A method for automatic detection of epileptic seizures in long-term scalp-EEG recordings called EpiScan will be presented. EpiScan is used as alarm device to notify medical staff of epilepsy monitoring units (EMUs) in case of a seizure. A prospective multi-center study was performed in three EMUs including 205 patients. A comparison between EpiScan and the Persyst seizure detector on the prospective data will be presented. In addition, the detection results of EpiScan on retrospective EEG data of 310 patients and the public available CHB-MIT dataset will be shown. A detection sensitivity of 81% was reached for unequivocal electrographic seizures with false alarm rate of only 7 per day. No statistical significant differences in the detection sensitivities could be found between the centers. The comparison to the Persyst seizure detector showed a lower false alarm rate of EpiScan but the difference was not of statistical significance. The automatic seizure detection method EpiScan showed high sensitivity and low false alarm rate in a prospective multi-center study on a large number of patients. The application as seizure alarm device in EMUs becomes feasible and will raise the efficiency of video-EEG monitoring and the safety levels of patients. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Early Detection of Human Focal Seizures Based on Cortical Multiunit Activity

PubMed Central

Park, Yun S.; Hochberg, Leigh R.; Eskandar, Emad N.; Cash, Sydney S.; Truccolo, Wilson

2014-01-01

Approximately 50 million people in the world suffer from epileptic seizures. Reliable early seizure detection could bring significantly beneficial therapeutic alternatives. In recent decades, most approaches have relied on scalp EEG and intracranial EEG signals, but practical early detection for closed-loop seizure control remains challenging. In this study, we present preliminary analyses of an early detection approach based on intracortical neuronal multiunit activity (MUA) recorded from a 96-microelectrode array (MEA). The approach consists of (1) MUA detection from broadband field potentials recorded at 30 kHz by the MEA; (2) MUA feature extraction; (3) cost-sensitive support vector machine classification of ictal and interictal samples; and (4) Kalman-filtering postprocessing. MUA was here defined as the number of threshold crossing (spike counts) applied to the 300 Hz – 6 kHz bandpass filtered local field potentials in 0.1 sec time windows. MUA features explored in this study included the mean, variance, and Fano-factor, computed across the MEA channels. In addition, we used the leading eigenvalues of MUA spatial and temporal correlation matrices computed in 1-sec moving time windows. We assessed the seizure detection approach on out-of-sample data from one-participant recordings with six seizure events and 4.73-hour interictal data. The proposed MUA-based detection approach yielded a 100% sensitivity (6/6) and no false positives, and a latency of 4.17 ± 2.27 sec (mean ± SD) with respect to ECoG-identified seizure onsets. These preliminary results indicate intracortical MUA may be a useful signal for early detection of human epileptic seizures. PMID:25571313

Detection of early seizures by diffuse optical tomography

NASA Astrophysics Data System (ADS)

Zhang, Tao; Hajihashemi, M. Reza; Zhou, Junli; Carney, Paul R.; Jiang, Huabei

2015-03-01

In epilepsy it has been challenging to detect early changes in brain activity that occurs prior to seizure onset and to map their origin and evolution for possible intervention. Besides, preclinical seizure experiments need to be conducted in awake animals with images reconstructed and displayed in real-time. We demonstrate using a rat model of generalized epilepsy that diffuse optical tomography (DOT) provides a unique functional neuroimaging modality for noninvasively and continuously tracking brain activities with high spatiotemporal resolution. We developed methods to conduct seizure experiments in fully awake rats using a subject-specific helmet and a restraining mechanism. For the first time, we detected early hemodynamic responses with heterogeneous patterns several minutes preceding the electroencephalographic seizure onset, supporting the presence of a "pre-seizure" state both in anesthetized and awake rats. Using a novel time-series analysis of scattering images, we show that the analysis of scattered diffuse light is a sensitive and reliable modality for detecting changes in neural activity associated with generalized seizure. We found widespread hemodynamic changes evolving from local regions of the bilateral cortex and thalamus to the entire brain, indicating that the onset of generalized seizures may originate locally rather than diffusely. Together, these findings suggest DOT represents a powerful tool for mapping early seizure onset and propagation pathways.

Looking for complexity in quantitative semiology of frontal and temporal lobe seizures using neuroethology and graph theory.

PubMed

Bertti, Poliana; Tejada, Julian; Martins, Ana Paula Pinheiro; Dal-Cól, Maria Luiza Cleto; Terra, Vera Cristina; de Oliveira, José Antônio Cortes; Velasco, Tonicarlo Rodrigues; Sakamoto, Américo Ceiki; Garcia-Cairasco, Norberto

2014-09-01

Epileptic syndromes and seizures are the expression of complex brain systems. Because no analysis of complexity has been applied to epileptic seizure semiology, our goal was to apply neuroethology and graph analysis to the study of the complexity of behavioral manifestations of epileptic seizures in human frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE). We analyzed the video recordings of 120 seizures of 18 patients with FLE and 28 seizures of 28 patients with TLE. All patients were seizure-free >1 year after surgery (Engel Class I). All patients' behavioral sequences were analyzed by means of a glossary containing all behaviors and analyzed for neuroethology (Ethomatic software). The same series were used for graph analysis (CYTOSCAPE). Behaviors, displayed as nodes, were connected by edges to other nodes according to their temporal sequence of appearance. Using neuroethology analysis, we confirmed data in the literature such as in FLE: brief/frequent seizures, complex motor behaviors, head and eye version, unilateral/bilateral tonic posturing, speech arrest, vocalization, and rapid postictal recovery and in the case of TLE: presence of epigastric aura, lateralized dystonias, impairment of consciousness/speech during ictal and postictal periods, and development of secondary generalization. Using graph analysis metrics of FLE and TLE confirmed data from flowcharts. However, because of the algorithms we used, they highlighted more powerfully the connectivity and complex associations among behaviors in a quite selective manner, depending on the origin of the seizures. The algorithms we used are commonly employed to track brain connectivity from EEG and MRI sources, which makes our study very promising for future studies of complexity in this field. Copyright © 2014 Elsevier Inc. All rights reserved.

Classifying epileptic EEG signals with delay permutation entropy and Multi-Scale K-means.

PubMed

Zhu, Guohun; Li, Yan; Wen, Peng Paul; Wang, Shuaifang

2015-01-01

Most epileptic EEG classification algorithms are supervised and require large training datasets, that hinder their use in real time applications. This chapter proposes an unsupervised Multi-Scale K-means (MSK-means) MSK-means algorithm to distinguish epileptic EEG signals and identify epileptic zones. The random initialization of the K-means algorithm can lead to wrong clusters. Based on the characteristics of EEGs, the MSK-means MSK-means algorithm initializes the coarse-scale centroid of a cluster with a suitable scale factor. In this chapter, the MSK-means algorithm is proved theoretically superior to the K-means algorithm on efficiency. In addition, three classifiers: the K-means, MSK-means MSK-means and support vector machine (SVM), are used to identify seizure and localize epileptogenic zone using delay permutation entropy features. The experimental results demonstrate that identifying seizure with the MSK-means algorithm and delay permutation entropy achieves 4. 7 % higher accuracy than that of K-means, and 0. 7 % higher accuracy than that of the SVM.

The probability of seizures during EEG monitoring in critically ill adults

PubMed Central

Westover, M. Brandon; Shafi, Mouhsin M.; Bianchi, Matt T.; Moura, Lidia M.V.R.; O’Rourke, Deirdre; Rosenthal, Eric S.; Chu, Catherine J.; Donovan, Samantha; Hoch, Daniel B.; Kilbride, Ronan D.; Cole, Andrew J.; Cash, Sydney S.

2014-01-01

Objective To characterize the risk for seizures over time in relation to EEG findings in hospitalized adults undergoing continuous EEG monitoring (cEEG). Methods Retrospective analysis of cEEG data and medical records from 625 consecutive adult inpatients monitored at a tertiary medical center. Using survival analysis methods, we estimated the time-dependent probability that a seizure will occur within the next 72-h, if no seizure has occurred yet, as a function of EEG abnormalities detected so far. Results Seizures occurred in 27% (168/625). The first seizure occurred early (<30 min of monitoring) in 58% (98/168). In 527 patients without early seizures, 159 (30%) had early epileptiform abnormalities, versus 368 (70%) without. Seizures were eventually detected in 25% of patients with early epileptiform discharges, versus 8% without early discharges. The 72-h risk of seizures declined below 5% if no epileptiform abnormalities were present in the first two hours, whereas 16 h of monitoring were required when epileptiform discharges were present. 20% (74/388) of patients without early epileptiform abnormalities later developed them; 23% (17/74) of these ultimately had seizures. Only 4% (12/294) experienced a seizure without preceding epileptiform abnormalities. Conclusions Seizure risk in acute neurological illness decays rapidly, at a rate dependent on abnormalities detected early during monitoring. This study demonstrates that substantial risk stratification is possible based on early EEG abnormalities. Significance These findings have implications for patient-specific determination of the required duration of cEEG monitoring in hospitalized patients. PMID:25082090

Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models.

PubMed

van Luijtelaar, Gilles; Lüttjohann, Annika; Makarov, Vladimir V; Maksimenko, Vladimir A; Koronovskii, Alexei A; Hramov, Alexander E

2016-02-15

Genetic rat models for childhood absence epilepsy have become instrumental in developing theories on the origin of absence epilepsy, the evaluation of new and experimental treatments, as well as in developing new methods for automatic seizure detection, prediction, and/or interference of seizures. Various methods for automated off and on-line analyses of ECoG in rodent models are reviewed, as well as data on how to interfere with the spike-wave discharges by different types of invasive and non-invasive electrical, magnetic, and optical brain stimulation. Also a new method for seizure prediction is proposed. Many selective and specific methods for off- and on-line spike-wave discharge detection seem excellent, with possibilities to overcome the issue of individual differences. Moreover, electrical deep brain stimulation is rather effective in interrupting ongoing spike-wave discharges with low stimulation intensity. A network based method is proposed for absence seizures prediction with a high sensitivity but a low selectivity. Solutions that prevent false alarms, integrated in a closed loop brain stimulation system open the ways for experimental seizure control. The presence of preictal cursor activity detected with state of the art time frequency and network analyses shows that spike-wave discharges are not caused by sudden and abrupt transitions but that there are detectable dynamic events. Their changes in time-space-frequency characteristics might yield new options for seizure prediction and seizure control. Copyright © 2015 Elsevier B.V. All rights reserved.

Apparatus and method for epileptic seizure detection using non-linear techniques

DOEpatents

Hively, Lee M.; Clapp, Ned E.; Daw, C. Stuart; Lawkins, William F.

1998-01-01

Methods and apparatus for automatically detecting epileptic seizures by monitoring and analyzing brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; determining that one or more trends in the nonlinear measures indicate a seizure, and providing notification of seizure occurrence.

Widespread changes in network activity allow non-invasive detection of mesial temporal lobe seizures

PubMed Central

Zepeda, Rodrigo; Cole, Andrew J.; Cash, Sydney S.

2016-01-01

Abstract Decades of experience with intracranial recordings in patients with epilepsy have demonstrated that seizures can occur in deep cortical regions such as the mesial temporal lobes without showing any obvious signs of seizure activity on scalp electroencephalogram. Predicated on the idea that these seizures are purely focal, currently, the only way to detect these ‘scalp-negative seizures’ is with intracranial recordings. However, intracranial recordings are only rarely performed in patients with epilepsy, and are almost never performed outside of the context of epilepsy. As such, little is known about scalp-negative seizures and their role in the natural history of epilepsy, their effect on cognitive function, and their association with other neurological diseases. Here, we developed a novel approach to non-invasively identify scalp-negative seizures arising from the mesial temporal lobe based on scalp electroencephalogram network connectivity measures. We identified 25 scalp-negative mesial temporal lobe seizures in 10 patients and obtained control records from an additional 13 patients, all of whom underwent recordings with foramen ovale electrodes and scalp electroencephalogram. Scalp data from these records were used to train a scalp-negative seizure detector, which consisted of a pair of logistic regression classifiers that used scalp electroencephalogram coherence properties as input features. On cross-validation performance, this detector correctly identified scalp-negative seizures in 40% of patients, and correctly identified the side of seizure onset for each seizure detected. In comparison, routine clinical interpretation of these scalp electroencephalograms failed to identify any of the scalp-negative seizures. Among the patients in whom the detector raised seizure alarms, 80% had scalp-negative mesial temporal lobe seizures. The detector had a false alarm rate of only 0.31 per day and a positive predictive value of 75%. Of the 13 control patients, false seizure alarms were raised in only one patient. The fact that our detector specifically recognizes focal mesial temporal lobe seizures based on scalp electroencephalogram coherence features, lends weight to the hypothesis that even focal seizures are a network phenomenon that involve widespread neural connectivity. Our scalp-negative seizure detector has clear clinical utility in patients with temporal lobe epilepsy, and its potential easily translates to other neurological disorders, such as Alzheimer’s disease, in which occult mesial temporal lobe seizures are suspected to play a significant role. Importantly, our work establishes a novel approach of using computational approaches to non-invasively detect deep seizure activity, without the need for invasive intracranial recordings. PMID:27474219

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks.

PubMed

Movahedi, Faezeh; Coyle, James L; Sejdic, Ervin

2018-05-01

Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this paper, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state-of-the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications. We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms could be modified to better suit the tasks desired. This review is intended to provide researchers with a broad overview of the currently existing deep belief network methodology for electroencephalography signals, as well as to highlight potential challenges for future research.

Mutual Information in Frequency and Its Application to Measure Cross-Frequency Coupling in Epilepsy

NASA Astrophysics Data System (ADS)

Malladi, Rakesh; Johnson, Don H.; Kalamangalam, Giridhar P.; Tandon, Nitin; Aazhang, Behnaam

2018-06-01

We define a metric, mutual information in frequency (MI-in-frequency), to detect and quantify the statistical dependence between different frequency components in the data, referred to as cross-frequency coupling and apply it to electrophysiological recordings from the brain to infer cross-frequency coupling. The current metrics used to quantify the cross-frequency coupling in neuroscience cannot detect if two frequency components in non-Gaussian brain recordings are statistically independent or not. Our MI-in-frequency metric, based on Shannon's mutual information between the Cramer's representation of stochastic processes, overcomes this shortcoming and can detect statistical dependence in frequency between non-Gaussian signals. We then describe two data-driven estimators of MI-in-frequency: one based on kernel density estimation and the other based on the nearest neighbor algorithm and validate their performance on simulated data. We then use MI-in-frequency to estimate mutual information between two data streams that are dependent across time, without making any parametric model assumptions. Finally, we use the MI-in- frequency metric to investigate the cross-frequency coupling in seizure onset zone from electrocorticographic recordings during seizures. The inferred cross-frequency coupling characteristics are essential to optimize the spatial and spectral parameters of electrical stimulation based treatments of epilepsy.

Apparatus and method for epileptic seizure detection using non-linear techniques

DOEpatents

Hively, L.M.; Clapp, N.E.; Daw, C.S.; Lawkins, W.F.

1998-04-28

Methods and apparatus are disclosed for automatically detecting epileptic seizures by monitoring and analyzing brain wave (EEG or MEG) signals. Steps include: acquiring the brain wave data from the patient; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; determining that one or more trends in the nonlinear measures indicate a seizure, and providing notification of seizure occurrence. 76 figs.

Epileptic seizure detection in EEG signal using machine learning techniques.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2018-03-01

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.

Real-time detection, quantification, warning, and control of epileptic seizures: the foundations for a scientific epileptology.

PubMed

Osorio, I; Frei, M G

2009-11-01

Substantive advances in clinical epileptology may be realized through the judicious use of real-time automated seizure detection, quantification, warning, and delivery of therapy in subjects with pharmacoresistant seizures. Materialization of these objectives is likely to elevate epileptology to the level of a mature clinical science.

Prevalence and predictors of subclinical seizures during scalp video-EEG monitoring in patients with epilepsy.

PubMed

Jin, Bo; Wang, Shan; Yang, Linglin; Shen, Chunhong; Ding, Yao; Guo, Yi; Wang, Zhongjin; Zhu, Junming; Wang, Shuang; Ding, Meiping

2017-08-01

This study first aimed to establish the prevalence and predictors of subclinical seizures in patients with epilepsy undergoing video electroencephalographic monitoring, then to evaluate the relationship of sleep/wake and circadian pattern with subclinical seizures. We retrospectively reviewed the charts of 742 consecutive patients admitted to our epilepsy center between July 2012 and October 2014. Demographic, electro-clinical data and neuroimage were collected. A total of 148 subclinical seizures were detected in 39 patients (5.3%) during video electroencephalographic monitoring. The mean duration of subclinical seizures was 47.18 s (range, 5-311). Pharmacoresistant epilepsy, abnormal MRI and the presence of interictal epileptiform discharges were independently associated with subclinical seizures in multivariate logistic regression analysis. Subclinical seizures helped localizing the presumed epileptogenic zone in 24 (61.5%) patients, and suggested multifocal epilepsy in five (12.8%). In addition, subclinical seizures occurred more frequently in sleep and night than wakefulness and daytime, respectively, and they were more likely seen between 21:00-03:00 h, and less likely seen between 09:00-12:00 h. Thirty patients (76.9%) had their first subclinical seizures within the first 24 h of monitoring while only 7.7% of patients had their first subclinical seizures detected within 20 min. Subclinical seizures are not uncommon in patients with epilepsy, particularly in those with pharmacoresistant epilepsy, abnormal MRI or interictal epileptiform discharges. Subclinical seizures occur in specific circadian patterns and in specific sleep/wake distributions. A 20-min VEEG monitoring might not be long enough to allow for their detection.

The probability of seizures during EEG monitoring in critically ill adults.

PubMed

Westover, M Brandon; Shafi, Mouhsin M; Bianchi, Matt T; Moura, Lidia M V R; O'Rourke, Deirdre; Rosenthal, Eric S; Chu, Catherine J; Donovan, Samantha; Hoch, Daniel B; Kilbride, Ronan D; Cole, Andrew J; Cash, Sydney S

2015-03-01

To characterize the risk for seizures over time in relation to EEG findings in hospitalized adults undergoing continuous EEG monitoring (cEEG). Retrospective analysis of cEEG data and medical records from 625 consecutive adult inpatients monitored at a tertiary medical center. Using survival analysis methods, we estimated the time-dependent probability that a seizure will occur within the next 72-h, if no seizure has occurred yet, as a function of EEG abnormalities detected so far. Seizures occurred in 27% (168/625). The first seizure occurred early (<30min of monitoring) in 58% (98/168). In 527 patients without early seizures, 159 (30%) had early epileptiform abnormalities, versus 368 (70%) without. Seizures were eventually detected in 25% of patients with early epileptiform discharges, versus 8% without early discharges. The 72-h risk of seizures declined below 5% if no epileptiform abnormalities were present in the first two hours, whereas 16h of monitoring were required when epileptiform discharges were present. 20% (74/388) of patients without early epileptiform abnormalities later developed them; 23% (17/74) of these ultimately had seizures. Only 4% (12/294) experienced a seizure without preceding epileptiform abnormalities. Seizure risk in acute neurological illness decays rapidly, at a rate dependent on abnormalities detected early during monitoring. This study demonstrates that substantial risk stratification is possible based on early EEG abnormalities. These findings have implications for patient-specific determination of the required duration of cEEG monitoring in hospitalized patients. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Widespread changes in network activity allow non-invasive detection of mesial temporal lobe seizures.

PubMed

Lam, Alice D; Zepeda, Rodrigo; Cole, Andrew J; Cash, Sydney S

2016-10-01

Decades of experience with intracranial recordings in patients with epilepsy have demonstrated that seizures can occur in deep cortical regions such as the mesial temporal lobes without showing any obvious signs of seizure activity on scalp electroencephalogram. Predicated on the idea that these seizures are purely focal, currently, the only way to detect these 'scalp-negative seizures' is with intracranial recordings. However, intracranial recordings are only rarely performed in patients with epilepsy, and are almost never performed outside of the context of epilepsy. As such, little is known about scalp-negative seizures and their role in the natural history of epilepsy, their effect on cognitive function, and their association with other neurological diseases. Here, we developed a novel approach to non-invasively identify scalp-negative seizures arising from the mesial temporal lobe based on scalp electroencephalogram network connectivity measures. We identified 25 scalp-negative mesial temporal lobe seizures in 10 patients and obtained control records from an additional 13 patients, all of whom underwent recordings with foramen ovale electrodes and scalp electroencephalogram. Scalp data from these records were used to train a scalp-negative seizure detector, which consisted of a pair of logistic regression classifiers that used scalp electroencephalogram coherence properties as input features. On cross-validation performance, this detector correctly identified scalp-negative seizures in 40% of patients, and correctly identified the side of seizure onset for each seizure detected. In comparison, routine clinical interpretation of these scalp electroencephalograms failed to identify any of the scalp-negative seizures. Among the patients in whom the detector raised seizure alarms, 80% had scalp-negative mesial temporal lobe seizures. The detector had a false alarm rate of only 0.31 per day and a positive predictive value of 75%. Of the 13 control patients, false seizure alarms were raised in only one patient. The fact that our detector specifically recognizes focal mesial temporal lobe seizures based on scalp electroencephalogram coherence features, lends weight to the hypothesis that even focal seizures are a network phenomenon that involve widespread neural connectivity. Our scalp-negative seizure detector has clear clinical utility in patients with temporal lobe epilepsy, and its potential easily translates to other neurological disorders, such as Alzheimer's disease, in which occult mesial temporal lobe seizures are suspected to play a significant role. Importantly, our work establishes a novel approach of using computational approaches to non-invasively detect deep seizure activity, without the need for invasive intracranial recordings. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Characterization of Early Partial Seizure Onset: Frequency, Complexity and Entropy

PubMed Central

Jouny, Christophe C.; Bergey, Gregory K.

2011-01-01

Objective A clear classification of partial seizures onset features is not yet established. Complexity and entropy have been very widely used to describe dynamical systems, but a systematic evaluation of these measures to characterize partial seizures has never been performed. Methods Eighteen different measures including power in frequency bands up to 300Hz, Gabor atom density (GAD), Higuchi fractal dimension (HFD), Lempel-Ziv complexity, Shannon entropy, sample entropy, and permutation entropy, were selected to test sensitivity to partial seizure onset. Intracranial recordings from forty-five patients with mesial temporal, neocortical temporal and neocortical extratemporal seizure foci were included (331 partial seizures). Results GAD, Lempel-Ziv complexity, HFD, high frequency activity, and sample entropy were the most reliable measures to assess early seizure onset. Conclusions Increases in complexity and occurrence of high-frequency components appear to be commonly associated with early stages of partial seizure evolution from all regions. The type of measure (frequency-based, complexity or entropy) does not predict the efficiency of the method to detect seizure onset. Significance Differences between measures such as GAD and HFD highlight the multimodal nature of partial seizure onsets. Improved methods for early seizure detection may be achieved from a better understanding of these underlying dynamics. PMID:21872526

On the use of harmony search algorithm in the training of wavelet neural networks

NASA Astrophysics Data System (ADS)

Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

2015-10-01

Wavelet neural networks (WNNs) are a class of feedforward neural networks that have been used in a wide range of industrial and engineering applications to model the complex relationships between the given inputs and outputs. The training of WNNs involves the configuration of the weight values between neurons. The backpropagation training algorithm, which is a gradient-descent method, can be used for this training purpose. Nonetheless, the solutions found by this algorithm often get trapped at local minima. In this paper, a harmony search-based algorithm is proposed for the training of WNNs. The training of WNNs, thus can be formulated as a continuous optimization problem, where the objective is to maximize the overall classification accuracy. Each candidate solution proposed by the harmony search algorithm represents a specific WNN architecture. In order to speed up the training process, the solution space is divided into disjoint partitions during the random initialization step of harmony search algorithm. The proposed training algorithm is tested onthree benchmark problems from the UCI machine learning repository, as well as one real life application, namely, the classification of electroencephalography signals in the task of epileptic seizure detection. The results obtained show that the proposed algorithm outperforms the traditional harmony search algorithm in terms of overall classification accuracy.

Epileptic seizure detection in EEG signal with GModPCA and support vector machine.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2017-01-01

Epilepsy is one of the most common neurological disorders caused by recurrent seizures. Electroencephalograms (EEGs) record neural activity and can detect epilepsy. Visual inspection of an EEG signal for epileptic seizure detection is a time-consuming process and may lead to human error; therefore, recently, a number of automated seizure detection frameworks were proposed to replace these traditional methods. Feature extraction and classification are two important steps in these procedures. Feature extraction focuses on finding the informative features that could be used for classification and correct decision-making. Therefore, proposing effective feature extraction techniques for seizure detection is of great significance. Principal Component Analysis (PCA) is a dimensionality reduction technique used in different fields of pattern recognition including EEG signal classification. Global modular PCA (GModPCA) is a variation of PCA. In this paper, an effective framework with GModPCA and Support Vector Machine (SVM) is presented for epileptic seizure detection in EEG signals. The feature extraction is performed with GModPCA, whereas SVM trained with radial basis function kernel performed the classification between seizure and nonseizure EEG signals. Seven different experimental cases were conducted on the benchmark epilepsy EEG dataset. The system performance was evaluated using 10-fold cross-validation. In addition, we prove analytically that GModPCA has less time and space complexities as compared to PCA. The experimental results show that EEG signals have strong inter-sub-pattern correlations. GModPCA and SVM have been able to achieve 100% accuracy for the classification between normal and epileptic signals. Along with this, seven different experimental cases were tested. The classification results of the proposed approach were better than were compared the results of some of the existing methods proposed in literature. It is also found that the time and space complexities of GModPCA are less as compared to PCA. This study suggests that GModPCA and SVM could be used for automated epileptic seizure detection in EEG signal.

Using Dictionary Pair Learning for Seizure Detection.

PubMed

Ma, Xin; Yu, Nana; Zhou, Weidong

2018-02-13

Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary pair learning (DPL) for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. First, for the EEG data, wavelet filtering and differential filtering are applied, and the kernel function is performed to make the signal linearly separable. In DPL, the synthesis dictionary and analysis dictionary are learned jointly from original training samples with alternating minimization method, and sparse coefficients are obtained by using of linear projection instead of costly [Formula: see text]-norm or [Formula: see text]-norm optimization. At last, the reconstructed residuals associated with seizure and nonseizure sub-dictionary pairs are calculated as the decision values, and the postprocessing is performed for improving the recognition rate and reducing the false detection rate of the system. A total of 530[Formula: see text]h from 20 patients with 81 seizures were used to evaluate the system. Our proposed method has achieved an average segment-based sensitivity of 93.39%, specificity of 98.51%, and event-based sensitivity of 96.36% with false detection rate of 0.236/h.

Novel images extraction model using improved delay vector variance feature extraction and multi-kernel neural network for EEG detection and prediction.

PubMed

Ge, Jing; Zhang, Guoping

2015-01-01

Advanced intelligent methodologies could help detect and predict diseases from the EEG signals in cases the manual analysis is inefficient available, for instance, the epileptic seizures detection and prediction. This is because the diversity and the evolution of the epileptic seizures make it very difficult in detecting and identifying the undergoing disease. Fortunately, the determinism and nonlinearity in a time series could characterize the state changes. Literature review indicates that the Delay Vector Variance (DVV) could examine the nonlinearity to gain insight into the EEG signals but very limited work has been done to address the quantitative DVV approach. Hence, the outcomes of the quantitative DVV should be evaluated to detect the epileptic seizures. To develop a new epileptic seizure detection method based on quantitative DVV. This new epileptic seizure detection method employed an improved delay vector variance (IDVV) to extract the nonlinearity value as a distinct feature. Then a multi-kernel functions strategy was proposed in the extreme learning machine (ELM) network to provide precise disease detection and prediction. The nonlinearity is more sensitive than the energy and entropy. 87.5% overall accuracy of recognition and 75.0% overall accuracy of forecasting were achieved. The proposed IDVV and multi-kernel ELM based method was feasible and effective for epileptic EEG detection. Hence, the newly proposed method has importance for practical applications.

Automated detection of videotaped neonatal seizures based on motion segmentation methods.

PubMed

Karayiannis, Nicolaos B; Tao, Guozhi; Frost, James D; Wise, Merrill S; Hrachovy, Richard A; Mizrahi, Eli M

2006-07-01

This study was aimed at the development of a seizure detection system by training neural networks using quantitative motion information extracted by motion segmentation methods from short video recordings of infants monitored for seizures. The motion of the infants' body parts was quantified by temporal motion strength signals extracted from video recordings by motion segmentation methods based on optical flow computation. The area of each frame occupied by the infants' moving body parts was segmented by direct thresholding, by clustering of the pixel velocities, and by clustering the motion parameters obtained by fitting an affine model to the pixel velocities. The computational tools and procedures developed for automated seizure detection were tested and evaluated on 240 short video segments selected and labeled by physicians from a set of video recordings of 54 patients exhibiting myoclonic seizures (80 segments), focal clonic seizures (80 segments), and random infant movements (80 segments). The experimental study described in this paper provided the basis for selecting the most effective strategy for training neural networks to detect neonatal seizures as well as the decision scheme used for interpreting the responses of the trained neural networks. Depending on the decision scheme used for interpreting the responses of the trained neural networks, the best neural networks exhibited sensitivity above 90% or specificity above 90%. The best among the motion segmentation methods developed in this study produced quantitative features that constitute a reliable basis for detecting myoclonic and focal clonic neonatal seizures. The performance targets of this phase of the project may be achieved by combining the quantitative features described in this paper with those obtained by analyzing motion trajectory signals produced by motion tracking methods. A video system based upon automated analysis potentially offers a number of advantages. Infants who are at risk for seizures could be monitored continuously using relatively inexpensive and non-invasive video techniques that supplement direct observation by nursery personnel. This would represent a major advance in seizure surveillance and offers the possibility for earlier identification of potential neurological problems and subsequent intervention.

Subclinical Early Post-Traumatic Seizures Detected by Continuous EEG Monitoring in a Consecutive Pediatric Cohort

PubMed Central

Arndt, Daniel H; Lerner, Jason T; Matsumoto, Joyce H; Madikians, Andranik; Yudovin, Sue; Valino, Heather; McArthur, David L; Wu, Joyce Y; Leung, Michelle; Buxley, Farzad; Szeliga, Conrad; Van Hirtum-Das, Michele; Sankar, Raman; Brooks-Kayal, Amy; Giza, Christopher C

2015-01-01

Summary Purpose Traumatic brain injury (TBI) is an important cause of morbidity and mortality in children and early post-traumatic seizures (EPTS) are a contributing factor to ongoing acute damage. Continuous video EEG monitoring (cEEG) was utilized to assess the burden of clinical and electrographic EPTS. Methods Eighty-seven consecutive, unselected (mild – severe), acute TBI patients requiring pediatric intensive care unit (PICU) admission at 2 academic centers were prospectively monitored with cEEG per established clinical TBI protocols. Clinical and subclinical seizures and status epilepticus (SE, clinical and subclinical) were assessed for their relation to clinical risk factors and short-term outcome measures. Key findings Of all patients, 42.5% (37/87) had seizures. Younger age (p=0.002) and mechanism (abusive head trauma - AHT, p<0.001) were significant risk factors. Subclinical seizures occurred in 16.1% (14/87), 6 of whom had only subclinical seizures. Risk factors for subclinical seizures included: younger age (p<0.001), AHT (p<0.001) and intraaxial bleed (p<0.001). Status Epilepticus (SE) occurred in 18.4% (16/87) with risk factors including: younger age (p<0.001), AHT (p<0.001), and intraaxial bleed (p=0.002). Subclinical SE was detected in 13.8% (12/87) with significant risk factors including: younger age (p<0.001), AHT (p=0.001), and intraaxial bleed (p=0.004). Subclinical seizures were associated with lower discharge KOSCHI score (p=0.002). SE and subclinical SE were associated with increased hospital length of stay (p=0.017 and p=0.041 respectively) and lower hospital discharge KOSCHI (p=0.007 and p=0.040 respectively). Significance cEEG monitoring significantly improves detection of seizures/SE and is the only way to detect subclinical seizures/SE. cEEG may be indicated after pediatric TBI, particularly in younger children, AHT cases, and those with intraaxial blood on CT. PMID:24032982

Early detection of epilepsy seizures based on a weightless neural network.

PubMed

de Aguiar, Kleber; Franca, Felipe M G; Barbosa, Valmir C; Teixeira, Cesar A D

2015-08-01

This work introduces a new methodology for the early detection of epileptic seizure based on the WiSARD weightless neural network model and a new approach in terms of preprocessing the electroencephalogram (EEG) data. WiSARD has, among other advantages, the capacity of perform the training phase in a very fast way. This speed in training is due to the fact that WiSARD's neurons work like Random Access Memories (RAM) addressed by input patterns. Promising results were obtained in the anticipation of seizure onsets in four representative patients from the European Database on Epilepsy (EPILEPSIAE). The proposed seizure early detection WNN architecture was explored by varying the detection anticipation (δ) in the 2 to 30 seconds interval, and by adopting 2 and 3 seconds as the width of the Sliding Observation Window (SOW) input. While in the most challenging patient (A) one obtained accuracies from 99.57% (δ=2s; SOW=3s) to 72.56% (δ=30s; SOW=2s), patient D seizures could be detected in the 99.77% (δ=2s; SOW=2s) to 99.93% (δ=30s; SOW=3s) accuracy interval.

Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds.

PubMed

Gao, Mengxuan; Igata, Hideyoshi; Takeuchi, Aoi; Sato, Kaoru; Ikegaya, Yuji

2017-02-01

Various biological factors have been implicated in convulsive seizures, involving side effects of drugs. For the preclinical safety assessment of drug development, it is difficult to predict seizure-inducing side effects. Here, we introduced a machine learning-based in vitro system designed to detect seizure-inducing side effects. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices, while 14 drugs were bath-perfused at 5 different concentrations each. For each experimental condition, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs) that induced seizure-like events and identified diphenhydramine, enoxacin, strychnine and theophylline as "seizure-inducing" drugs, which indeed were reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to detect the seizure-inducing side effects of preclinical drugs. Copyright © 2017 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

The role of multiple-scale modelling of epilepsy in seizure forecasting

PubMed Central

Kuhlmann, Levin; Grayden, David B.; Wendling, Fabrice; Schiff, Steven J.

2014-01-01

Over the past three decades, a number of seizure prediction, or forecasting, methods have been developed. Although major achievements were accomplished regarding the statistical evaluation of proposed algorithms, it is recognized that further progress is still necessary for clinical application in patients. The lack of physiological motivation can partly explain this limitation. Therefore, a natural question is raised: can computational models of epilepsy be used to improve these methods? Here we review the literature on the multiple-scale neural modelling of epilepsy and the use of such models to infer physiological changes underlying epilepsy and epileptic seizures. We argue how these methods can be applied to advance the state-of-the-art in seizure forecasting. PMID:26035674

Establishment of a novel experimental protocol for drug-induced seizure liability screening based on a locomotor activity assay in zebrafish.

PubMed

Koseki, Naoteru; Deguchi, Jiro; Yamashita, Akihito; Miyawaki, Izuru; Funabashi, Hitoshi

2014-08-01

As drug-induced seizures have severe impact on drug development, evaluating seizure induction potential of candidate drugs at the early stages of drug discovery is important. A novel assay system using zebrafish has attracted interest as a high throughput toxicological in vivo assay system, and we tried to establish an experimental method for drug-induced seizure liability on the basis of locomotor activity in zebrafish. We monitored locomotor activity at high-speed movement (> 20 mm/sec) for 60 min immediately after exposure, and assessed seizure liability potential in some drugs using locomotor activity. However this experimental procedure was not sufficient for predicting seizures because the potential of several drugs with demonstrated seizure potential in mammals was not detected. We, therefore, added other parameters for locomotor activity such as extending exposure time or conducting flashlight stimulation (10 Hz) which is a known seizure induction stimulus, and these additional parameters improved seizure potential detection in some drugs. The validation study using the improved methodology was used to assess 52 commercially available drugs, and the prediction rate was approximately 70%. The experimental protocol established in this present study is considered useful for seizure potential screening during early stages of drug discovery.

Feasibility of recording high frequency oscillations with tripolar concentric ring electrodes during pentylenetetrazole-induced seizures in rats.

PubMed

Makeyev, Oleksandr; Liu, Xiang; Wang, Liling; Zhu, Zhenghan; Taveras, Aristides; Troiano, Derek; Medvedev, Andrei V; Besio, Walter G

2012-01-01

As epilepsy remains a refractory condition in about 30% of patients with complex partial seizures, electrical stimulation of the brain has recently shown potential for additive seizure control therapy. Previously, we applied noninvasive transcranial focal stimulation via novel tripolar concentric ring electrodes (TCREs) on the scalp of rats after inducing seizures with pentylenetetrazole (PTZ). We developed a close-loop system to detect seizures and automatically trigger the stimulation and evaluated its effect on the electrographic activity recorded by TCREs in rats. In our previous work the detectors of seizure onset were based on seizure-induced changes in signal power in the frequency range up to 100 Hz, while in this preliminary study we assess the feasibility of recording high frequency oscillations (HFOs) in the range up to 300 Hz noninvasively with scalp TCREs during PTZ-induced seizures. Grand average power spectral density estimate and generalized likelihood ratio tests were used to compare power of electrographic activity at different stages of seizure development in a group of rats (n= 8). The results suggest that TCREs have the ability to record HFOs from the scalp as well as that scalp-recorded HFOs can potentially be used as features for seizure onset detection.

Delving into α-stable distribution in noise suppression for seizure detection from scalp EEG

NASA Astrophysics Data System (ADS)

Wang, Yueming; Qi, Yu; Wang, Yiwen; Lei, Zhen; Zheng, Xiaoxiang; Pan, Gang

2016-10-01

Objective. There is serious noise in EEG caused by eye blink and muscle activities. The noise exhibits similar morphologies to epileptic seizure signals, leading to relatively high false alarms in most existing seizure detection methods. The objective in this paper is to develop an effective noise suppression method in seizure detection and explore the reason why it works. Approach. Based on a state-space model containing a non-linear observation function and multiple features as the observations, this paper delves deeply into the effect of the α-stable distribution in the noise suppression for seizure detection from scalp EEG. Compared with the Gaussian distribution, the α-stable distribution is asymmetric and has relatively heavy tails. These properties make it more powerful in modeling impulsive noise in EEG, which usually can not be handled by the Gaussian distribution. Specially, we give a detailed analysis in the state estimation process to show the reason why the α-stable distribution can suppress the impulsive noise. Main results. To justify each component in our model, we compare our method with 4 different models with different settings on a collected 331-hour epileptic EEG data. To show the superiority of our method, we compare it with the existing approaches on both our 331-hour data and 892-hour public data. The results demonstrate that our method is most effective in both the detection rate and the false alarm. Significance. This is the first attempt to incorporate the α-stable distribution to a state-space model for noise suppression in seizure detection and achieves the state-of-the-art performance.

Closed-loop control of a fragile network: application to seizure-like dynamics of an epilepsy model

PubMed Central

Ehrens, Daniel; Sritharan, Duluxan; Sarma, Sridevi V.

2015-01-01

It has recently been proposed that the epileptic cortex is fragile in the sense that seizures manifest through small perturbations in the synaptic connections that render the entire cortical network unstable. Closed-loop therapy could therefore entail detecting when the network goes unstable, and then stimulating with an exogenous current to stabilize the network. In this study, a non-linear stochastic model of a neuronal network was used to simulate both seizure and non-seizure activity. In particular, synaptic weights between neurons were chosen such that the network's fixed point is stable during non-seizure periods, and a subset of these connections (the most fragile) were perturbed to make the same fixed point unstable to model seizure events; and, the model randomly transitions between these two modes. The goal of this study was to measure spike train observations from this epileptic network and then apply a feedback controller that (i) detects when the network goes unstable, and then (ii) applies a state-feedback gain control input to the network to stabilize it. The stability detector is based on a 2-state (stable, unstable) hidden Markov model (HMM) of the network, and detects the transition from the stable mode to the unstable mode from using the firing rate of the most fragile node in the network (which is the output of the HMM). When the unstable mode is detected, a state-feedback gain is applied to generate a control input to the fragile node bringing the network back to the stable mode. Finally, when the network is detected as stable again, the feedback control input is switched off. High performance was achieved for the stability detector, and feedback control suppressed seizures within 2 s after onset. PMID:25784851

DOE Office of Scientific and Technical Information (OSTI.GOV)

Murugesan, Sugeerth; Bouchard, Kristofer; Chang, Edward

There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior. We introduce a novel multi-scale visual analysis system, ECoG ClusterFlow, for the detailed exploration of ECoG data. Our systemmore » detects and visualizes dynamic high-level structures, such as communities, derived from the time-varying connectivity network. The system supports two major views: 1) an overview summarizing the evolution of clusters over time and 2) an electrode view using hierarchical glyph-based design to visualize the propagation of clusters in their spatial, anatomical context. We present case studies that were performed in collaboration with neuroscientists and neurosurgeons using simulated and recorded epileptic seizure data to demonstrate our system's effectiveness. ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identify the site of seizure genesis and its spatial progression during various the stages of a seizure. Our system serves as a fast and powerful means for the generation of preliminary hypotheses that can be used as a basis for subsequent application of rigorous statistical methods, with the ultimate goal being the clinical treatment of epileptogenic zones.« less

Single unit action potentials in humans and the effect of seizure activity

PubMed Central

Merricks, Edward M.; Smith, Elliot H.; McKhann, Guy M.; Goodman, Robert R.; Bateman, Lisa M.; Emerson, Ronald G.

2015-01-01

Spike-sorting algorithms have been used to identify the firing patterns of isolated neurons (‘single units’) from implanted electrode recordings in patients undergoing assessment for epilepsy surgery, but we do not know their potential for providing helpful clinical information. It is important therefore to characterize both the stability of these recordings and also their context. A critical consideration is where the units are located with respect to the focus of the pathology. Recent analyses of neuronal spiking activity, recorded over extended spatial areas using microelectrode arrays, have demonstrated the importance of considering seizure activity in terms of two distinct spatial territories: the ictal core and penumbral territories. The pathological information in these two areas, however, is likely to be very different. We investigated, therefore, whether units could be followed reliably over prolonged periods of times in these two areas, including during seizure epochs. We isolated unit recordings from several hundred neurons from four patients undergoing video-telemetry monitoring for surgical evaluation of focal neocortical epilepsies. Unit stability could last in excess of 40 h, and across multiple seizures. A key finding was that in the penumbra, spike stereotypy was maintained even during the seizure. There was a net tendency towards increased penumbral firing during the seizure, although only a minority of units (10–20%) showed significant changes over the baseline period, and notably, these also included neurons showing significant reductions in firing. In contrast, within the ictal core territories, regions characterized by intense hypersynchronous multi-unit firing, our spike sorting algorithms failed as the units were incorporated into the seizure activity. No spike sorting was possible from that moment until the end of the seizure, but recovery of the spike shape was rapid following seizure termination: some units reappeared within tens of seconds of the end of the seizure, and over 80% reappeared within 3 min (τrecov = 104 ± 22 s). The recovery of the mean firing rate was close to pre-ictal levels also within this time frame, suggesting that the more protracted post-ictal state cannot be explained by persistent cellular neurophysiological dysfunction in either the penumbral or the core territories. These studies lay the foundation for future investigations of how these recordings may inform clinical practice. See Kimchi and Cash (doi:10.1093/awv264) for a scientific commentary on this article. PMID:26187332

Optimized methods for epilepsy therapy development using an etiologically realistic model of focal epilepsy in the rat

PubMed Central

Eastman, Clifford L.; Fender, Jason S.; Temkin, Nancy R.; D’Ambrosio, Raimondo

2015-01-01

Conventionally developed antiseizure drugs fail to control epileptic seizures in about 30% of patients, and no treatment prevents epilepsy. New etiologically realistic, syndrome-specific epilepsy models are expected to identify better treatments by capturing currently unknown ictogenic and epileptogenic mechanisms that operate in the corresponding patient populations. Additionally, the use of electrocorticography permits better monitoring of epileptogenesis and the full spectrum of acquired seizures, including focal nonconvulsive seizures that are typically difficult to treat in humans. Thus, the combined use of etiologically realistic models and electrocorticography may improve our understanding of the genesis and progression of epilepsy, and facilitate discovery and translation of novel treatments. However, this approach is labor intensive and must be optimized. To this end, we used an etiologically realistic rat model of posttraumatic epilepsy, in which the initiating fluid percussion injury closely replicates contusive closed-head injury in humans, and has been adapted to maximize epileptogenesis and focal non-convulsive seizures. We obtained week-long 5-electrode electrocorticography 1 month post-injury, and used a Monte-Carlo-based non-parametric bootstrap strategy to test the impact of electrode montage design, duration-based seizure definitions, group size and duration of recordings on the assessment of posttraumatic epilepsy, and on statistical power to detect antiseizure and antiepileptogenic treatment effects. We found that use of seizure definition based on clinical criteria rather than event duration, and of recording montages closely sampling the activity of epileptic foci, maximize the power to detect treatment effects. Detection of treatment effects was marginally improved by prolonged recording, and 24 h recording epochs were sufficient to provide 80% power to detect clinically interesting seizure control or prevention of seizures with small groups of animals. We conclude that appropriate electrode montage and clinically relevant seizure definition permit convenient deployment of fluid percussion injury and electrocorticography for epilepsy therapy development. PMID:25523813

Analyzing large data sets acquired through telemetry from rats exposed to organophosphorous compounds: an EEG study.

PubMed

de Araujo Furtado, Marcio; Zheng, Andy; Sedigh-Sarvestani, Madineh; Lumley, Lucille; Lichtenstein, Spencer; Yourick, Debra

2009-10-30

The organophosphorous compound soman is an acetylcholinesterase inhibitor that causes damage to the brain. Exposure to soman causes neuropathology as a result of prolonged and recurrent seizures. In the present study, long-term recordings of cortical EEG were used to develop an unbiased means to quantify measures of seizure activity in a large data set while excluding other signal types. Rats were implanted with telemetry transmitters and exposed to soman followed by treatment with therapeutics similar to those administered in the field after nerve agent exposure. EEG, activity and temperature were recorded continuously for a minimum of 2 days pre-exposure and 15 days post-exposure. A set of automatic MATLAB algorithms have been developed to remove artifacts and measure the characteristics of long-term EEG recordings. The algorithms use short-time Fourier transforms to compute the power spectrum of the signal for 2-s intervals. The spectrum is then divided into the delta, theta, alpha, and beta frequency bands. A linear fit to the power spectrum is used to distinguish normal EEG activity from artifacts and high amplitude spike wave activity. Changes in time spent in seizure over a prolonged period are a powerful indicator of the effects of novel therapeutics against seizures. A graphical user interface has been created that simultaneously plots the raw EEG in the time domain, the power spectrum, and the wavelet transform. Motor activity and temperature are associated with EEG changes. The accuracy of this algorithm is also verified against visual inspection of video recordings up to 3 days after exposure.

Detection of epileptic seizure in EEG signals using linear least squares preprocessing.

PubMed

Roshan Zamir, Z

2016-09-01

An epileptic seizure is a transient event of abnormal excessive neuronal discharge in the brain. This unwanted event can be obstructed by detection of electrical changes in the brain that happen before the seizure takes place. The automatic detection of seizures is necessary since the visual screening of EEG recordings is a time consuming task and requires experts to improve the diagnosis. Much of the prior research in detection of seizures has been developed based on artificial neural network, genetic programming, and wavelet transforms. Although the highest achieved accuracy for classification is 100%, there are drawbacks, such as the existence of unbalanced datasets and the lack of investigations in performances consistency. To address these, four linear least squares-based preprocessing models are proposed to extract key features of an EEG signal in order to detect seizures. The first two models are newly developed. The original signal (EEG) is approximated by a sinusoidal curve. Its amplitude is formed by a polynomial function and compared with the predeveloped spline function. Different statistical measures, namely classification accuracy, true positive and negative rates, false positive and negative rates and precision, are utilised to assess the performance of the proposed models. These metrics are derived from confusion matrices obtained from classifiers. Different classifiers are used over the original dataset and the set of extracted features. The proposed models significantly reduce the dimension of the classification problem and the computational time while the classification accuracy is improved in most cases. The first and third models are promising feature extraction methods with the classification accuracy of 100%. Logistic, LazyIB1, LazyIB5, and J48 are the best classifiers. Their true positive and negative rates are 1 while false positive and negative rates are 0 and the corresponding precision values are 1. Numerical results suggest that these models are robust and efficient for detecting epileptic seizure. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Interictal epileptiform discharges in persons without a history of seizures: what do they mean?

PubMed

So, Elson L

2010-08-01

Interictal epileptiform discharge (IED) is rarely observed in healthy volunteers without a history of seizures, but higher rates of occurrence are reported in children than in adults. Higher rates are also observed among neurologic inpatients and outpatients without a seizure history, but the risk of subsequent unprovoked seizures or epilepsy is low in healthy volunteers and patients. An exception is the patients with autism spectrum disorders, attention deficit/hyperactivity disorder, or cerebral palsy, who are predisposed to epilepsy development. However, it is currently unclear whether epilepsy risk is higher for patients with incidentally detected IED than for the patients without IED. Hospitalized patients with IED but no prior seizures often have underlying acute or progressive brain disorders. Although they have increased risk of acute seizures, the risk for subsequent unprovoked seizures or epilepsy is unknown and requires assessment on an individual basis. For patients who have psychogenic spells but no seizure history, the rate of IED detection is low, similar to that of healthy volunteers. The association between IED and transitory cognitive impairment has not been established in nonepileptic persons. Evidence thus far does not suggest that routine EEG screening of pilot candidates reduces risk of flight-related accidents.

Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies.

PubMed

Bidwell, Jonathan; Khuwatsamrit, Thanin; Askew, Brittain; Ehrenberg, Joshua Andrew; Helmers, Sandra

2015-11-01

This review surveys current seizure detection and classification technologies as they relate to aiding clinical decision-making during epilepsy treatment. Interviews and data collected from neurologists and a literature review highlighted a strong need for better distinguishing between patients exhibiting generalized and partial seizure types as well as achieving more accurate seizure counts. This information is critical for enabling neurologists to select the correct class of antiepileptic drugs (AED) for their patients and evaluating AED efficiency during long-term treatment. In our questionnaire, 100% of neurologists reported they would like to have video from patients prior to selecting an AED during an initial consultation. Presently, only 30% have access to video. In our technology review we identified that only a subset of available technologies surpassed patient self-reporting performance due to high false positive rates. Inertial seizure detection devices coupled with video capture for recording seizures at night could stand to address collecting seizure counts that are more accurate than current patient self-reporting during day and night time use. Copyright © 2015. Published by Elsevier Ltd.

Effects of Antiepileptic Drugs on Spontaneous Recurrent Seizures in a Novel Model of Extended Hippocampal Kindling in Mice.

PubMed

Song, Hongmei; Tufa, Uilki; Chow, Jonathan; Sivanenthiran, Nila; Cheng, Chloe; Lim, Stellar; Wu, Chiping; Feng, Jiachun; Eubanks, James H; Zhang, Liang

2018-01-01

Epilepsy is a common neurological disorder characterized by naturally-occurring spontaneous recurrent seizures and comorbidities. Kindling has long been used to model epileptogenic mechanisms and to assess antiepileptic drugs. In particular, extended kindling can induce spontaneous recurrent seizures without gross brain lesions, as seen clinically. To date, the development of spontaneous recurrent seizures following extended kindling, and the effect of the antiepileptic drugs on these seizures are not well understood. In the present study we aim to develop a mouse model of extended hippocampal kindling for the first time. Once established, we plan to evaluate the effect of three different antiepileptic drugs on the development of the extended-hippocampal-kindled-induced spontaneous recurrent seizures. Male C57 black mice were used for chronic hippocampal stimulations or handling manipulations (twice daily for up to 70 days). Subsequently, animals underwent continuous video/EEG monitoring for seizure detection. Spontaneous recurrent seizures were consistently observed in extended kindled mice but no seizures were detected in the control animals. The aforementioned seizures were generalized events characterized by hippocampal ictal discharges and concurrent motor seizures. Incidence and severity of the seizures was relatively stable while monitored over a few months after termination of the hippocampal stimulation. Three antiepileptic drugs with distinct action mechanisms were tested: phenytoin, lorazepam and levetiracetam. They were applied via intra-peritoneal injections at anticonvulsive doses and their effects on the spontaneous recurrent seizures were analyzed 10-12 h post-injection. Phenytoin (25 mg/kg) and levetiracetam (400 mg/kg) abolished the spontaneous recurrent seizures. Lorazepam (1.5 mg/kg) decreased motor seizure severity but did not reduce the incidence and duration of corresponding hippocampal discharges, implicating its inhibitory effects on seizure spread. No gross brain lesions were observed in a set of extended hippocampal kindled mice submitted to histological evaluation. All these data suggests that our model could be considered as a novel mouse model of extended hippocampal kindling. Some limitations remain to be considered.

Epileptic Seizure Prediction Using Diffusion Distance and Bayesian Linear Discriminate Analysis on Intracranial EEG.

PubMed

Yuan, Shasha; Zhou, Weidong; Chen, Liyan

2018-02-01

Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature - diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance. The features of all channels composing a feature vector are then fed into a Bayesian Linear Discriminant Analysis (BLDA) classifier. Finally, postprocessing procedure is applied to reduce false prediction alarms. The prediction method is evaluated on the public intracranial EEG dataset, which consists of 577.67[Formula: see text]h of intracranial EEG recordings from 21 patients with 87 seizures. We achieved a sensitivity of 85.11% for a seizure occurrence period of 30[Formula: see text]min and a sensitivity of 93.62% for a seizure occurrence period of 50[Formula: see text]min, both with the seizure prediction horizon of 10[Formula: see text]s. Our false prediction rate was 0.08/h. The proposed method yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.

Standards for testing and clinical validation of seizure detection devices.

PubMed

Beniczky, Sándor; Ryvlin, Philippe

2018-06-01

To increase the quality of studies on seizure detection devices, we propose standards for testing and clinical validation of such devices. We identified 4 key features that are important for studies on seizure detection devices: subjects, recordings, data analysis and alarms, and reference standard. For each of these features, we list the specific aspects that need to be addressed in the studies, and depending on these, studies are classified into 5 phases (0-4). We propose a set of outcome measures that need to be reported, and we propose standards for reporting the results. These standards will help in designing and reporting studies on seizure detection devices, they will give readers clear information on the level of evidence provided by the studies, and they will help regulatory bodies in assessing the quality of the validation studies. These standards are flexible, allowing classification of the studies into one of the 5 phases. We propose actions that can facilitate development of novel methods and devices. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

Real-time Seizure Detection System Using Multiple Single-Neuron Recordings

DTIC Science & Technology

2001-10-25

electrodes were implanted bilaterally into the temporal lobe of each rat. The rats were anesthetized with nebutal (50mg/kg). Small craniotomies were...1997. [9] Fanselow, E.E., Reid, A.P., Nicolelis, M.A.L., Reduction of pentylenetetrazole-induced seizure activity in awake rats by seizure-triggered

Pre-seizure state identified by diffuse optical tomography

PubMed Central

Zhang, Tao; Zhou, Junli; Jiang, Ruixin; Yang, Hao; Carney, Paul R.; Jiang, Huabei

2014-01-01

In epilepsy it has been challenging to detect early changes in brain activity that occurs prior to seizure onset and to map their origin and evolution for possible intervention. Here we demonstrate using a rat model of generalized epilepsy that diffuse optical tomography (DOT) provides a unique functional neuroimaging modality for noninvasively and continuously tracking such brain activities with high spatiotemporal resolution. We detected early hemodynamic responses with heterogeneous patterns, along with intracranial electroencephalogram gamma power changes, several minutes preceding the electroencephalographic seizure onset, supporting the presence of a “pre-seizure” state. We also observed the decoupling between local hemodynamic and neural activities. We found widespread hemodynamic changes evolving from local regions of the bilateral cortex and thalamus to the entire brain, indicating that the onset of generalized seizures may originate locally rather than diffusely. Together, these findings suggest DOT represents a powerful tool for mapping early seizure onset and propagation pathways. PMID:24445927

Performance of Spectrogram-Based Seizure Identification of Adult EEGs by Critical Care Nurses and Neurophysiologists.

PubMed

Amorim, Edilberto; Williamson, Craig A; Moura, Lidia M V R; Shafi, Mouhsin M; Gaspard, Nicolas; Rosenthal, Eric S; Guanci, Mary M; Rajajee, Venkatakrishna; Westover, M Brandon

2017-07-01

Continuous EEG screening using spectrograms or compressed spectral arrays (CSAs) by neurophysiologists has shorter review times with minimal loss of sensitivity for seizure detection when compared with visual analysis of raw EEG. Limited data are available on the performance characteristics of CSA-based seizure detection by neurocritical care nurses. This is a prospective cross-sectional study that was conducted in two academic neurocritical care units and involved 33 neurointensive care unit nurses and four neurophysiologists. All nurses underwent a brief training session before testing. Forty two-hour CSA segments of continuous EEG were reviewed and rated for the presence of seizures. Two experienced clinical neurophysiologists masked to the CSA data performed conventional visual analysis of the raw EEG and served as the gold standard. The overall accuracy was 55.7% among nurses and 67.5% among neurophysiologists. Nurse seizure detection sensitivity was 73.8%, and the false-positive rate was 1-per-3.2 hours. Sensitivity and false-alarm rate for the neurophysiologists was 66.3% and 1-per-6.4 hours, respectively. Interrater agreement for seizure screening was fair for nurses (Gwet AC1 statistic: 43.4%) and neurophysiologists (AC1: 46.3%). Training nurses to perform seizure screening utilizing continuous EEG CSA displays is feasible and associated with moderate sensitivity. Nurses and neurophysiologists had comparable sensitivities, but nurses had a higher false-positive rate. Further work is needed to improve sensitivity and reduce false-alarm rates.

Multi-scale visual analysis of time-varying electrocorticography data via clustering of brain regions

DOE PAGES

Murugesan, Sugeerth; Bouchard, Kristofer; Chang, Edward; ...

2017-06-06

There exists a need for effective and easy-to-use software tools supporting the analysis of complex Electrocorticography (ECoG) data. Understanding how epileptic seizures develop or identifying diagnostic indicators for neurological diseases require the in-depth analysis of neural activity data from ECoG. Such data is multi-scale and is of high spatio-temporal resolution. Comprehensive analysis of this data should be supported by interactive visual analysis methods that allow a scientist to understand functional patterns at varying levels of granularity and comprehend its time-varying behavior. We introduce a novel multi-scale visual analysis system, ECoG ClusterFlow, for the detailed exploration of ECoG data. Our systemmore » detects and visualizes dynamic high-level structures, such as communities, derived from the time-varying connectivity network. The system supports two major views: 1) an overview summarizing the evolution of clusters over time and 2) an electrode view using hierarchical glyph-based design to visualize the propagation of clusters in their spatial, anatomical context. We present case studies that were performed in collaboration with neuroscientists and neurosurgeons using simulated and recorded epileptic seizure data to demonstrate our system's effectiveness. ECoG ClusterFlow supports the comparison of spatio-temporal patterns for specific time intervals and allows a user to utilize various clustering algorithms. Neuroscientists can identify the site of seizure genesis and its spatial progression during various the stages of a seizure. Our system serves as a fast and powerful means for the generation of preliminary hypotheses that can be used as a basis for subsequent application of rigorous statistical methods, with the ultimate goal being the clinical treatment of epileptogenic zones.« less

Towards an Online Seizure Advisory System-An Adaptive Seizure Prediction Framework Using Active Learning Heuristics.

PubMed

Karuppiah Ramachandran, Vignesh Raja; Alblas, Huibert J; Le, Duc V; Meratnia, Nirvana

2018-05-24

In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20 % of the labelled data and also improve the prediction accuracy even under the noisy condition.

[Machine Learning-based Prediction of Seizure-inducing Action as an Adverse Drug Effect].

PubMed

Gao, Mengxuan; Sato, Motoshige; Ikegaya, Yuji

2018-01-01

　During the preclinical research period of drug development, animal testing is widely used to help screen out a drug's dangerous side effects. However, it remains difficult to predict side effects within the central nervous system. Here, we introduce a machine learning-based in vitro system designed to detect seizure-inducing side effects before clinical trial. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices that were bath-perfused with each of 14 different drugs, and at 5 different concentrations of each drug. For each of these experimental conditions, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs) that induced seizure-like events, and identified diphenhydramine, enoxacin, strychnine and theophylline as "seizure-inducing" drugs, which have indeed been reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to pre-clinically detect seizure-inducing side effects of drugs.

Source localization of temporal lobe epilepsy using PCA-LORETA analysis on ictal EEG recordings.

PubMed

Stern, Yaki; Neufeld, Miriam Y; Kipervasser, Svetlana; Zilberstein, Amir; Fried, Itzhak; Teicher, Mina; Adi-Japha, Esther

2009-04-01

Localizing the source of an epileptic seizure using noninvasive EEG suffers from inaccuracies produced by other generators not related to the epileptic source. The authors isolated the ictal epileptic activity, and applied a source localization algorithm to identify its estimated location. Ten ictal EEG scalp recordings from five different patients were analyzed. The patients were known to have temporal lobe epilepsy with a single epileptic focus that had a concordant MRI lesion. The patients had become seizure-free following partial temporal lobectomy. A midinterval (approximately 5 seconds) period of ictal activity was used for Principal Component Analysis starting at ictal onset. The level of epileptic activity at each electrode (i.e., the eigenvector of the component that manifest epileptic characteristic), was used as an input for low-resolution tomography analysis for EEG inverse solution (Zilberstain et al., 2004). The algorithm accurately and robustly identified the epileptic focus in these patients. Principal component analysis and source localization methods can be used in the future to monitor the progression of an epileptic seizure and its expansion to other areas.

Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

PubMed

Jiang, Yizhang; Wu, Dongrui; Deng, Zhaohong; Qian, Pengjiang; Wang, Jun; Wang, Guanjin; Chung, Fu-Lai; Choi, Kup-Sze; Wang, Shitong

2017-12-01

Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be faster and more consistent. However, the classification accuracy is usually not satisfactory for two main reasons: the distributions of the data used for training and testing may be different, and the amount of training data may not be enough. In addition, most machine learning approaches generate black-box models that are difficult to interpret. In this paper, we integrate transductive transfer learning, semi-supervised learning and TSK fuzzy system to tackle these three problems. More specifically, we use transfer learning to reduce the discrepancy in data distribution between the training and testing data, employ semi-supervised learning to use the unlabeled testing data to remedy the shortage of training data, and adopt TSK fuzzy system to increase model interpretability. Two learning algorithms are proposed to train the system. Our experimental results show that the proposed approaches can achieve better performance than many state-of-the-art seizure classification algorithms.

Effects of Antiepileptic Drugs on Spontaneous Recurrent Seizures in a Novel Model of Extended Hippocampal Kindling in Mice

PubMed Central

Song, Hongmei; Tufa, Uilki; Chow, Jonathan; Sivanenthiran, Nila; Cheng, Chloe; Lim, Stellar; Wu, Chiping; Feng, Jiachun; Eubanks, James H.; Zhang, Liang

2018-01-01

Epilepsy is a common neurological disorder characterized by naturally-occurring spontaneous recurrent seizures and comorbidities. Kindling has long been used to model epileptogenic mechanisms and to assess antiepileptic drugs. In particular, extended kindling can induce spontaneous recurrent seizures without gross brain lesions, as seen clinically. To date, the development of spontaneous recurrent seizures following extended kindling, and the effect of the antiepileptic drugs on these seizures are not well understood. In the present study we aim to develop a mouse model of extended hippocampal kindling for the first time. Once established, we plan to evaluate the effect of three different antiepileptic drugs on the development of the extended-hippocampal-kindled-induced spontaneous recurrent seizures. Male C57 black mice were used for chronic hippocampal stimulations or handling manipulations (twice daily for up to 70 days). Subsequently, animals underwent continuous video/EEG monitoring for seizure detection. Spontaneous recurrent seizures were consistently observed in extended kindled mice but no seizures were detected in the control animals. The aforementioned seizures were generalized events characterized by hippocampal ictal discharges and concurrent motor seizures. Incidence and severity of the seizures was relatively stable while monitored over a few months after termination of the hippocampal stimulation. Three antiepileptic drugs with distinct action mechanisms were tested: phenytoin, lorazepam and levetiracetam. They were applied via intra-peritoneal injections at anticonvulsive doses and their effects on the spontaneous recurrent seizures were analyzed 10–12 h post-injection. Phenytoin (25 mg/kg) and levetiracetam (400 mg/kg) abolished the spontaneous recurrent seizures. Lorazepam (1.5 mg/kg) decreased motor seizure severity but did not reduce the incidence and duration of corresponding hippocampal discharges, implicating its inhibitory effects on seizure spread. No gross brain lesions were observed in a set of extended hippocampal kindled mice submitted to histological evaluation. All these data suggests that our model could be considered as a novel mouse model of extended hippocampal kindling. Some limitations remain to be considered. PMID:29867462

Comparison of the Intramuscular, Intranasal or Sublingual Routes of Midazolam Administration for the Control of Soman-Induced Seizures

DTIC Science & Technology

2008-01-01

23, 2008) Abstract: This study evaluated the anticonvulsant effectiveness of midazolam to stop seizures elicited by the nerve agent soman when...seizure activity that was detected in the electroencephalographic record. When given immediately after seizure onset, the anticonvulsant ED 50 of...that time. At the 40-min. treatment delay, the anticonvulsant ED 50 s of intramuscular or intranasal midazolam did not differ and both were

Seizures in Pediatric Patients With Liver Transplant and Efficacy of Levetiracetam.

PubMed

Kılıç, Betül; Güngör, Serdal; Arslan, Müjgan; Selimoğlu, Mukadder Ayşe; Yılmaz, Sezai

2017-07-01

The aim of this study was to evaluate the risk factors, clinical implications, and prognosis of new-onset seizures that occurred after pediatric liver transplantation, and to assess the efficacy of levetiracetam treatment. The clinical and laboratory data of liver transplanted 28 children who had seizures after liver transplantation and specifically of 18 children who received levetiracetam were analyzed retrospectively. Sixteen patients (88.9%) remained seizure-free and in 2 (11.1%), more than 50% reduction in seizures were detected with levetiracetam treatment. In conclusion, seizures are generally the most common complication by a spectrum of seizure types, and sometimes cause symptomatic epilepsy. The most common risk factors for seizures in transplant recipients is immunosuppressant toxicity. Currently, there isn't a specific treatment involving the transplant patient population. Levetiracetam may be preferable in pediatric patients as it's reliable for liver disease and has advantages in the treatment of postoperative seizures due to its intravenous usage.

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

PubMed

Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

2018-04-02

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Automated detection of a preseizure state based on a decrease in synchronization in intracranial electroencephalogram recordings from epilepsy patients

NASA Astrophysics Data System (ADS)

Mormann, Florian; Andrzejak, Ralph G.; Kreuz, Thomas; Rieke, Christoph; David, Peter; Elger, Christian E.; Lehnertz, Klaus

2003-02-01

The question whether information extracted from the electroencephalogram (EEG) of epilepsy patients can be used for the prediction of seizures has recently attracted much attention. Several studies have reported evidence for the existence of a preseizure state that can be detected using different measures derived from the theory of dynamical systems. Most of these studies, however, have neglected to sufficiently investigate the specificity of the observed effects or suffer from other methodological shortcomings. In this paper we present an automated technique for the detection of a preseizure state from EEG recordings using two different measures for synchronization between recording sites, namely, the mean phase coherence as a measure for phase synchronization and the maximum linear cross correlation as a measure for lag synchronization. Based on the observation of characteristic drops in synchronization prior to seizure onset, we used this phenomenon for the characterization of a preseizure state and its distinction from the remaining seizure-free interval. After optimizing our technique on a group of 10 patients with temporal lobe epilepsy we obtained a successful detection of a preseizure state prior to 12 out of 14 analyzed seizures for both measures at a very high specificity as tested on recordings from the seizure-free interval. After checking for in-sample overtraining via cross validation, we applied a surrogate test to validate the observed predictability. Based on our results, we discuss the differences of the two synchronization measures in terms of the dynamics underlying seizure generation in focal epilepsies.

Comparison between Scalp EEG and Behind-the-Ear EEG for Development of a Wearable Seizure Detection System for Patients with Focal Epilepsy

PubMed Central

Gu, Ying; Cleeren, Evy; Dan, Jonathan; Claes, Kasper; Hunyadi, Borbála

2017-01-01

A wearable electroencephalogram (EEG) device for continuous monitoring of patients suffering from epilepsy would provide valuable information for the management of the disease. Currently no EEG setup is small and unobtrusive enough to be used in daily life. Recording behind the ear could prove to be a solution to a wearable EEG setup. This article examines the feasibility of recording epileptic EEG from behind the ear. It is achieved by comparison with scalp EEG recordings. Traditional scalp EEG and behind-the-ear EEG were simultaneously acquired from 12 patients with temporal, parietal, or occipital lobe epilepsy. Behind-the-ear EEG consisted of cross-head channels and unilateral channels. The analysis on Electrooculography (EOG) artifacts resulting from eye blinking showed that EOG artifacts were absent on cross-head channels and had significantly small amplitudes on unilateral channels. Temporal waveform and frequency content during seizures from behind-the-ear EEG visually resembled that from scalp EEG. Further, coherence analysis confirmed that behind-the-ear EEG acquired meaningful epileptic discharges similarly to scalp EEG. Moreover, automatic seizure detection based on support vector machine (SVM) showed that comparable seizure detection performance can be achieved using these two recordings. With scalp EEG, detection had a median sensitivity of 100% and a false detection rate of 1.14 per hour, while, with behind-the-ear EEG, it had a median sensitivity of 94.5% and a false detection rate of 0.52 per hour. These findings demonstrate the feasibility of detecting seizures from EEG recordings behind the ear for patients with focal epilepsy. PMID:29295522

Mapping cortical haemodynamics during neonatal seizures using diffuse optical tomography: A case study

PubMed Central

Singh, Harsimrat; Cooper, Robert J.; Wai Lee, Chuen; Dempsey, Laura; Edwards, Andrea; Brigadoi, Sabrina; Airantzis, Dimitrios; Everdell, Nick; Michell, Andrew; Holder, David; Hebden, Jeremy C.; Austin, Topun

2014-01-01

Seizures in the newborn brain represent a major challenge to neonatal medicine. Neonatal seizures are poorly classified, under-diagnosed, difficult to treat and are associated with poor neurodevelopmental outcome. Video-EEG is the current gold-standard approach for seizure detection and monitoring. Interpreting neonatal EEG requires expertise and the impact of seizures on the developing brain remains poorly understood. In this case study we present the first ever images of the haemodynamic impact of seizures on the human infant brain, obtained using simultaneous diffuse optical tomography (DOT) and video-EEG with whole-scalp coverage. Seven discrete periods of ictal electrographic activity were observed during a 60 minute recording of an infant with hypoxic–ischaemic encephalopathy. The resulting DOT images show a remarkably consistent, high-amplitude, biphasic pattern of changes in cortical blood volume and oxygenation in response to each electrographic event. While there is spatial variation across the cortex, the dominant haemodynamic response to seizure activity consists of an initial increase in cortical blood volume prior to a large and extended decrease typically lasting several minutes. This case study demonstrates the wealth of physiologically and clinically relevant information that DOT–EEG techniques can yield. The consistency and scale of the haemodynamic responses observed here also suggest that DOT–EEG has the potential to provide improved detection of neonatal seizures. PMID:25161892

Automated detection of videotaped neonatal seizures of epileptic origin.

PubMed

Karayiannis, Nicolaos B; Xiong, Yaohua; Tao, Guozhi; Frost, James D; Wise, Merrill S; Hrachovy, Richard A; Mizrahi, Eli M

2006-06-01

This study aimed at the development of a seizure-detection system by training neural networks with quantitative motion information extracted from short video segments of neonatal seizures of the myoclonic and focal clonic types and random infant movements. The motion of the infants' body parts was quantified by temporal motion-strength signals extracted from video segments by motion-segmentation methods based on optical flow computation. The area of each frame occupied by the infants' moving body parts was segmented by clustering the motion parameters obtained by fitting an affine model to the pixel velocities. The motion of the infants' body parts also was quantified by temporal motion-trajectory signals extracted from video recordings by robust motion trackers based on block-motion models. These motion trackers were developed to adjust autonomously to illumination and contrast changes that may occur during the video-frame sequence. Video segments were represented by quantitative features obtained by analyzing motion-strength and motion-trajectory signals in both the time and frequency domains. Seizure recognition was performed by conventional feed-forward neural networks, quantum neural networks, and cosine radial basis function neural networks, which were trained to detect neonatal seizures of the myoclonic and focal clonic types and to distinguish them from random infant movements. The computational tools and procedures developed for automated seizure detection were evaluated on a set of 240 video segments of 54 patients exhibiting myoclonic seizures (80 segments), focal clonic seizures (80 segments), and random infant movements (80 segments). Regardless of the decision scheme used for interpreting the responses of the trained neural networks, all the neural network models exhibited sensitivity and specificity>90%. For one of the decision schemes proposed for interpreting the responses of the trained neural networks, the majority of the trained neural-network models exhibited sensitivity>90% and specificity>95%. In particular, cosine radial basis function neural networks achieved the performance targets of this phase of the project (i.e., sensitivity>95% and specificity>95%). The best among the motion segmentation and tracking methods developed in this study produced quantitative features that constitute a reliable basis for detecting neonatal seizures. The performance targets of this phase of the project were achieved by combining the quantitative features obtained by analyzing motion-strength signals with those produced by analyzing motion-trajectory signals. The computational procedures and tools developed in this study to perform off-line analysis of short video segments will be used in the next phase of this project, which involves the integration of these procedures and tools into a system that can process and analyze long video recordings of infants monitored for seizures in real time.

Intraoperative seizures during craniotomy under general anesthesia.

PubMed

Howe, John; Lu, Xiaoying; Thompson, Zoe; Peterson, Gordon W; Losey, Travis E

2016-05-01

An acute symptomatic seizure is a clinical seizure occurring at the time of or in close temporal association with a brain insult. We report an acute symptomatic seizure occurring during a surgical procedure in a patient who did not have a prior history of epilepsy and who did not have a lesion associated with an increased risk of epilepsy. To characterize the incidence and clinical features of intraoperative seizures during craniotomy under general anesthesia, we reviewed cases where continuous EEG was acquired during craniotomy. Records of 400 consecutive cases with propofol as general anesthesia during craniotomy were reviewed. Demographic data, indication for surgery, clinical history, history of prior seizures, duration of surgery and duration of burst suppression were recorded. Cases where seizures were observed were analyzed in detail. Two out of 400 patients experienced intraoperative seizures, including one patient who appeared to have an acute symptomatic seizure related to the surgical procedure itself and a second patient who experienced two seizures likely related to an underlying diagnosis of epilepsy. This is the first report of an acute symptomatic seizure secondary to a neurosurgical procedure. Overall, 0.5% of patients monitored experienced seizures, indicating that intraoperative seizures are rare, and EEG monitoring during craniotomies is of low yield in detecting seizures. Copyright © 2016. Published by Elsevier Ltd.

Responsive cortical stimulation for the treatment of medically intractable partial epilepsy.

PubMed

Morrell, Martha J

2011-09-27

This multicenter, double-blind, randomized controlled trial assessed the safety and effectiveness of responsive cortical stimulation as an adjunctive therapy for partial onset seizures in adults with medically refractory epilepsy. A total of 191 adults with medically intractable partial epilepsy were implanted with a responsive neurostimulator connected to depth or subdural leads placed at 1 or 2 predetermined seizure foci. The neurostimulator was programmed to detect abnormal electrocorticographic activity. One month after implantation, subjects were randomized 1:1 to receive stimulation in response to detections (treatment) or to receive no stimulation (sham). Efficacy and safety were assessed over a 12-week blinded period and a subsequent 84-week open-label period during which all subjects received responsive stimulation. Seizures were significantly reduced in the treatment (-37.9%, n = 97) compared to the sham group (-17.3%, n = 94; p = 0.012) during the blinded period and there was no difference between the treatment and sham groups in adverse events. During the open-label period, the seizure reduction was sustained in the treatment group and seizures were significantly reduced in the sham group when stimulation began. There were significant improvements in overall quality of life (p < 0.02) and no deterioration in mood or neuropsychological function. Responsive cortical stimulation reduces the frequency of disabling partial seizures, is associated with improvements in quality of life, and is well-tolerated with no mood or cognitive effects. Responsive stimulation may provide another adjunctive treatment option for adults with medically intractable partial seizures. This study provides Class I evidence that responsive cortical stimulation is effective in significantly reducing seizure frequency for 12 weeks in adults who have failed 2 or more antiepileptic medication trials, 3 or more seizures per month, and 1 or 2 seizure foci.

Predicting epileptic seizures from scalp EEG based on attractor state analysis.

PubMed

Chu, Hyunho; Chung, Chun Kee; Jeong, Woorim; Cho, Kwang-Hyun

2017-05-01

Epilepsy is the second most common disease of the brain. Epilepsy makes it difficult for patients to live a normal life because it is difficult to predict when seizures will occur. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. In this paper, we investigate a novel seizure precursor based on attractor state analysis for seizure prediction. We analyze the transition process from normal to seizure attractor state and investigate a precursor phenomenon seen before reaching the seizure attractor state. From the result of an analysis, we define a quantified spectral measure in scalp EEG for seizure prediction. From scalp EEG recordings, the Fourier coefficients of six EEG frequency bands are extracted, and the defined spectral measure is computed based on the coefficients for each half-overlapped 20-second-long window. The computed spectral measure is applied to seizure prediction using a low-complexity methodology. Within scalp EEG, we identified an early-warning indicator before an epileptic seizure occurs. Getting closer to the bifurcation point that triggers the transition from normal to seizure state, the power spectral density of low frequency bands of the perturbation of an attractor in the EEG, showed a relative increase. A low-complexity seizure prediction algorithm using this feature was evaluated, using ∼583h of scalp EEG in which 143 seizures in 16 patients were recorded. With the test dataset, the proposed method showed high sensitivity (86.67%) with a false prediction rate of 0.367h -1 and average prediction time of 45.3min. A novel seizure prediction method using scalp EEG, based on attractor state analysis, shows potential for application with real epilepsy patients. This is the first study in which the seizure-precursor phenomenon of an epileptic seizure is investigated based on attractor-based analysis of the macroscopic dynamics of the brain. With the scalp EEG, we first propose use of a spectral feature identified for seizure prediction, in which the dynamics of an attractor are excluded, and only the perturbation dynamics from the attractor are considered. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated Detection of Epileptic Biomarkers in Resting-State Interictal MEG Data

PubMed Central

Soriano, Miguel C.; Niso, Guiomar; Clements, Jillian; Ortín, Silvia; Carrasco, Sira; Gudín, María; Mirasso, Claudio R.; Pereda, Ernesto

2017-01-01

Certain differences between brain networks of healthy and epilectic subjects have been reported even during the interictal activity, in which no epileptic seizures occur. Here, magnetoencephalography (MEG) data recorded in the resting state is used to discriminate between healthy subjects and patients with either idiopathic generalized epilepsy or frontal focal epilepsy. Signal features extracted from interictal periods without any epileptiform activity are used to train a machine learning algorithm to draw a diagnosis. This is potentially relevant to patients without frequent or easily detectable spikes. To analyze the data, we use an up-to-date machine learning algorithm and explore the benefits of including different features obtained from the MEG data as inputs to the algorithm. We find that the relative power spectral density of the MEG time-series is sufficient to distinguish between healthy and epileptic subjects with a high prediction accuracy. We also find that a combination of features such as the phase-locked value and the relative power spectral density allow to discriminate generalized and focal epilepsy, when these features are calculated over a filtered version of the signals in certain frequency bands. Machine learning algorithms are currently being applied to the analysis and classification of brain signals. It is, however, less evident to identify the proper features of these signals that are prone to be used in such machine learning algorithms. Here, we evaluate the influence of the input feature selection on a clinical scenario to distinguish between healthy and epileptic subjects. Our results indicate that such distinction is possible with a high accuracy (86%), allowing the discrimination between idiopathic generalized and frontal focal epilepsy types. PMID:28713260

Capturing the state transitions of seizure-like events using Hidden Markov models.

PubMed

Guirgis, Mirna; Serletis, Demitre; Carlen, Peter L; Bardakjian, Berj L

2011-01-01

The purpose of this study was to investigate the number of states present in the progression of a seizure-like event (SLE). Of particular interest is to determine if there are more than two clearly defined states, as this would suggest that there is a distinct state preceding an SLE. Whole-intact hippocampus from C57/BL mice was used to model epileptiform activity induced by the perfusion of a low Mg(2+)/high K(+) solution while extracellular field potentials were recorded from CA3 pyramidal neurons. Hidden Markov models (HMM) were used to model the state transitions of the recorded SLEs by incorporating various features of the Hilbert transform into the training algorithm; specifically, 2- and 3-state HMMs were explored. Although the 2-state model was able to distinguish between SLE and nonSLE behavior, it provided no improvements compared to visual inspection alone. However, the 3-state model was able to capture two distinct nonSLE states that visual inspection failed to discriminate. Moreover, by developing an HMM based system a priori knowledge of the state transitions was not required making this an ideal platform for seizure prediction algorithms.

A comparison of continuous video-EEG monitoring and 30-minute EEG in an ICU.

PubMed

Khan, Omar I; Azevedo, Christina J; Hartshorn, Alendia L; Montanye, Justin T; Gonzalez, Juan C; Natola, Mark A; Surgenor, Stephen D; Morse, Richard P; Nordgren, Richard E; Bujarski, Krzysztof A; Holmes, Gregory L; Jobst, Barbara C; Scott, Rod C; Thadani, Vijay M

2014-12-01

To determine whether there is added benefit in detecting electrographic abnormalities from 16-24 hours of continuous video-EEG in adult medical/surgical ICU patients, compared to a 30-minute EEG. This was a prospectively enroled non-randomized study of 130 consecutive ICU patients for whom EEG was requested. For 117 patients, a 30-minute EEG was requested for altered mental state and/or suspected seizures; 83 patients continued with continuous video-EEG for 16-24 hours and 34 patients had only the 30-minute EEG. For 13 patients with prior seizures, continuous video-EEG was requested and was carried out for 16-24 hours. We gathered EEG data prospectively, and reviewed the medical records retrospectively to assess the impact of continuous video-EEG. A total of 83 continuous video-EEG recordings were performed for 16-24 hours beyond 30 minutes of routine EEG. All were slow, and 34% showed epileptiform findings in the first 30 minutes, including 2% with seizures. Over 16-24 hours, 14% developed new or additional epileptiform abnormalities, including 6% with seizures. In 8%, treatment was changed based on continuous video-EEG. Among the 34 EEGs limited to 30 minutes, almost all were slow and 18% showed epileptiform activity, including 3% with seizures. Among the 13 patients with known seizures, continuous video-EEG was slow in all and 69% had epileptiform abnormalities in the first 30 minutes, including 31% with seizures. An additional 8% developed epileptiform abnormalities over 16-24 hours. In 46%, treatment was changed based on continuous video-EEG. This study indicates that if continuous video-EEG is not available, a 30-minute EEG in the ICU has a substantial diagnostic yield and will lead to the detection of the majority of epileptiform abnormalities. In a small percentage of patients, continuous video-EEG will lead to the detection of additional epileptiform abnormalities. In a sub-population, with a history of seizures prior to the initiation of EEG recording, the benefits of continuous video-EEG in monitoring seizure activity and influencing treatment may be greater.

Reversible MRI lesions after seizures.

PubMed

Aykut-Bingol, C; Tekin, S; Ince, D; Aktan, S

1997-06-01

After generalized or partial seizures, transient lesions may appear on magnetic resonance (MR) images. The mechanisms of MR changes might be a defect in cerebral autoregulation and blood-brain permeability. We report a patient with partial and secondary generalized tonic-clonic seizures. After her first seizure which was generalized tonic-clonic in nature, we detected multiple high signal intensities over the frontal cortical area on proton density images which were enhanced with gadolinium on T1-weighted images. The first and repeated EEGs showed no abnormalities or epileptic discharges. We started carbamezapine (600 mg/d) and excluded systemic diseases like vasculitis, infections, aetiological factors causing cerebrovascular diseases. In the follow-up, she was seizure free under antiepileptic therapy and no other neurological deficit. Repeated MR scans after 24 months from her first seizure revealed no pathologic signal intensities. Although the pathophysiology is unknown, recognition of reversible lesions helps diagnostic and therapeutic approaches to abnormal MR findings after seizures.

Therapeutic Devices for Epilepsy

PubMed Central

Fisher, Robert S.

2011-01-01

Therapeutic devices provide new options for treating drug-resistant epilepsy. These devices act by a variety of mechanisms to modulate neuronal activity. Only vagus nerve stimulation, which continues to develop new technology, is approved for use in the United States. Deep brain stimulation (DBS) of anterior thalamus for partial epilepsy recently was approved in Europe and several other countries. Responsive neurostimulation, which delivers stimuli to one or two seizure foci in response to a detected seizure, recently completed a successful multicenter trial. Several other trials of brain stimulation are in planning or underway. Transcutaneous magnetic stimulation (TMS) may provide a noninvasive method to stimulate cortex. Controlled studies of TMS split on efficacy, and may depend on whether a seizure focus is near a possible region for stimulation. Seizure detection devices in the form of “shake” detectors via portable accelerometers can provide notification of an ongoing tonic-clonic seizure, or peace of mind in the absence of notification. Prediction of seizures from various aspects of EEG is in early stages. Prediction appears to be possible in a subpopulation of people with refractory seizures and a clinical trial of an implantable prediction device is underway. Cooling of neocortex or hippocampus reversibly can attenuate epileptiform EEG activity and seizures, but engineering problems remain in its implementation. Optogenetics is a new technique that can control excitability of specific populations of neurons with light. Inhibition of epileptiform activity has been demonstrated in hippocampal slices, but use in humans will require more work. In general, devices provide useful palliation for otherwise uncontrollable seizures, but with a different risk profile than with most drugs. Optimizing the place of devices in therapy for epilepsy will require further development and clinical experience. PMID:22367987

Vagus nerve stimulation magnet activation for seizures: a critical review.

PubMed

Fisher, R S; Eggleston, K S; Wright, C W

2015-01-01

Some patients receiving VNS Therapy report benefit from manually activating the generator with a handheld magnet at the time of a seizure. A review of 20 studies comprising 859 subjects identified patients who reported on-demand magnet mode stimulation to be beneficial. Benefit was reported in a weighted average of 45% of patients (range 0-89%) using the magnet, with seizure cessation claimed in a weighted average of 28% (range 15-67%). In addition to seizure termination, patients sometimes reported decreased intensity or duration of seizures or the post-ictal period. One study reported an isolated instance of worsening with magnet stimulation (Arch Pediatr Adolesc Med, 157, 2003 and 560). All of the reviewed studies assessed adjunctive magnet use. No studies were designed to provide Level I evidence of efficacy of magnet-induced stimulation. Retrospective analysis of one pivotal randomized trial of VNS therapy showed significantly more seizures terminated or improved in the active stimulation group vs the control group. Prospective, controlled studies would be required to isolate the effect and benefit of magnet mode stimulation and to document that the magnet-induced stimulation is the proximate cause of seizure reduction. Manual application of the magnet to initiate stimulation is not always practical because many patients are immobilized or unaware of their seizures, asleep or not in reach of the magnet. Algorithms based on changes in heart rate at or near the onset of the seizure provide a methodology for automated responsive stimulation. Because literature indicates additional benefits from on-demand magnet mode stimulation, a potential role exists for automatic activation of stimulation. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis

PubMed Central

Gajic, Dragoljub; Djurovic, Zeljko; Gligorijevic, Jovan; Di Gennaro, Stefano; Savic-Gajic, Ivana

2015-01-01

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved. PMID:25852534

Frequent Seizures Are Associated with a Network of Gray Matter Atrophy in Temporal Lobe Epilepsy with or without Hippocampal Sclerosis

PubMed Central

Coan, Ana C.; Campos, Brunno M.; Yasuda, Clarissa L.; Kubota, Bruno Y.; Bergo, Felipe PG.; Guerreiro, Carlos AM.; Cendes, Fernando

2014-01-01

Objective Patients with temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS) have diffuse subtle gray matter (GM) atrophy detectable by MRI quantification analyses. However, it is not clear whether the etiology and seizure frequency are associated with this atrophy. We aimed to evaluate the occurrence of GM atrophy and the influence of seizure frequency in patients with TLE and either normal MRI (TLE-NL) or MRI signs of HS (TLE-HS). Methods We evaluated a group of 172 consecutive patients with unilateral TLE-HS or TLE-NL as defined by hippocampal volumetry and signal quantification (122 TLE-HS and 50 TLE-NL) plus a group of 82 healthy individuals. Voxel-based morphometry was performed with VBM8/SPM8 in 3T MRIs. Patients with up to three complex partial seizures and no generalized tonic-clonic seizures in the previous year were considered to have infrequent seizures. Those who did not fulfill these criteria were considered to have frequent seizures. Results Patients with TLE-HS had more pronounced GM atrophy, including the ipsilateral mesial temporal structures, temporal lobe, bilateral thalami and pre/post-central gyri. Patients with TLE-NL had more subtle GM atrophy, including the ipsilateral orbitofrontal cortex, bilateral thalami and pre/post-central gyri. Both TLE-HS and TLE-NL showed increased GM volume in the contralateral pons. TLE-HS patients with frequent seizures had more pronounced GM atrophy in extra-temporal regions than TLE-HS with infrequent seizures. Patients with TLE-NL and infrequent seizures had no detectable GM atrophy. In both TLE-HS and TLE-NL, the duration of epilepsy correlated with GM atrophy in extra-hippocampal regions. Conclusion Although a diffuse network GM atrophy occurs in both TLE-HS and TLE-NL, this is strikingly more evident in TLE-HS and in patients with frequent seizures. These findings suggest that neocortical atrophy in TLE is related to the ongoing seizures and epilepsy duration, while thalamic atrophy is more probably related to the original epileptogenic process. PMID:24475055

Granger Causality Relationships between Local Field Potentials in an Animal Model of Temporal Lobe Epilepsy

PubMed Central

Cadotte, Alex J.; DeMarse, Thomas B.; Mareci, Thomas H.; Parekh, Mansi; Talathi, Sachin S.; Hwang, Dong-Uk; Ditto, William L.; Ding, Mingzhou; Carney, Paul R.

2010-01-01

An understanding of the in vivo spatial emergence of abnormal brain activity during spontaneous seizure onset is critical to future early seizure detection and closed-loop seizure prevention therapies. In this study, we use Granger causality (GC) to determine the strength and direction of relationships between local field potentials (LFPs) recorded from bilateral microelectrode arrays in an intermittent spontaneous seizure model of chronic temporal lobe epilepsy before, during, and after Racine grade partial onset generalized seizures. Our results indicate distinct patterns of directional GC relationships within the hippocampus, specifically from the CA1 subfield to the dentate gryus, prior to and during seizure onset. Our results suggest sequential and hierarchical temporal relationships between the CA1 and dentate gyrus within and across hippocampal hemispheres during seizure. Additionally, our analysis suggests a reversal in the direction of GC relationships during seizure, from an abnormal pattern to more anatomically expected pattern. This reversal correlates well with the observed behavioral transition from tonic to clonic seizure in time-locked video. These findings highlight the utility of GC to reveal dynamic directional temporal relationships between multichannel LFP recordings from multiple brain regions during unprovoked spontaneous seizures. PMID:20304005

Granger causality relationships between local field potentials in an animal model of temporal lobe epilepsy.

PubMed

Cadotte, Alex J; DeMarse, Thomas B; Mareci, Thomas H; Parekh, Mansi B; Talathi, Sachin S; Hwang, Dong-Uk; Ditto, William L; Ding, Mingzhou; Carney, Paul R

2010-05-30

An understanding of the in vivo spatial emergence of abnormal brain activity during spontaneous seizure onset is critical to future early seizure detection and closed-loop seizure prevention therapies. In this study, we use Granger causality (GC) to determine the strength and direction of relationships between local field potentials (LFPs) recorded from bilateral microelectrode arrays in an intermittent spontaneous seizure model of chronic temporal lobe epilepsy before, during, and after Racine grade partial onset generalized seizures. Our results indicate distinct patterns of directional GC relationships within the hippocampus, specifically from the CA1 subfield to the dentate gyrus, prior to and during seizure onset. Our results suggest sequential and hierarchical temporal relationships between the CA1 and dentate gyrus within and across hippocampal hemispheres during seizure. Additionally, our analysis suggests a reversal in the direction of GC relationships during seizure, from an abnormal pattern to more anatomically expected pattern. This reversal correlates well with the observed behavioral transition from tonic to clonic seizure in time-locked video. These findings highlight the utility of GC to reveal dynamic directional temporal relationships between multichannel LFP recordings from multiple brain regions during unprovoked spontaneous seizures. (c) 2010 Elsevier B.V. All rights reserved.

Optical changes in cortical tissue during seizure activity using optical coherence tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Ornelas, Danielle; Hasan, Md.; Gonzalez, Oscar; Krishnan, Giri; Szu, Jenny I.; Myers, Timothy; Hirota, Koji; Bazhenov, Maxim; Binder, Devin K.; Park, Boris H.

2017-02-01

Epilepsy is a chronic neurological disorder characterized by recurrent and unpredictable seizures. Electrophysiology has remained the gold standard of neural activity detection but its resolution and high susceptibility to noise and motion artifact limit its efficiency. Optical imaging techniques, including fMRI, intrinsic optical imaging, and diffuse optical imaging, have also been used to detect neural activity yet these techniques rely on the indirect measurement of changes in blood flow. A more direct optical imaging technique is optical coherence tomography (OCT), a label-free, high resolution, and minimally invasive imaging technique that can produce depth-resolved cross-sectional and 3D images. In this study, OCT was used to detect non-vascular depth-dependent optical changes in cortical tissue during 4-aminopyridine (4-AP) induced seizure onset. Calculations of localized optical attenuation coefficient (µ) allow for the assessment of depth-resolved volumetric optical changes in seizure induced cortical tissue. By utilizing the depth-dependency of the attenuation coefficient, we demonstrate the ability to locate and remove the optical effects of vasculature within the upper regions of the cortex on the attenuation calculations of cortical tissue in vivo. The results of this study reveal a significant depth-dependent decrease in attenuation coefficient of nonvascular cortical tissue both ex vivo and in vivo. Regions exhibiting decreased attenuation coefficient show significant temporal correlation to regions of increased electrical activity during seizure onset and progression. This study allows for a more thorough and biologically relevant analysis of the optical signature of seizure activity in vivo using OCT.

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

PubMed

Tsiouris, Κostas Μ; Pezoulas, Vasileios C; Zervakis, Michalis; Konitsiotis, Spiros; Koutsouris, Dimitrios D; Fotiadis, Dimitrios I

2018-05-17

The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an imminent seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic seizure prediction using EEG signals, expanding the use of deep learning algorithms with convolutional neural networks (CNN). A pre-analysis is initially performed to find the optimal architecture of the LSTM network by testing several modules and layers of memory units. Based on these results, a two-layer LSTM network is selected to evaluate seizure prediction performance using four different lengths of preictal windows, ranging from 15 min to 2 h. The LSTM model exploits a wide range of features extracted prior to classification, including time and frequency domain features, between EEG channels cross-correlation and graph theoretic features. The evaluation is performed using long-term EEG recordings from the open CHB-MIT Scalp EEG database, suggest that the proposed methodology is able to predict all 185 seizures, providing high rates of seizure prediction sensitivity and low false prediction rates (FPR) of 0.11-0.02 false alarms per hour, depending on the duration of the preictal window. The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

MANAGEMENT OF A REEVE'S MUNTJAC ( MUNTIACUS REEVESI) WITH SEIZURES USING LEVETIRACETAM.

PubMed

Blatt, Emily R; Seeley, Kathryn E; Lovett, Mathew C; Junge, Randall E

2017-12-01

This report describes the diagnosis and management of idiopathic epilepsy in a 4-yr-old intact female Reeve's muntjac ( Muntiacus reevesi). The patient was initially witnessed to have isolated paroxysmal events consistent with epileptic seizures (altered consciousness, lateral recumbency, tonic/clonic movement of limbs) lasting less than 3 min with an immediate return to normal consciousness. The seizure frequency increased to >3 seizures within 24 hr and phenobarbital 3 mg/kg orally every 12 hr was started. Because of continued epileptic seizures and low serum phenobarbital levels, the dose was increased until significant elevations of aspartate aminotransferase (AST) and alkaline phosphatase (ALP) were detected. Levetiracetam 40 mg/kg orally every 12 hr was initiated and the phenobarbital was weaned and discontinued. One breakthrough seizure has been witnessed in the 10 mo since starting levetiracetam.

Evaluation of Hospitalized Intractable Epileptic Children with SPECT Scan in Ahvaz, South West of Iran

PubMed Central

Ahmadi, Faramarz; Malekian, Arash; Davoodzadeh, Hannaneh; Kabirinia, Hossein

2016-01-01

Introduction Seizures are the most frequent neurologic disorder seen in childhood. Epilepsy is a group of disorders that includes an abnormally increased susceptibility to seizures. Aim To examine the effectiveness of SPECT (Single Photon Emission Computerized Tomography) in detecting seizure foci in 21 Iranian children who had medically refractory epilepsy. Materials and Methods Children between 2 to 15 years of age with uncontrolled seizures were investigated using SPECT scan as a standardized protocol. Results In 16 cases (76.2%), likely seizure foci were evident, as were seen in the form of decreased regional blood flow, while in 5 cases (23.8%), SPECT scan results were normal. Left temporal lobe was the most common area which had decreased regional blood flow. Conclusion SPECT scan can potentially be used to investigate children with uncontrolled seizures. PMID:27891419

[Role of video electroencephalogram in diagnosis and localization of epilepsy in children].

PubMed

Yang, Xiao-Yan; Long, Li-Li; Xiao, Bo

2016-10-01

To study the role of video electroencephalogram (VEEG) versus regular electroencephalogram (REEG) in the diagnosis of epilepsy and localization of origin of epileptic discharge in children through a comparative analysis. A retrospective analysis was performed for the clinical data of 223 children with clinical paroxysmal symptoms in the past and suspected epilepsy. VEEG and REEG were compared from the aspects of monitoring of clinical seizures, interictal epileptiform discharge (IED), localization of the origin of IED, and identification of non-epileptic seizures, and the detection rate of IED during awakening and sleep stages was also compared. Compared with REEG, VEEG had significantly higher detection rates of IED and synchronous clinical seizures in children with epileptiform discharge (P<0.01). Of all children, 86 were diagnosed with epilepsy, 78 were diagnosed with epilepsy syndrome, 31 were diagnosed with non-epileptic seizures, and 81 had a definite location of the origin of epileptic discharge according to the VEEG. The detection rate of IED in the sleep stage was higher than that in the awakening stage (46% vs 13.2%; P<0.01), and IED was mainly detected in the NREM I-II stages according to the VEEG. VEEG has a significantly better performance than REEG in the diagnosis and localization of epilepsy in children and has a high value in clinical practice.

Oxygen desaturations triggered by partial seizures: implications for cardiopulmonary instability in epilepsy

NASA Technical Reports Server (NTRS)

Blum, A. S.; Ives, J. R.; Goldberger, A. L.; Al-Aweel, I. C.; Krishnamurthy, K. B.; Drislane, F. W.; Schomer, D. L.

2000-01-01

PURPOSE: The occurrence of hypoxemia in adults with partial seizures has not been systematically explored. Our aim was to study in detail the temporal dynamics of this specific type of ictal-associated hypoxemia. METHODS: During long-term video/EEG monitoring (LTM), patients underwent monitoring of oxygen saturation using a digital Spo2 (pulse oximeter) transducer. Six patients (nine seizures) were identified with oxygen desaturations after the onset of partial seizure activity. RESULTS: Complex partial seizures originated from both left and right temporal lobes. Mean seizure duration (+/-SD) was 73 +/- 18 s. Mean Spo2 desaturation duration was 76 +/- 19 s. The onset of oxygen desaturation followed seizure onset with a mean delay of 43 +/- 16 s. Mean (+/-SD) Spo2 nadir was 83 +/- 5% (range, 77-91%), occurring an average of 35 +/- 12 s after the onset of the desaturation. One seizure was associated with prolonged and recurrent Spo2 desaturations. CONCLUSIONS: Partial seizures may be associated with prominent oxygen desaturations. The comparable duration of each seizure and its subsequent desaturation suggests a close mechanistic (possibly causal) relation. Spo2 monitoring provides an added means for seizure detection that may increase LTM yield. These observations also raise the possibility that ictal ventilatory dysfunction could play a role in certain cases of sudden unexpected death in epilepsy in adults with partial seizures.

Neonatal Seizures: Advances in Mechanisms and Management

PubMed Central

Glass, Hannah C.

2013-01-01

Synopsis Seizures occur in approximately 1–5 per 1,000 live births, and are among the most common neurologic conditions managed by a neonatal neurocritical care service. There are several, age-specific factors that are particular to the developing brain, which influence excitability and seizure generation, response to medications, and impact of seizures on brain structure and function. Neonatal seizures are often associated with serious underlying brain injury such as hypoxia-ischemia, stroke or hemorrhage. Conventional, prolonged, continuous video-electroencephalogram (cEEG) is the gold standard for detecting seizures, whereas amplitude-integrated EEG (aEEG) is a convenient and useful bedside tool. Evaluation of neonatal seizures involves a thorough search for the etiology of the seizures, and includes detailed clinical history, routine chemistries, neuroimaging (and preferably magnetic resonance imaging, MRI), and specialized testing such as screening for inborn errors of metabolism if no structural cause is identified and seizures persist after correction of transient metabolic deficits. Expert opinion supports rapid medical treatment to abolish electrographic seizures, however the relative risk versus benefit for aggressive medical treatment of neonatal seizures is not known. While there is increasing evidence to support a harmful effect of seizures on the developing brain, there is also evidence that commonly used medications are potentially neurotoxic in animal models. Newer agents appear less harmful, but data are lacking regarding optimal dosing and efficacy. PMID:24524454

A screening questionnaire for convulsive seizures: A three-stage field-validation in rural Bolivia.

PubMed

Giuliano, Loretta; Cicero, Calogero Edoardo; Crespo Gómez, Elizabeth Blanca; Padilla, Sandra; Bruno, Elisa; Camargo, Mario; Marin, Benoit; Sofia, Vito; Preux, Pierre-Marie; Strohmeyer, Marianne; Bartoloni, Alessandro; Nicoletti, Alessandra

2017-01-01

Epilepsy is one of the most common neurological diseases in Latin American Countries (LAC) and epilepsy associated with convulsive seizures is the most frequent type. Therefore, the detection of convulsive seizures is a priority, but a validated Spanish-language screening tool to detect convulsive seizures is not available. We performed a field validation to evaluate the accuracy of a Spanish-language questionnaire to detect convulsive seizures in rural Bolivia using a three-stage design. The questionnaire was also administered face-to-face, using a two-stage design, to evaluate the difference in accuracy. The study was carried out in the rural communities of the Gran Chaco region. The questionnaire consists of a single screening question directed toward the householders and a confirmatory section administered face-to-face to the index case. Positive subjects underwent a neurological examination to detect false positive and true positive subjects. To estimate the proportion of false negative, a random sample of about 20% of the screened negative underwent a neurological evaluation. 792 householders have been interviewed representing a population of 3,562 subjects (52.2% men; mean age 24.5 ± 19.7 years). We found a sensitivity of 76.3% (95% CI 59.8-88.6) with a specificity of 99.6% (95% CI 99.4-99.8). The two-stage design showed only a slightly higher sensitivity respect to the three-stage design. Our screening tool shows a good accuracy and can be easily used by trained health workers to quickly screen the population of the rural communities of LAC through the householders using a three-stage design.

Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats.

PubMed

Ouyang, Gaoxiang; Li, Xiaoli; Dang, Chuangyin; Richards, Douglas A

2008-08-01

Understanding the transition of brain activity towards an absence seizure is a challenging task. In this paper, we use recurrence quantification analysis to indicate the deterministic dynamics of EEG series at the seizure-free, pre-seizure and seizure states in genetic absence epilepsy rats. The determinism measure, DET, based on recurrence plot, was applied to analyse these three EEG datasets, each dataset containing 300 single-channel EEG epochs of 5-s duration. Then, statistical analysis of the DET values in each dataset was carried out to determine whether their distributions over the three groups were significantly different. Furthermore, a surrogate technique was applied to calculate the significance level of determinism measures in EEG recordings. The mean (+/-SD) DET of EEG was 0.177+/-0.045 in pre-seizure intervals. The DET values of pre-seizure EEG data are significantly higher than those of seizure-free intervals, 0.123+/-0.023, (P<0.01), but lower than those of seizure intervals, 0.392+/-0.110, (P<0.01). Using surrogate data methods, the significance of determinism in EEG epochs was present in 25 of 300 (8.3%), 181 of 300 (60.3%) and 289 of 300 (96.3%) in seizure-free, pre-seizure and seizure intervals, respectively. Results provide some first indications that EEG epochs during pre-seizure intervals exhibit a higher degree of determinism than seizure-free EEG epochs, but lower than those in seizure EEG epochs in absence epilepsy. The proposed methods have the potential of detecting the transition between normal brain activity and the absence seizure state, thus opening up the possibility of intervention, whether electrical or pharmacological, to prevent the oncoming seizure.

Automatic Vagus Nerve Stimulation Triggered by Ictal Tachycardia: Clinical Outcomes and Device Performance--The U.S. E-37 Trial.

PubMed

Fisher, Robert S; Afra, Pegah; Macken, Micheal; Minecan, Daniela N; Bagić, Anto; Benbadis, Selim R; Helmers, Sandra L; Sinha, Saurabh R; Slater, Jeremy; Treiman, David; Begnaud, Jason; Raman, Pradheep; Najimipour, Bita

2016-02-01

The Automatic Stimulation Mode (AutoStim) feature of the Model 106 Vagus Nerve Stimulation (VNS) Therapy System stimulates the left vagus nerve on detecting tachycardia. This study evaluates performance, safety of the AutoStim feature during a 3-5-day Epilepsy Monitoring Unit (EMU) stay and long- term clinical outcomes of the device stimulating in all modes. The E-37 protocol (NCT01846741) was a prospective, unblinded, U.S. multisite study of the AspireSR(®) in subjects with drug-resistant partial onset seizures and history of ictal tachycardia. VNS Normal and Magnet Modes stimulation were present at all times except during the EMU stay. Outpatient visits at 3, 6, and 12 months tracked seizure frequency, severity, quality of life, and adverse events. Twenty implanted subjects (ages 21-69) experienced 89 seizures in the EMU. 28/38 (73.7%) of complex partial and secondarily generalized seizures exhibited ≥20% increase in heart rate change. 31/89 (34.8%) of seizures were treated by Automatic Stimulation on detection; 19/31 (61.3%) seizures ended during the stimulation with a median time from stimulation onset to seizure end of 35 sec. Mean duty cycle at six-months increased from 11% to 16%. At 12 months, quality of life and seizure severity scores improved, and responder rate was 50%. Common adverse events were dysphonia (n = 7), convulsion (n = 6), and oropharyngeal pain (n = 3). The Model 106 performed as intended in the study population, was well tolerated and associated with clinical improvement from baseline. The study design did not allow determination of which factors were responsible for improvements. © 2015 The Authors. Neuromodulation: Technology at the Neural Interface published by Wiley Periodicals, Inc. on behalf of International Neuromodulation Society.

Long-term monitoring of cardiorespiratory patterns in drug-resistant epilepsy.

PubMed

Goldenholz, Daniel M; Kuhn, Amanda; Austermuehle, Alison; Bachler, Martin; Mayer, Christopher; Wassertheurer, Siegfried; Inati, Sara K; Theodore, William H

2017-01-01

Sudden unexplained death in epilepsy (SUDEP) during inpatient electroencephalography (EEG) monitoring has been a rare but potentially preventable event, with associated cardiopulmonary markers. To date, no systematic evaluation of alarm settings for a continuous pulse oximeter (SpO 2 ) has been performed. In addition, evaluation of the interrelationship between the ictal and interictal states for cardiopulmonary measures has not been reported. Patients with epilepsy were monitored using video-EEG, SpO 2 , and electrocardiography (ECG). Alarm thresholds were tested systematically, balancing the number of false alarms with true seizure detections. Additional cardiopulmonary patterns were explored using automated ECG analysis software. One hundred ninety-three seizures (32 generalized) were evaluated from 45 patients (7,104 h recorded). Alarm thresholds of 80-86% SpO 2 detected 63-73% of all generalized convulsions and 20-28% of all focal seizures (81-94% of generalized and 25-36% of focal seizures when considering only evaluable data). These same thresholds resulted in 25-146 min between false alarms. The sequential probability of ictal SpO 2 revealed a potential common seizure termination pathway of desaturation. A statistical model of corrected QT intervals (QTc), heart rate (HR), and SpO 2 revealed close cardiopulmonary coupling ictally. Joint probability maps of QTc and SpO 2 demonstrated that many patients had baseline dysfunction in either cardiac, pulmonary, or both domains, and that ictally there was dissociation-some patients exhibited further dysfunction in one or both domains. Optimal selection of continuous pulse oximetry thresholds involves a tradeoff between seizure detection accuracy and false alarm frequency. Alarming at 86% for patients that tend to have fewer false alarms and at 80% for those who have more, would likely result in a reasonable tradeoff. The cardiopulmonary findings may lead to SUDEP biomarkers and early seizure termination therapies. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

PubMed

Lie, Octavian V; van Mierlo, Pieter

2017-01-01

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

Transient suppression of seizures by repetitive transcranial magnetic stimulation in a case of Rasmussen's encephalitis.

PubMed

Rotenberg, Alexander; Depositario-Cabacar, Dewi; Bae, Erica Hyunji; Harini, Chellamani; Pascual-Leone, Alvaro; Takeoka, Masanori

2008-07-01

Repetitive transcranial magnetic stimulation (rTMS) has been applied with variable success to terminate the seizures of epilepsia partialis continua. The rationale for using this technique to suppress ongoing seizures is the capacity of rTMS to interrupt ongoing neuronal activity, and to produce a lasting decrease in cortical excitability with low-frequency (1 Hz) stimulation. We report a case of epilepsia partialis continua in a child with Rasmussen's encephalitis, in whom seizures were transiently suppressed by 1-Hz rTMS delivered in nine daily 30-minute sessions. In this case, total ictal time was significantly reduced during stimulation, but the daily baseline seizure rate remained unchanged. Notably, the detection and quantification of this short-lived improvement were enabled by recording EEG continuously during the rTMS session. Thus, we present this case to illustrate a potential utility of combined continuous EEG recording and rTMS in seizure treatment.

Levetiracetam for Treatment of Neonatal Seizures

PubMed Central

Abend, Nicholas S.; Gutierrez-Colina, Ana M.; Monk, Heather M.; Dlugos, Dennis J.; Clancy, Robert R.

2011-01-01

Neonatal seizures are often refractory to treatment with initial antiseizure medications. Consequently, clinicians turn to alternatives such as levetiracetam, despite the lack of published data regarding its safety, tolerability, or efficacy in the neonatal population. We report a retrospectively identified cohort of 23 neonates with electroencephalographically confirmed seizures who received levetiracetam. Levetiracetam was considered effective if administration was associated with a greater than 50% seizure reduction within 24 hours. Levetiracetam was initiated at a mean conceptional age of 41 weeks. The mean initial dose was 16 ± 6 mg/kg and the mean maximum dose was 45 ± 19 mg/kg/day. No respiratory or cardiovascular adverse effects were reported or detected. Levetiracetam was associated with a greater than 50% seizure reduction in 35% (8 of 23), including seizure termination in 7. Further study is warranted to determine optimal levetiracetam dosing in neonates and to compare efficacy with other antiseizure medications. PMID:21233461

Principal dynamic mode analysis of neural mass model for the identification of epileptic states

NASA Astrophysics Data System (ADS)

Cao, Yuzhen; Jin, Liu; Su, Fei; Wang, Jiang; Deng, Bin

2016-11-01

The detection of epileptic seizures in Electroencephalography (EEG) signals is significant for the diagnosis and treatment of epilepsy. In this paper, in order to obtain characteristics of various epileptiform EEGs that may differentiate different states of epilepsy, the concept of Principal Dynamic Modes (PDMs) was incorporated to an autoregressive model framework. First, the neural mass model was used to simulate the required intracerebral EEG signals of various epileptiform activities. Then, the PDMs estimated from the nonlinear autoregressive Volterra models, as well as the corresponding Associated Nonlinear Functions (ANFs), were used for the modeling of epileptic EEGs. The efficient PDM modeling approach provided physiological interpretation of the system. Results revealed that the ANFs of the 1st and 2nd PDMs for the auto-regressive input exhibited evident differences among different states of epilepsy, where the ANFs of the sustained spikes' activity encountered at seizure onset or during a seizure were the most differentiable from that of the normal state. Therefore, the ANFs may be characteristics for the classification of normal and seizure states in the clinical detection of seizures and thus provide assistance for the diagnosis of epilepsy.

Identifying CNVs in 15q11q13 and 16p11.2 of Patients with Seizures Increases the Rates of Detecting Pathogenic Changes

PubMed Central

Vianna, Gabrielle S.; Freitas, Mariana L.; Oliveira, Valdirene T.de; Pietra, Rafaella X.; Gonçalves, Michele da S.; Rocha, Patrícia P.O.; Monteiro, Rejane A.C.; Ferreira, Luana C.A.; Xavier, Rosana R.; Carvalho, Andréia M.; Lima, Patrícia R. de M.; Monteiro, Maria Augusta N.P.; Mateo, Elvis C.; Giannetti, Juliana G.; César, Giovana da C.; Lima, Joziele de S.; Medeiros, Paula F.V.; Jehee, Fernanda S.

2016-01-01

Chromosomal changes are frequently observed in patients with syndromic seizures. Understanding the genetic etiology of this pathology is crucial for the guidance and genetic counseling of families as well as for the establishment of appropriate treatment. A combination of MLPA kits was used to identify pathogenic CNVs in a group of 70 syndromic patients with seizures. Initially, a screening was performed for subtelomeric changes (MLPA P036 and P070 kits) and for the regions most frequently related to microdeletion/microduplication syndromes (MLPA P064). Subsequently, the MLPA P343 was used to identify alterations in the 15q11q13, 16p11.2, and 22q13 regions. Screening with MLPA P343 allowed a 10-15.7% increase in the detection rate of CNVs reinforcing the importance of investigating changes in 15q11q13 and 16p11.2 in syndromic patients with seizures. We also demonstrated that the MLPA technique is an alternative with a great diagnostic potential, and we proposed its use as part of the initial assessment of syndromic patients with seizures. PMID:27920636

Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

PubMed

Fergus, Paul; Hignett, David; Hussain, Abir; Al-Jumeily, Dhiya; Abdel-Aziz, Khaled

2015-01-01

The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier. We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders.

Effects of hypoxia-induced neonatal seizures on acute hippocampal injury and later-life seizure susceptibility and anxiety-related behavior in mice.

PubMed

Rodriguez-Alvarez, Natalia; Jimenez-Mateos, Eva M; Dunleavy, Mark; Waddington, John L; Boylan, Geraldine B; Henshall, David C

2015-11-01

Seizures are common during the neonatal period, often due to hypoxic-ischemic encephalopathy and may contribute to acute brain injury and the subsequent development of cognitive deficits and childhood epilepsy. Here we explored short- and long-term consequences of neonatal hypoxia-induced seizures in 7 day old C57BL/6J mice. Seizure activity, molecular markers of hypoxia and histological injury were investigated acutely after hypoxia and response to chemoconvulsants and animal behaviour was explored at adulthood. Hypoxia was induced by exposing pups to 5% oxygen for 15 min (global hypoxia). Electrographically defined seizures with behavioral correlates occurred in 95% of these animals and seizures persisted for many minutes after restitution of normoxia. There was minimal morbidity or mortality. Pre- or post-hypoxia injection of phenobarbital (50mg/kg) had limited efficacy at suppressing seizures. The hippocampus from neonatal hypoxia-seizure mice displayed increased expression of vascular endothelial growth factor and the immediate early gene c-fos, minimal histological evidence of cell injury and activation of caspase-3 in scattered neurons. Behavioral analysis of mice five weeks after hypoxia-induced seizures detected novel anxiety-related and other behaviors, while performance in a spatial memory test was similar to controls. Seizure threshold tests with kainic acid at six weeks revealed that mice previously subject to neonatal hypoxia-induced seizures developed earlier, more frequent and longer-duration seizures. This study defines a set of electro-clinical, molecular, pharmacological and behavioral consequences of hypoxia-induced seizures that indicate short- and long-term deleterious outcomes and may be a useful model to investigate the pathophysiology and treatment of neonatal seizures in humans. Copyright © 2015 Elsevier Inc. All rights reserved.

Silent hippocampal seizures and spikes identified by foramen ovale electrodes in Alzheimer's disease.

PubMed

Lam, Alice D; Deck, Gina; Goldman, Alica; Eskandar, Emad N; Noebels, Jeffrey; Cole, Andrew J

2017-06-01

We directly assessed mesial temporal activity using intracranial foramen ovale electrodes in two patients with Alzheimer's disease (AD) without a history or EEG evidence of seizures. We detected clinically silent hippocampal seizures and epileptiform spikes during sleep, a period when these abnormalities were most likely to interfere with memory consolidation. The findings in these index cases support a model in which early development of occult hippocampal hyperexcitability may contribute to the pathogenesis of AD.

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone.

PubMed

Schindler, Kaspar; Rummel, Christian; Andrzejak, Ralph G; Goodfellow, Marc; Zubler, Frédéric; Abela, Eugenio; Wiest, Roland; Pollo, Claudio; Steimer, Andreas; Gast, Heidemarie

2016-09-01

To show that time-irreversible EEG signals recorded with intracranial electrodes during seizures can serve as markers of the epileptogenic zone. We use the recently developed method of mapping time series into directed horizontal graphs (dHVG). Each node of the dHVG represents a time point in the original intracranial EEG (iEEG) signal. Statistically significant differences between the distributions of the nodes' number of input and output connections are used to detect time-irreversible iEEG signals. In 31 of 32 seizure recordings we found time-irreversible iEEG signals. The maximally time-irreversible signals always occurred during seizures, with highest probability in the middle of the first seizure half. These signals spanned a large range of frequencies and amplitudes but were all characterized by saw-tooth like shaped components. Brain regions removed from patients who became post-surgically seizure-free generated significantly larger time-irreversibilities than regions removed from patients who still had seizures after surgery. Our results corroborate that ictal time-irreversible iEEG signals can indeed serve as markers of the epileptogenic zone and can be efficiently detected and quantified in a time-resolved manner by dHVG based methods. Ictal time-irreversible EEG signals can help to improve pre-surgical evaluation in patients suffering from pharmaco-resistant epilepsies. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Non-convulsive seizures and non-convulsive status epilepticus monitoring in the intensive care unit. A real need for the Gulf Cooperation Council countries.

PubMed

Mesraoua, Boulenouar; Wieser, Heinz G

2009-10-01

Continuous EEG (cEEG) monitoring in the intensive care unit (ICU) is essential for detecting non-convulsive seizures/status epilepticus (NCSs, NCSE). Currently there exist a number of continuous EEG monitoring systems adapted for use in the ICU. However, these systems have been trained using EEG data collected from healthy, neurologically intact patients with epileptic seizures, a very different patient population from ICU patients. The review consists of 2 parts, clinical and technological aspects. In the first one, we summarize the electroencephalographic aspects of NCSs/NCSE and other EEG patterns encountered in the ICU. In the second part, we explain how to develop a novel cEEG monitoring system to be used in Hamad Medical Corporation ICUs, Doha, Qatar, that is able to detect pathological EEG patterns commonly occurring in the critically ill patient. Real-time monitoring of seizure discharges, and other pathological EEG patterns will allow correct diagnosis and adequate treatment in a timely fashion.

Non-intrusive head movement analysis of videotaped seizures of epileptic origin.

PubMed

Mandal, Bappaditya; Eng, How-Lung; Lu, Haiping; Chan, Derrick W S; Ng, Yen-Ling

2012-01-01

In this work we propose a non-intrusive video analytic system for patient's body parts movement analysis in Epilepsy Monitoring Unit. The system utilizes skin color modeling, head/face pose template matching and face detection to analyze and quantify the head movements. Epileptic patients' heads are analyzed holistically to infer seizure and normal random movements. The patient does not require to wear any special clothing, markers or sensors, hence it is totally non-intrusive. The user initializes the person-specific skin color and selects few face/head poses in the initial few frames. The system then tracks the head/face and extracts spatio-temporal features. Support vector machines are then used on these features to classify seizure-like movements from normal random movements. Experiments are performed on numerous long hour video sequences captured in an Epilepsy Monitoring Unit at a local hospital. The results demonstrate the feasibility of the proposed system in pediatric epilepsy monitoring and seizure detection.

Sleep-dependent memory consolidation in the epilepsy monitoring unit: A pilot study.

PubMed

Sarkis, Rani A; Alam, Javad; Pavlova, Milena K; Dworetzky, Barbara A; Pennell, Page B; Stickgold, Robert; Bubrick, Ellen J

2016-08-01

We sought to examine whether patients with focal epilepsy exhibit sleep dependent memory consolidation, whether memory retention rates correlated with particular aspects of sleep physiology, and how the process was affected by seizures. We prospectively recruited patients with focal epilepsy and assessed declarative memory using a task consisting of 15 pairs of colored pictures on a 5×6 grid. Patients were tested 12h after training, once after 12h of wakefulness and once after 12h that included sleep. EMG chin electrodes were placed to enable sleep scoring. The number and density of sleep spindles were assessed using a wavelet-based algorithm. Eleven patients were analyzed age 21-56years. The percentage memory retention over 12h of wakefulness was 62.7% and over 12h which included sleep 83.6% (p=0.04). Performance on overnight testing correlated with the duration of slow wave sleep (SWS) (r=+0.63, p<0.05). Three patients had seizures during the day, and 3 had nocturnal seizures. Day-time seizures did not affect retention rates, while those patients who had night time seizures had a drop in retention from an average of 92% to 60.5%. There is evidence of sleep dependent memory consolidation in patients with epilepsy which mostly correlates with the amount of SWS. Our preliminary findings suggest that nocturnal seizures likely disrupt sleep dependent memory consolidation. Findings highlight the importance of SWS in sleep dependent memory consolidation and the adverse impact of nocturnal seizures on this process. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Sleep-dependent Memory Consolidation in the Epilepsy Monitoring Unit: a Pilot Study

PubMed Central

Sarkis, Rani A.; Alam, Javad; Pavlova, Milena K.; Dworetzky, Barbara A.; Pennell, Page B.; Stickgold, Robert; Bubrick, Ellen J.

2018-01-01

Objective We sought to examine whether patients with focal epilepsy exhibit sleep dependent memory consolidation, whether memory retention rates correlated with particular aspects of sleep physiology, and how the process was affected by seizures. Methods We prospectively recruited patients with focal epilepsy and assessed declarative memory using a task consisting of 15 pairs of colored pictures on a 5 × 6 grid. Patients were tested 12 hours after training, once after 12 hours of wakefulness and once after 12 hours that included sleep. EMG chin electrodes were placed to enable sleep scoring. The number and density of sleep spindles were assessed using a wavelet-based algorithm. Results Eleven patients were analyzed age 21–56 years. The percentage memory retention over 12 hours of wakefulness was 62.7% % and over 12 hours which included sleep 83.6 % (p = 0.04). Performance on overnight testing correlated with the duration of slow wave sleep (SWS) (r=+0.63, p <0.05). Three patients had seizures during the day, and another 3 had nocturnal seizures. Day-time seizures did not affect retention rates, while those patients who had night time seizures had a drop in retention from an average of 92% to 60.5%. Conclusions There is evidence of sleep dependent memory consolidation in patients with epilepsy which mostly correlates with the amount of SWS. Our preliminary findings suggest that nocturnal seizures likely disrupt sleep dependent memory consolidation. Significance Findings highlight the importance of SWS in sleep dependent memory consolidation and the adverse impact of nocturnal seizures on this process. PMID:27417054

Random ensemble learning for EEG classification.

PubMed

Hosseini, Mohammad-Parsa; Pompili, Dario; Elisevich, Kost; Soltanian-Zadeh, Hamid

2018-01-01

Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein informative components of electroencephalogram (EEG)-derived data are extracted and an automatic method is presented using infinite independent component analysis (I-ICA) to select independent features. The feature space is divided into subspaces via random selection and multichannel support vector machines (SVMs) are used to classify these subspaces. The result of each classifier is then combined by majority voting to establish the final output. In addition, a random subspace ensemble using a combination of SVM, multilayer perceptron (MLP) neural network and an extended k-nearest neighbors (k-NN), called extended nearest neighbor (ENN), is developed for the EEG and electrocorticography (ECoG) big data problem. To evaluate the solution, a benchmark ECoG of eight patients with temporal and extratemporal epilepsy was implemented in a distributed computing framework as a multitier cloud-computing architecture. Using leave-one-out cross-validation, the accuracy, sensitivity, specificity, and both false positive and false negative ratios of the proposed method were found to be 0.97, 0.98, 0.96, 0.04, and 0.02, respectively. Application of the solution to cases under investigation with ECoG has also been effected to demonstrate its utility. Copyright © 2017 Elsevier B.V. All rights reserved.

Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors.

PubMed

Onorati, Francesco; Regalia, Giulia; Caborni, Chiara; Migliorini, Matteo; Bender, Daniel; Poh, Ming-Zher; Frazier, Cherise; Kovitch Thropp, Eliana; Mynatt, Elizabeth D; Bidwell, Jonathan; Mai, Roberto; LaFrance, W Curt; Blum, Andrew S; Friedman, Daniel; Loddenkemper, Tobias; Mohammadpour-Touserkani, Fatemeh; Reinsberger, Claus; Tognetti, Simone; Picard, Rosalind W

2017-11-01

New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors. Hand-annotated video-electroencephalographic seizure events were collected from 69 patients at six clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 h of data, including 55 convulsive epileptic seizures (six focal tonic-clonic seizures and 49 focal to bilateral tonic-clonic seizures) from 22 patients. Recordings were analyzed offline to train and test two new machine learning classifiers and a published classifier based on EDA and ACM. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses. The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and an FAR of 0.2 events/day. No nocturnal seizures were missed. Most patients had <1 false alarm every 4 days, with an FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures), the FAR is up to 13 times lower than with the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median = 29.3 s, range = 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of postictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures. The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behavior and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Gamma knife treatment for refractory epilepsy in seizure focus localized by positron emission tomography/CT★

PubMed Central

Bai, Xia; Wang, Xuemei; Wang, Hongwei; Zhao, Shigang; Han, Xiaodong; Hao, Linjun; Wang, Xiangcheng

2012-01-01

A total of 80 patients with refractory epilepsy were recruited from the Inner Mongolia Medical College Affiliated Hospital. The foci of 60% of the patients could be positioned using a combined positron emission tomography/CT imaging modality. Hyper- and hypometabolism foci were examined as part of this study. Patients who had abnormal metabolism in positron emission tomography/CT imaging were divided into intermittent-phase group and the seizure-phase group. The intermittent-phase group was further divided into a single-focus group and a multiple-foci group according to the number of seizure foci detected by imaging. Following gamma knife treatment, seizure frequency was significantly lower in the intermittent-phase group and the seizure-phase group. Wieser’s classification reached Grade I or II in nearly 40% of patients. Seizure frequency was significantly lower following treatment, but Wieser’s classification score was significantly higher in the seizure-phase group compared with the intermittent-phase group. Seizure frequency was significantly lower following treatment in the single-focus group, but Wieser’s classification score was significantly higher in the single-focus group as compared with the multiple-foci group. PMID:25317147

Epileptic Seizures Prediction Using Machine Learning Methods

PubMed Central

Usman, Syed Muhammad

2017-01-01

Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures' sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects. PMID:29410700

Hippocampal effective synchronization values are not pre-seizure indicator without considering the state of the onset channels

PubMed Central

Shayegh, Farzaneh; Sadri, Saeed; Amirfattahi, Rassoul; Ansari-Asl, Karim; Bellanger, Jean-Jacques; Senhadji, Lotfi

2014-01-01

In this paper, a model-based approach is presented to quantify the effective synchrony between hippocampal areas from depth-EEG signals. This approach is based on the parameter identification procedure of a realistic Multi-Source/Multi-Channel (MSMC) hippocampal model that simulates the function of different areas of hippocampus. In the model it is supposed that the observed signals recorded using intracranial electrodes are generated by some hidden neuronal sources, according to some parameters. An algorithm is proposed to extract the intrinsic (solely relative to one hippocampal area) and extrinsic (coupling coefficients between two areas) model parameters, simultaneously, by a Maximum Likelihood (ML) method. Coupling coefficients are considered as the measure of effective synchronization. This work can be considered as an application of Dynamic Causal Modeling (DCM) that enables us to understand effective synchronization changes during transition from inter-ictal to pre -ictal state. The algorithm is first validated by using some synthetic datasets. Then by extracting the coupling coefficients of real depth-EEG signals by the proposed approach, it is observed that the coupling values show no significant difference between ictal, pre-ictal and inter-ictal states, i.e., either the increase or decrease of coupling coefficients has been observed in all states. However, taking the value of intrinsic parameters into account, pre-seizure state can be distinguished from inter-ictal state. It is claimed that seizures start to appear when there are seizure-related physiological parameters on the onset channel, and its coupling coefficient toward other channels increases simultaneously. As a result of considering both intrinsic and extrinsic parameters as the feature vector, inter-ictal, pre-ictal and ictal activities are discriminated from each other with an accuracy of 91.33% accuracy. PMID:25061815

Nonseizure SUDEP: Sudden unexpected death in epilepsy without preceding epileptic seizures.

PubMed

Lhatoo, Samden D; Nei, Maromi; Raghavan, Manoj; Sperling, Michael; Zonjy, Bilal; Lacuey, Nuria; Devinsky, Orrin

2016-07-01

To describe the phenomenology of monitored sudden unexpected death in epilepsy (SUDEP) occurring in the interictal period where death occurs without a seizure preceding it. We report a case series of monitored definite and probable SUDEP where no electroclinical evidence of underlying seizures was found preceding death. Three patients (two definite and one probable) had SUDEP. They had a typical high SUDEP risk profile with longstanding intractable epilepsy and frequent generalized tonic-clonic seizures (GTCS). All patients had varying patterns of respiratory and bradyarrhythmic cardiac dysfunction with profound electroencephalography (EEG) suppression. In two patients, patterns of cardiorespiratory failure were similar to those seen in some patients in the Mortality in Epilepsy Monitoring Units Study (MORTEMUS). SUDEP almost always occur postictally, after GTCS and less commonly after a partial seizure. Monitored SUDEP or near-SUDEP cases without a seizure have not yet been reported in literature. When nonmonitored SUDEP occurs in an ambulatory setting without an overt seizure, the absence of EEG information prevents the exclusion of a subtle seizure. These cases confirm the existence of nonseizure SUDEP; such deaths may not be prevented by seizure detection-based devices. SUDEP risk in patients with epilepsy may constitute a spectrum of susceptibility wherein some are relatively immune, death occurs in others with frequent GTCS with one episode of seizure ultimately proving fatal, while in others still, death may occur even in the absence of a seizure. We emphasize the heterogeneity of SUDEP phenomena. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

27 CFR 555.186 - Seizure or forfeiture.

Code of Federal Regulations, 2013 CFR

2013-04-01

... EXPLOSIVES, DEPARTMENT OF JUSTICE EXPLOSIVES COMMERCE IN EXPLOSIVES Marking of Plastic Explosives § 555.186 Seizure or forfeiture. Any plastic explosive that does not contain a detection agent in violation of 18 U... of this chapter for regulations on summary destruction of plastic explosives that do not contain a...

27 CFR 555.186 - Seizure or forfeiture.

Code of Federal Regulations, 2014 CFR

2014-04-01

... EXPLOSIVES, DEPARTMENT OF JUSTICE EXPLOSIVES COMMERCE IN EXPLOSIVES Marking of Plastic Explosives § 555.186 Seizure or forfeiture. Any plastic explosive that does not contain a detection agent in violation of 18 U... of this chapter for regulations on summary destruction of plastic explosives that do not contain a...

27 CFR 555.186 - Seizure or forfeiture.

Code of Federal Regulations, 2012 CFR

2012-04-01

... EXPLOSIVES, DEPARTMENT OF JUSTICE EXPLOSIVES COMMERCE IN EXPLOSIVES Marking of Plastic Explosives § 555.186 Seizure or forfeiture. Any plastic explosive that does not contain a detection agent in violation of 18 U... of this chapter for regulations on summary destruction of plastic explosives that do not contain a...

27 CFR 555.186 - Seizure or forfeiture.

Code of Federal Regulations, 2011 CFR

2011-04-01

... EXPLOSIVES, DEPARTMENT OF JUSTICE EXPLOSIVES COMMERCE IN EXPLOSIVES Marking of Plastic Explosives § 555.186 Seizure or forfeiture. Any plastic explosive that does not contain a detection agent in violation of 18 U... of this chapter for regulations on summary destruction of plastic explosives that do not contain a...

27 CFR 555.186 - Seizure or forfeiture.

Code of Federal Regulations, 2010 CFR

2010-04-01

... EXPLOSIVES, DEPARTMENT OF JUSTICE EXPLOSIVES COMMERCE IN EXPLOSIVES Marking of Plastic Explosives § 555.186 Seizure or forfeiture. Any plastic explosive that does not contain a detection agent in violation of 18 U... of this chapter for regulations on summary destruction of plastic explosives that do not contain a...

The Inhibitory Effects of Npas4 on Seizures in Pilocarpine-Induced Epileptic Rats

PubMed Central

Guo, Jiamei; Yang, Guang; Long, Xianghua; Hu, Rong; Shen, Wenjing; Wang, Xuefeng; Zeng, Kebin

2014-01-01

To explore the effects of neuronal Per-Arnt-Sim domain protein 4 (Npas4) on seizures in pilocarpine-induced epileptic rats, Npas4 expression was detected by double-label immunofluorescence, immunohistochemistry, and Western blotting in the brains of pilocarpine-induced epileptic model rats at 6 h, 24 h, 72 h, 7 d, 14 d, 30 d, and 60 d after status epilepticus. Npas4 was localized primarily in the nucleus and in the cytoplasm of neurons. The Npas4 protein levels increased in the acute phase of seizures (between 6 h and 72 h) and decreased in the chronic phases (between 7 d and 60 d) in the rat model. Npas4 expression was knocked down by specific siRNA interference. Then, the animals were treated with pilocarpine, and the effects on seizures were evaluated on the 7th day. The onset latencies of pilocarpine-induced seizures were decreased, while the seizure frequency, duration and attack rate increased in these rats. Our study indicates that Npas4 inhibits seizure attacks in pilocarpine-induced epileptic rats. PMID:25536221

The inhibitory effects of Npas4 on seizures in pilocarpine-induced epileptic rats.

PubMed

Wang, Dan; Ren, Min; Guo, Jiamei; Yang, Guang; Long, Xianghua; Hu, Rong; Shen, Wenjing; Wang, Xuefeng; Zeng, Kebin

2014-01-01

To explore the effects of neuronal Per-Arnt-Sim domain protein 4 (Npas4) on seizures in pilocarpine-induced epileptic rats, Npas4 expression was detected by double-label immunofluorescence, immunohistochemistry, and Western blotting in the brains of pilocarpine-induced epileptic model rats at 6 h, 24 h, 72 h, 7 d, 14 d, 30 d, and 60 d after status epilepticus. Npas4 was localized primarily in the nucleus and in the cytoplasm of neurons. The Npas4 protein levels increased in the acute phase of seizures (between 6 h and 72 h) and decreased in the chronic phases (between 7 d and 60 d) in the rat model. Npas4 expression was knocked down by specific siRNA interference. Then, the animals were treated with pilocarpine, and the effects on seizures were evaluated on the 7th day. The onset latencies of pilocarpine-induced seizures were decreased, while the seizure frequency, duration and attack rate increased in these rats. Our study indicates that Npas4 inhibits seizure attacks in pilocarpine-induced epileptic rats.

The effect of albendazole treatment on seizure outcomes in patients with symptomatic neurocysticercosis.

PubMed

Romo, Matthew L; Wyka, Katarzyna; Carpio, Arturo; Leslie, Denise; Andrews, Howard; Bagiella, Emilia; Hauser, W Allen; Kelvin, Elizabeth A

2015-11-01

Randomized controlled trials have found an inconsistent effect of anthelmintic treatment on long-term seizure outcomes in neurocysticercosis. The objective of this study was to further explore the effect of albendazole treatment on long-term seizure outcomes and to determine if there is evidence for a differential effect by seizure type. In this trial, 178 patients with active or transitional neurocysticercosis cysts and new-onset symptoms were randomized to 8 days of treatment with albendazole (n=88) or placebo (n=90), both with prednisone, and followed for 24 months. We used negative binomial regression and logistic regression models to determine the effect of albendazole on the number of seizures and probability of recurrent or new-onset seizures, respectively, over follow-up. Treatment with albendazole was associated with a reduction in the number of seizures during 24 months of follow-up, but this was only significant for generalized seizures during months 1-12 (unadjusted rate ratio [RR] 0.19; 95% CI: 0.04-0.91) and months 1-24 (unadjusted RR 0.06; 95% CI: 0.01-0.57). We did not detect a significant effect of albendazole on reducing the number of focal seizures or on the probability of having a seizure, regardless of seizure type or time period. Albendazole treatment may be associated with some symptomatic improvement; however, this association seems to be specific to generalized seizures. Future research is needed to identify strategies to better reduce long-term seizure burden in patients with neurocysticercosis. © The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Alternative surgical approaches in epilepsy.

PubMed

Gigante, Paul R; Goodman, Robert R

2011-08-01

The mainstay of epilepsy surgery is the resection of a presumed seizure focus or disruption of seizure propagation pathways. These approaches cannot be applied to all patients with medically refractory epilepsy (MRE). Since 1997, vagus nerve stimulation has been a palliative adjunct to the care of MRE patients. Deep brain stimulation (DBS) in select locations has been reported to reduce seizure frequency in small studies over the past three decades. Recently published results from the SANTE (Stimulation of the Anterior Nuclei of Thalamus for Epilepsy) trial-the first large-scale, randomized, double-blind trial of bilateral anterior thalamus DBS for MRE-demonstrate a significant reduction in seizure frequency with programmed stimulation. Another surgical alternative is the RNS™ System (NeuroPace, Mountain View, CA), which uses a closed-loop system termed responsive neurostimulation to both detect apparent seizure onsets and deliver stimulation. Recently presented results from the RNS™ pivotal trial demonstrate a sustained reduction in seizure frequency with stimulation, although comprehensive trial results are pending.

Epileptic seizure detection from EEG signals with phase-amplitude cross-frequency coupling and support vector machine

NASA Astrophysics Data System (ADS)

Liu, Yang; Wang, Jiang; Cai, Lihui; Chen, Yingyuan; Qin, Yingmei

2018-03-01

As a pattern of cross-frequency coupling (CFC), phase-amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB-MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

Understanding Death in Children With Epilepsy.

PubMed

Donner, Elizabeth J; Camfield, Peter; Brooks, Linda; Buchhalter, Jeffrey; Camfield, Carol; Loddenkemper, Tobias; Wirrell, Elaine

2017-05-01

Death in children with epilepsy is profoundly disturbing, with lasting effects on the family, community, and health care providers. The overall risk of death for children with epilepsy is about ten times that of the general population. However, the risk of premature death for children without associated neurological comorbidities is similar to that of the general population, and most deaths are related to the cause of the epilepsy or associated neurological disability, not seizures. The most common cause of seizure-related death in children with epilepsy is sudden unexpected death in epilepsy (SUDEP). SUDEP is relatively uncommon in childhood, but the risk increases if epilepsy persists into adulthood. Although the direct cause of SUDEP remains unknown, most often death follows a generalized convulsive seizure and the risk of SUDEP is strongly related to drug-resistant epilepsy and frequent generalized tonic-clonic seizures. The most effective SUDEP prevention strategy is to reduce the frequency of seizures, although a number of seizure detection devices are under development and in the future may prove to be useful for seizure detection for those at particularly high risk. There are distinct benefits for health care professionals to discuss mortality with the family soon after the diagnosis of epilepsy. An individual approach is appropriate. When a child with epilepsy dies, particularly if the death was unexpected, family grief may be profound. Physicians and other health care professionals have a critical role in supporting families that lose a child to epilepsy. This review will provide health care providers with information needed to discuss the risk of death in children with epilepsy and support families following a loss. Copyright © 2017 Elsevier Inc. All rights reserved.

Detection of cortical optical changes during seizure activity using optical coherence tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Ornelas, Danielle; Hasan, Md.; Gonzalez, Oscar; Krishnan, Giri; Szu, Jenny I.; Myers, Timothy; Hirota, Koji; Bazhenov, Maxim; Binder, Devin K.; Park, Boris H.

2017-02-01

Electrophysiology has remained the gold standard of neural activity detection but its resolution and high susceptibility to noise and motion artifact limit its efficiency. Imaging techniques, including fMRI, intrinsic optical imaging, and diffuse optical imaging, have been used to detect neural activity, but rely on indirect measurements such as changes in blood flow. Fluorescence-based techniques, including genetically encoded indicators, are powerful techniques, but require introduction of an exogenous fluorophore. A more direct optical imaging technique is optical coherence tomography (OCT), a label-free, high resolution, and minimally invasive imaging technique that can produce depth-resolved cross-sectional and 3D images. In this study, we sought to examine non-vascular depth-dependent optical changes directly related to neural activity. We used an OCT system centered at 1310 nm to search for changes in an ex vivo brain slice preparation and an in vivo model during 4-AP induced seizure onset and propagation with respect to electrical recording. By utilizing Doppler OCT and the depth-dependency of the attenuation coefficient, we demonstrate the ability to locate and remove the optical effects of vasculature within the upper regions of the cortex from in vivo attenuation calculations. The results of this study show a non-vascular decrease in intensity and attenuation in ex vivo and in vivo seizure models, respectively. Regions exhibiting decreased optical changes show significant temporal correlation to regions of increased electrical activity during seizure. This study allows for a thorough and biologically relevant analysis of the optical signature of seizure activity both ex vivo and in vivo using OCT.

Surgical treatment of temporal lobe epilepsy: clinical, radiological, and histopathological findings in 178 patients.

PubMed Central

Zentner, J; Hufnagel, A; Wolf, H K; Ostertun, B; Behrens, E; Campos, M G; Solymosi, L; Elger, C E; Wiestler, O D; Schramm, J

1995-01-01

The surgical treatment of pharmacoresistant temporal lobe epilepsy is increasing rapidly. The correlation of preoperative MRI, histopathological findings, and postoperative seizure control is reported for 178 patients with chronic medically intractable temporal lobe epilepsy who were operated on between November 1987 and January 1993. Histopathologically there were distinct structural abnormalities in 97.2% of the surgical specimens. Signal abnormalities on MRI were present in 98.7% of patients with neoplastic lesions (n = 79), 76.6% of patients with non-neoplastic focal lesions (n = 55), and 69.2% of patients with Ammon's horn sclerosis (n = 39). Overall, structural abnormalities were detected by MRI in 82.7% of all patients. The mean postoperative follow up period was three years. Some 92% of the patients benefited from surgery: 103 patients (61.7%) were seizure free, 26 (15.5%) had no more than two seizures a year, and 24 (14.4%) showed a reduction of seizure frequency of at least 75%. Fourteen patients (8.4%) had a < 75% reduction of seizure frequency. The percentage of patients who were completely free of seizures after operation was 68.5% for patients with neoplastic lesions, 66.7% for Ammon's horn sclerosis, and 54.0% for patients with non-neoplastic focal lesions. By contrast, none of the patients in whom histopathological findings were normal became seizure free postoperatively. The data show that the presence of focal lesions or Ammon's horn sclerosis as determined by histopathological examination is associated with improved postoperative seizure control compared with patients without specific pathological findings. Brain MRI was very sensitive in detecting neoplasms; however, its sensitivity and specificity were limited with respect to non-neoplastic focal lesions and Ammon's horn sclerosis. Improvement of imaging techniques may provide a more precise definition of structural lesions in these cases and facilitate limited surgical resections of the epileptogenic area rather than standardised anatomical resections. Images PMID:7608662

Heart rate changes in partial seizures: analysis of influencing factors among refractory patients

PubMed Central

2014-01-01

Background We analyzed the frequency of heart rate (HR) changes related to seizures, and we sought to identify the influencing factors of these changes during partial seizures, to summarize the regularity of the HR changes and gain some insight into the mechanisms involved in the neuronal regulation of cardiovascular function. To date, detailed information on influencing factors of HR changes related to seizures by multiple linear regression analysis remains scarce. Methods Using video-electroencephalograph (EEG)-electrocardiograph (ECG) recordings, we retrospectively assessed the changes in the HR of 81 patients during a total of 181 seizures, including 27 simple partial seizures (SPS), 110 complex partial seizures (CPS) and 44 complex partial seizures secondarily generalized (CPS-G). The epileptogenic focus and the seizure type, age, gender, and sleep/wakefulness state of each patient were evaluated during and after the seizure onset. The HR changes were evaluated in the stage of epilepsy as time varies. Results Of the 181 seizures from 81 patients with ictal ECGs, 152 seizures (83.98%) from 74 patients were accompanied by ictal tachycardia (IT). And only 1 patient was accompanied by ictal bradycardia (IB). A patient has both IT and IB. We observed that HR difference was independently correlated with side, type and sleep/wakefulness state. In this analysis, the HR changes were related to the side, gender, seizure type, and sleep/wakefulness state. Right focus, male, sleep, and CPS-G showed more significant increases than that were observed in left, female, wakefulness, SPS and CPS. HR increases rapidly within 10 seconds before seizure onset and ictus, and typically slows to normal with seizure offset. Conclusion CPS-G, sleep and right focus led to higher ictal HR. The HR in the stage of epilepsy has regularly been observed to change to become time-varying. The risk factors of ictal HR need to be controlled along with sleep, CPS-G and right focus. Our study first explains that the HR in seizures has a regular evolution varying with time. Our study might help to further clarify the basic mechanisms of interactions between heart and brain, making seizure detection and closed-loop systems a possible therapeutic alternative in refractory patients. PMID:24950859

Age- and sex-dependent susceptibility to phenobarbital-resistant neonatal seizures: role of chloride co-transporters

PubMed Central

Kang, Seok Kyu; Markowitz, Geoffrey J.; Kim, Shin Tae; Johnston, Michael V.; Kadam, Shilpa D.

2015-01-01

Ischemia in the immature brain is an important cause of neonatal seizures. Temporal evolution of acquired neonatal seizures and their response to anticonvulsants are of great interest, given the unreliability of the clinical correlates and poor efficacy of first-line anti-seizure drugs. The expression and function of the electroneutral chloride co-transporters KCC2 and NKCC1 influence the anti-seizure efficacy of GABAA-agonists. To investigate ischemia-induced seizure susceptibility and efficacy of the GABAA-agonist phenobarbital (PB), with NKCC1 antagonist bumetanide (BTN) as an adjunct treatment, we utilized permanent unilateral carotid-ligation to produce acute ischemic-seizures in post-natal day 7, 10, and 12 CD1 mice. Immediate post-ligation video-electroencephalograms (EEGs) quantitatively evaluated baseline and post-treatment seizure burdens. Brains were examined for stroke-injury and western blot analyses to evaluate the expression of KCC2 and NKCC1. Severity of acute ischemic seizures post-ligation was highest at P7. PB was an efficacious anti-seizure agent at P10 and P12, but not at P7. BTN failed as an adjunct, at all ages tested and significantly blunted PB-efficacy at P10. Significant acute post-ischemic downregulation of KCC2 was detected at all ages. At P7, males displayed higher age-dependent seizure susceptibility, associated with a significant developmental lag in their KCC2 expression. This study established a novel neonatal mouse model of PB-resistant seizures that demonstrates age/sex-dependent susceptibility. The age-dependent profile of KCC2 expression and its post-insult downregulation may underlie the PB-resistance reported in this model. Blocking NKCC1 with low-dose BTN following PB treatment failed to improve PB-efficacy. PMID:26029047

Seizure semiology: an important clinical clue to the diagnosis of autoimmune epilepsy.

PubMed

Lv, Rui-Juan; Ren, Hai-Tao; Guan, Hong-Zhi; Cui, Tao; Shao, Xiao-Qiu

2018-02-01

The purpose of this study is to analyze the seizure semiologic characteristics of patients with autoimmune epilepsy (AE) and describe the investigation characteristics of AE using a larger sample size. This observational retrospective case series study was conducted from a tertiary epilepsy center between May 2014 and March 2017. Cases of new-onset seizures were selected based on laboratory evidence of autoimmunity. At the same time, typical mesial temporal lobe epilepsy (MTLE) patients with hippocampal sclerosis (HS) were recruited as the control group from the subjects who underwent presurgical evaluation during the same period. A total of 61 patients with AE were identified. Specific autoimmune antibodies were detected in 39 patients (63.93%), including anti-VGKC in 23 patients (37.70%), anti-NMDA-R in 9 patients (14.75%), anti-GABA B -R in 6 patients (9.84%), and anti-amphiphysin in 1 patient (1.64%). Regarding the seizure semiology, no significant differences were noted between AE patients with autoantibody and patients with suspected AE without antibody. Compared to typical MTLE patients with HS, both AE patients with autoantibody and patients with suspected AE without antibody had the same seizure semiologic characteristics, including more frequent SPS or CPS, shorter seizure duration, rare postictal confusion, and common sleeping SGTC seizures. This study highlights important seizure semiologic characteristics of AE. Patients with autoimmune epilepsy had special seizure semiologic characteristics. For patients with autoimmune epilepsy presenting with new-onset seizures in isolation or with a seizure-predominant neurological disorder, the special seizure semiologic characteristics may remind us to test neuronal nuclear/cytoplasmic antibodies early and initiate immunomodulatory therapies as soon as possible. Furthermore, the absence of neural-specific autoantibodies does not rule out AE.

Age- and sex-dependent susceptibility to phenobarbital-resistant neonatal seizures: role of chloride co-transporters.

PubMed

Kang, Seok Kyu; Markowitz, Geoffrey J; Kim, Shin Tae; Johnston, Michael V; Kadam, Shilpa D

2015-01-01

Ischemia in the immature brain is an important cause of neonatal seizures. Temporal evolution of acquired neonatal seizures and their response to anticonvulsants are of great interest, given the unreliability of the clinical correlates and poor efficacy of first-line anti-seizure drugs. The expression and function of the electroneutral chloride co-transporters KCC2 and NKCC1 influence the anti-seizure efficacy of GABAA-agonists. To investigate ischemia-induced seizure susceptibility and efficacy of the GABAA-agonist phenobarbital (PB), with NKCC1 antagonist bumetanide (BTN) as an adjunct treatment, we utilized permanent unilateral carotid-ligation to produce acute ischemic-seizures in post-natal day 7, 10, and 12 CD1 mice. Immediate post-ligation video-electroencephalograms (EEGs) quantitatively evaluated baseline and post-treatment seizure burdens. Brains were examined for stroke-injury and western blot analyses to evaluate the expression of KCC2 and NKCC1. Severity of acute ischemic seizures post-ligation was highest at P7. PB was an efficacious anti-seizure agent at P10 and P12, but not at P7. BTN failed as an adjunct, at all ages tested and significantly blunted PB-efficacy at P10. Significant acute post-ischemic downregulation of KCC2 was detected at all ages. At P7, males displayed higher age-dependent seizure susceptibility, associated with a significant developmental lag in their KCC2 expression. This study established a novel neonatal mouse model of PB-resistant seizures that demonstrates age/sex-dependent susceptibility. The age-dependent profile of KCC2 expression and its post-insult downregulation may underlie the PB-resistance reported in this model. Blocking NKCC1 with low-dose BTN following PB treatment failed to improve PB-efficacy.

Psychological characteristics of patients with newly developed psychogenic seizures

PubMed Central

van Merode, T; Twellaar, M; Kotsopoulos, I; Kessels, A; Merckelbach, H; de Krom, M C T F M; Knottnerus, J

2004-01-01

Methods: Using validated scales, 178 patients from the general population diagnosed with newly developed seizures were assessed, at a point in time when the nature of their seizures was yet unknown to either doctors or patients. After standardised neurological examination, 138 patients were diagnosed with non-psychogenic seizures (NPS), while 40 patients were found to have psychogenic seizures (PS). To evaluate possible differences between the genders and the diagnostic groups, univariate analyses of variance were done. Results: PS patients reported significantly more comorbid psychopathological complaints, dissociative experiences, anxiety, and self-reported childhood trauma than NPS patients. In addition, PS patients had lower quality of life ratings than NPS patients. These effects were not modulated by gender. Conclusions: The results of the present study indicate that patients with newly developed PS constitute a group with complex psychopathological features that warrant early detection and treatment. PMID:15258225

A Novel Signal Modeling Approach for Classification of Seizure and Seizure-Free EEG Signals.

PubMed

Gupta, Anubha; Singh, Pushpendra; Karlekar, Mandar

2018-05-01

This paper presents a signal modeling-based new methodology of automatic seizure detection in EEG signals. The proposed method consists of three stages. First, a multirate filterbank structure is proposed that is constructed using the basis vectors of discrete cosine transform. The proposed filterbank decomposes EEG signals into its respective brain rhythms: delta, theta, alpha, beta, and gamma. Second, these brain rhythms are statistically modeled with the class of self-similar Gaussian random processes, namely, fractional Brownian motion and fractional Gaussian noises. The statistics of these processes are modeled using a single parameter called the Hurst exponent. In the last stage, the value of Hurst exponent and autoregressive moving average parameters are used as features to design a binary support vector machine classifier to classify pre-ictal, inter-ictal (epileptic with seizure free interval), and ictal (seizure) EEG segments. The performance of the classifier is assessed via extensive analysis on two widely used data set and is observed to provide good accuracy on both the data set. Thus, this paper proposes a novel signal model for EEG data that best captures the attributes of these signals and hence, allows to boost the classification accuracy of seizure and seizure-free epochs.

EEG power as a biomarker to predict the outcome after cardiac arrest and cardiopulmonary resuscitation induced global ischemia.

PubMed

Weitzel, Lindsay-Rae; Sampath, Dayalan; Shimizu, Kaori; White, Andrew M; Herson, Paco S; Raol, Yogendra H

2016-11-15

Cardiac arrest (CA) is a major cause of mortality and survivors often develop neurologic deficits. The objective of this study was to determine the effect of CA and cardiopulmonary resuscitation (CPR) in mice on the EEG and neurologic outcomes, and identify biomarkers that can prognosticate poor outcomes. Video-EEG records were obtained at various periods following CA-CPR and examined manually to determine the presence of spikes and sharp-waves, and seizures. EEG power was calculated using a fast Fourier transform (FFT) algorithm. Fifty percent mice died within 72h following CA and successful CPR. Universal suppression of the background EEG was observed in all mice following CA-CPR, however, a more severe and sustained reduction in EEG power occurred in the mice that did not survive beyond 72h than those that survived until sacrificed. Spikes and sharp wave activity appeared in the cortex and hippocampus of all mice, but only one out of eight mice developed a purely electrographic seizure in the acute period after CA-CPR. Interestingly, none of the mice that died experienced any acute seizures. At 10days after the CA-CPR, 25% of the mice developed spontaneous convulsive and nonconvulsive seizures that remained restricted to the hippocampus. The frequency of nonconvulsive seizures was higher than that of convulsive seizures. A strong association between changes in EEG power and mortality following CA-CPR were observed in our study. Therefore, we suggest that the EEG power can be used to prognosticate mortality following CA-CPR induced global ischemia. Copyright © 2016 Elsevier Inc. All rights reserved.

Speech recognition features for EEG signal description in detection of neonatal seizures.

PubMed

Temko, A; Boylan, G; Marnane, W; Lightbody, G

2010-01-01

In this work, features which are usually employed in automatic speech recognition (ASR) are used for the detection of neonatal seizures in newborn EEG. Three conventional ASR feature sets are compared to the feature set which has been previously developed for this task. The results indicate that the thoroughly-studied spectral envelope based ASR features perform reasonably well on their own. Additionally, the SVM Recursive Feature Elimination routine is applied to all extracted features pooled together. It is shown that ASR features consistently appear among the top-rank features.

Removing interictal fast ripples on electrocorticography linked with seizure freedom in children.

PubMed

Wu, J Y; Sankar, R; Lerner, J T; Matsumoto, J H; Vinters, H V; Mathern, G W

2010-11-09

Fast ripples (FR, 250-500 Hz) detected with chronic intracranial electrodes are proposed biomarkers of epileptogenesis. This study determined whether resection of FR-containing neocortex recorded during intraoperative electrocorticography (ECoG) was associated with postoperative seizure freedom in pediatric patients with mostly extratemporal lesions. FRs were retrospectively reviewed in 30 consecutive pediatric cases. ECoGs were recorded at 2,000 Hz sampling rate and visually inspected for FR, with reviewer blinded to the resection and outcome. Average age at surgery was 9.1 ± 6.7 years, ECoG duration was 11.8 ± 8.1 minutes, and postoperative follow-up was 27 ± 4 months. FRs were undetected in 6 ECoGs with remote or extensive lesions. FR episodes (n = 273) were identified in ECoGs from 24 patients, and in 64% FRs were independent of spikes, sharp waves, voltage attenuation, and paroxysmal fast activity. Of these 24 children, FR-containing cortex was removed in 19 and all became seizure-free, including 1 child after a second surgery. The remaining 5 children had incomplete FR resection and all continued with seizures postoperatively. In 2 ECoGs, the location of electrographic seizures matched FR location. FR-containing cortex was found outside of MRI and FDG-PET abnormalities in 6 children. FRs were detected during intraoperative ECoG in 80% of pediatric epilepsy cases, and complete resection of FR cortex correlated with postoperative seizure freedom. These findings support the view that interictal FRs are excellent surrogate markers of epileptogenesis, can be recorded during brief ECoG, and could be used to guide future surgical resections in children.

Association between brain structural anomalies, electroencephalogram and history of seizures in Mucopolysaccharidosis type II (Hunter syndrome).

PubMed

Jiménez-Arredondo, Ramón Ernesto; Brambila-Tapia, Aniel Jessica Leticia; Mercado-Silva, Francisco Miguel; Ortiz-Aranda, Martha; Benites-Godinez, Verónica; Olmos-García-de-Alba, Graciela; Figuera, Luis Eduardo

2017-03-01

Mucopolysaccharidosis type II or Hunter syndrome (MPS II) is a genetic disease that can course with intellectual impairment and central nervous system (CNS) alterations. To date, no report has documented electroencephalogram (EEG) measures associated with CNS alterations, detected by imaging studies, and the history of seizures in patients with MPS II. Therefore, we decided to search this association. We included 9 patients with MPS II and performed imaging studies of the brain to detect the presence of cortico-subcortical atrophy, enlarged subarachnoid space and supratentorial ventricular size. Additionally, we performed EEG studies in sleep and awake conditions and a complete clinical description. Five out of the nine patients presented history of seizures and all except one patient (88.9%) presented some CNS structural alteration in the imaging studies, being the most frequent the cortico-subcortical atrophy (77.8%). The EEG results showed low amplitude in all patients and low voltage in sleep condition in eight patients with interhemispheric asymmetry in six patients during awake and sleep conditions. Although the five patients with history of seizures did not present a distinctive EEG anomaly, four of them presented some structural alteration in the imaging studies. In conclusion, most patients presented structural alterations in the CNS; likewise, all of them presented EEG anomalies mainly during sleep conditions. However, a clear association between EEG, CNS and the history of seizures was not established.

Biochemical abnormalities in neonatal seizures.

PubMed

Sood, Arvind; Grover, Neelam; Sharma, Roshan

2003-03-01

The presence of seizure does not constitute a diagnoses but it is a symptom of an underlying central nervous system disorder due to systemic or biochemical disturbances. Biochemical disturbances occur frequently in the neonatal seizures either as an underlying cause or as an associated abnormality. In their presence, it is difficult to control seizure and there is a risk of further brain damage. Early recognition and treatment of biochemical disturbances is essential for optimal management and satisfactory long term outcome. The present study was conducted in the department of pediatrics in IGMC Shimla on 59 neonates. Biochemical abnormalities were detected in 29 (49.15%) of cases. Primary metabolic abnormalities occurred in 10(16.94%) cases of neonatal seizures, most common being hypocalcaemia followed by hypoglycemia, other metabolic abnormalities include hypomagnesaemia and hyponateremia. Biochemical abnormalities were seen in 19(38.77%) cases of non metabolic seizure in neonates. Associated metabolic abnormalities were observed more often with Hypoxic-ischemic-encephalopathy (11 out of 19) cases and hypoglycemia was most common in this group. No infant had hyponateremia, hyperkelemia or low zinc level.

Ambulatory Seizure Monitoring: From Concept to Prototype Device.

PubMed

Myers, Mark H; Threatt, Madeline; Solies, Karsten M; McFerrin, Brent M; Hopf, Lindsey B; Birdwell, J Douglas; Sillay, Karl A

2016-07-01

The brain, made up of billions of neurons and synapses, is the marvelous core of human thought, action and memory. However, if neuronal activity manifests into abnormal electrical activity across the brain, neural behavior may exhibit synchronous neural firings known as seizures. If unprovoked seizures occur repeatedly, a patient may be diagnosed with epilepsy. The scope of this project is to develop an ambulatory seizure monitoring system that can be used away from a hospital, making it possible for the user to stay at home, and primary care personnel to monitor a patient's seizure activity in order to provide deeper analysis of the patient's condition and apply personalized intervention techniques. The ambulatory seizure monitoring device is a research device that has been developed with the objective of acquiring a portable, clean electroencephalography (EEG) signal and transmitting it wirelessly to a handheld device for processing and notification. This device is comprised of 4 phases: acquisition, transmission, processing and notification. During the acquisition stage, the EEG signal is detected using EEG electrodes; these signals are filtered and amplified before being transmitted in the second stage. The processing stage encompasses the signal processing and seizure prediction. A notification is sent to the patient and designated contacts, given an impending seizure. Each of these phases is comprised of various design components, hardware and software. The experimental findings illustrate that there may be a triggering mechanism through the phase lock value method that enables seizure prediction. The device addresses the need for long-term monitoring of the patient's seizure condition in order to provide the clinician a better understanding of the seizure's duration and frequency and ultimately provide the best remedy for the patient.

Ambulatory Seizure Monitoring: From Concept to Prototype Device

PubMed Central

Myers, Mark H.; Threatt, Madeline; Solies, Karsten M.; McFerrin, Brent M.; Hopf, Lindsey B.; Birdwell, J. Douglas; Sillay, Karl A.

2016-01-01

Background The brain, made up of billions of neurons and synapses, is the marvelous core of human thought, action and memory. However, if neuronal activity manifests into abnormal electrical activity across the brain, neural behavior may exhibit synchronous neural firings known as seizures. If unprovoked seizures occur repeatedly, a patient may be diagnosed with epilepsy. Purpose The scope of this project is to develop an ambulatory seizure monitoring system that can be used away from a hospital, making it possible for the user to stay at home, and primary care personnel to monitor a patient's seizure activity in order to provide deeper analysis of the patient's condition and apply personalized intervention techniques. Methods The ambulatory seizure monitoring device is a research device that has been developed with the objective of acquiring a portable, clean electroencephalography (EEG) signal and transmitting it wirelessly to a handheld device for processing and notification. Result This device is comprised of 4 phases: acquisition, transmission, processing and notification. During the acquisition stage, the EEG signal is detected using EEG electrodes; these signals are filtered and amplified before being transmitted in the second stage. The processing stage encompasses the signal processing and seizure prediction. A notification is sent to the patient and designated contacts, given an impending seizure. Each of these phases is comprised of various design components, hardware and software. The experimental findings illustrate that there may be a triggering mechanism through the phase lock value method that enables seizure prediction. Conclusion The device addresses the need for long-term monitoring of the patient's seizure condition in order to provide the clinician a better understanding of the seizure's duration and frequency and ultimately provide the best remedy for the patient. PMID:27647960

Nicotine increases eclampsia-like seizure threshold and attenuates microglial activity in rat hippocampus through the α7 nicotinic acetylcholine receptor.

PubMed

Li, Xiaolan; Han, Xinjia; Bao, Junjie; Liu, Yuanyuan; Ye, Aihua; Thakur, Mukesh; Liu, Huishu

2016-07-01

A considerable number of studies have demonstrated that nicotine, a α7-nicotinic acetylcholine receptor (α7-nAChR) agonist, can dampen immune response through the cholinergic anti-inflammatory pathway. Evidence suggests that inflammation plays a critical role in eclampsia, which contributes to maternal and fetal morbidity and mortality. In the present study, possible anti-inflammation and neuro-protective effects of nicotine via α7-nAChRs have been investigated after inducing eclampsia-like seizures in rats. Rat eclampsia-like models were established by administering lipopolysaccharide (LPS) plus pentylenetetrazol (PTZ) in pregnant rats. Rats were given nicotine from gestation day (GD) 14-19. Then, clinical symptoms were detected. Seizure severity was recorded by behavioral tests, serum levels of inflammatory cytokines were measured by Luminex assays, microglia and astrocyte expressions were detected by immunofluorescence, and changes in neuronal number in the hippocampal CA1 region among different groups were detected by Nissl staining. Our results revealed that nicotine effectively improved fetal outcomes. Furthermore, it significantly decreased systolic blood pressure, and maternal serum levels of Th1 cytokines (TNF-α, IL-1β, IL-6 and IL-12P70) and an IL-17 cytokine (IL-17A), and dramatically increased eclampsia-like seizure threshold. Moreover, this attenuated neuronal loss and decreased the expression of microglial activation markers of the hippocampal CA1 region in the eclampsia-like group. Additionally, pretreatment with α-bungarotoxin, a selective α7-nAChR antagonist could prevent the protective effects of nicotine in eclampsia-like model rats. Our findings indicate that the administration of nicotine may attenuate microglial activity and increase eclampsia-like seizure threshold in rat hippocampus through the α7 nicotinic receptor. Copyright © 2016 Elsevier B.V. All rights reserved.

High frequency oscillations (80–500 Hz) in the preictal period in patients with focal seizures

PubMed Central

Jacobs, Julia; Zelmann, Rina; Jirsch, Jeffrey; Chander, Rahul; Châtillon, Claude-Édouard; Dubeau, François; Gotman, Jean

2013-01-01

Summary Purpose Intracranial depth macroelectrode recordings from patients with focal seizures demonstrate interictal and ictal high frequency oscillations (HFOs, 80–500 Hz). These HFOs are more frequent in the seizure-onset zone (SOZ) and reported to be linked to seizure genesis. We evaluated whether HFO activity changes in a systematic way during the preictal period. Methods Fifteen minutes of preictal intracranial electroencephalography (EEG) recordings were evaluated in seven consecutive patients with well-defined SOZ. EEG was filtered at 500 Hz and sampled at 2,000 Hz. Ripples (80–250 Hz) and fast ripples (250–500 Hz) were visually marked, and spectral analysis was performed in seizure-onset as well as nonseizure-onset channels. Linear regressions fitted to the power trends corresponding to intervals of 1, 5, and 15 min before the seizure onset was calculated. Results Total rates of HFOs were significantly higher in the SOZ than outside. Preictal increases and decreases in HFO rates and band power could be detected in all patients, and they were not limited to the SOZs. These measures were very variable, and nosystematic trends were observed when comparing patients or seizures in the same patient. Discussion High frequencies in the range of 80–500 Hz are present during the preictal period and are more prominent in the SOZ. They do not change in a systematic way before seizure onset for the horizons we tested. The 80–500 Hz band may be used for the localization of seizure-onset areas but may be more difficult to use for seizure prediction purposes. PMID:19400871

Instantaneous frequency based newborn EEG seizure characterisation

NASA Astrophysics Data System (ADS)

Mesbah, Mostefa; O'Toole, John M.; Colditz, Paul B.; Boashash, Boualem

2012-12-01

The electroencephalogram (EEG), used to noninvasively monitor brain activity, remains the most reliable tool in the diagnosis of neonatal seizures. Due to their nonstationary and multi-component nature, newborn EEG seizures are better represented in the joint time-frequency domain than in either the time domain or the frequency domain. Characterising newborn EEG seizure nonstationarities helps to better understand their time-varying nature and, therefore, allow developing efficient signal processing methods for both modelling and seizure detection and classification. In this article, we used the instantaneous frequency (IF) extracted from a time-frequency distribution to characterise newborn EEG seizures. We fitted four frequency modulated (FM) models to the extracted IFs, namely a linear FM, a piecewise-linear FM, a sinusoidal FM, and a hyperbolic FM. Using a database of 30-s EEG seizure epochs acquired from 35 newborns, we were able to show that, depending on EEG channel, the sinusoidal and piecewise-linear FM models best fitted 80-98% of seizure epochs. To further characterise the EEG seizures, we calculated the mean frequency and frequency span of the extracted IFs. We showed that in the majority of the cases (>95%), the mean frequency resides in the 0.6-3 Hz band with a frequency span of 0.2-1 Hz. In terms of the frequency of occurrence of the four seizure models, the statistical analysis showed that there is no significant difference( p = 0.332) between the two hemispheres. The results also indicate that there is no significant differences between the two hemispheres in terms of the mean frequency ( p = 0.186) and the frequency span ( p = 0.302).

Continuous electroencephalogram monitoring in the intensive care unit.

PubMed

Friedman, Daniel; Claassen, Jan; Hirsch, Lawrence J

2009-08-01

Because of recent technical advances, it is now possible to record and monitor the continuous digital electroencephalogram (EEG) of many critically ill patients simultaneously. Continuous EEG monitoring (cEEG) provides dynamic information about brain function that permits early detection of changes in neurologic status, which is especially useful when the clinical examination is limited. Nonconvulsive seizures are common in comatose critically ill patients and can have multiple negative effects on the injured brain. The majority of seizures in these patients cannot be detected without cEEG. cEEG monitoring is most commonly used to detect and guide treatment of nonconvulsive seizures, including after convulsive status epilepticus. In addition, cEEG is used to guide management of pharmacological coma for treatment of increased intracranial pressure. An emerging application for cEEG is to detect new or worsening brain ischemia in patients at high risk, especially those with subarachnoid hemorrhage. Improving quantitative EEG software is helping to make it feasible for cEEG (using full scalp coverage) to provide continuous information about changes in brain function in real time at the bedside and to alert clinicians to any acute brain event, including seizures, ischemia, increasing intracranial pressure, hemorrhage, and even systemic abnormalities affecting the brain, such as hypoxia, hypotension, acidosis, and others. Monitoring using only a few electrodes or using full scalp coverage, but without expert review of the raw EEG, must be done with extreme caution as false positives and false negatives are common. Intracranial EEG recording is being performed in a few centers to better detect seizures, ischemia, and peri-injury depolarizations, all of which may contribute to secondary injury. When cEEG is combined with individualized, physiologically driven decision making via multimodality brain monitoring, intensivists can identify when the brain is at risk for injury or when neuronal injury is already occurring and intervene before there is permanent damage. The exact role and cost-effectiveness of cEEG at the current time remains unclear, but we believe it has significant potential to improve neurologic outcomes in a variety of settings.

Status Epilepticus

PubMed Central

Manno, Edward M.

2011-01-01

Status epilepticus is a neurological emergency that is commonly encountered by the neurohospitalist. Successful treatment depends upon the recognition of prolonged seizure activity and the acute mobilization of available resources. Pharmacologic treatment regimens have been shown to decrease the time needed for successful control of seizures and have provided for the rapid administration of anticonvulsant medications. Treatment strategies have evolved so that clinicians can administer effective doses of medication by whatever routes of administration are immediately available. Traditional algorithms for the treatment of status epilepticus have used a stepwise approach to the administration of first-, second-, and third-order medications. More recent options have included aggressive anesthetic doses of medications while second-line medications are being titrated. PMID:23983834

Localization of cortical tissue optical changes during seizure activity in vivo with optical coherence tomography

PubMed Central

Eberle, Melissa M.; Hsu, Mike S.; Rodriguez, Carissa L.; Szu, Jenny I.; Oliveira, Michael C.; Binder, Devin K.; Park, B. Hyle

2015-01-01

Optical coherence tomography (OCT) is a high resolution, minimally invasive imaging technique, which can produce depth-resolved cross-sectional images. In this study, OCT was used to detect changes in the optical properties of cortical tissue in vivo in mice during the induction of global (pentylenetetrazol) and focal (4-aminopyridine) seizures. Through the use of a confidence interval statistical method on depth-resolved volumes of attenuation coefficient, we demonstrated localization of regions exhibiting both significant positive and negative changes in attenuation coefficient, as well as differentiating between global and focal seizure propagation. PMID:26137382

Detection of recurrent activation patterns across focal seizures: Application to seizure onset zone identification.

PubMed

Vila-Vidal, Manel; Principe, Alessandro; Ley, Miguel; Deco, Gustavo; Tauste Campo, Adrià; Rocamora, Rodrigo

2017-06-01

We introduce a method that quantifies the consistent involvement of intracranially monitored regions in recurrent focal seizures. We evaluated the consistency of two ictal spectral activation patterns (mean power change and power change onset time) in intracranial recordings across focal seizures from seven patients with clinically marked seizure onset zone (SOZ). We examined SOZ discrimination using both patterns in different frequency bands and periods of interest. Activation patterns were proved to be consistent across more than 80% of recurrent ictal epochs. In all patients, whole-seizure mean activations were significantly higher for SOZ than non-SOZ regions (P<0.05) while activation onset times were significantly lower for SOZ than for non-SOZ regions (P<0.001) in six patients. Alpha-beta bands (8-20Hz) achieved the highest patient-average effect size on the whole-seizure period while gamma band (20-70Hz) achieved the highest discrimination values between SOZ and non-SOZ sites near seizure onset (0-5s). Consistent spectral activation patterns in focal epilepsies discriminate the SOZ with high effect sizes upon appropriate selection of frequency bands and activation periods. The present method may be used to improve epileptogenic identification as well as pinpoint additional regions that are functionally altered during ictal events. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Time-Varying Networks of Inter-Ictal Discharging Reveal Epileptogenic Zone.

PubMed

Zhang, Luyan; Liang, Yi; Li, Fali; Sun, Hongbin; Peng, Wenjing; Du, Peishan; Si, Yajing; Song, Limeng; Yu, Liang; Xu, Peng

2017-01-01

The neuronal synchronous discharging may cause an epileptic seizure. Currently, most of the studies conducted to investigate the mechanism of epilepsy are based on EEGs or functional magnetic resonance imaging (fMRI) recorded during the ictal discharging or the resting-state, and few studies have probed into the dynamic patterns during the inter-ictal discharging that are much easier to record in clinical applications. Here, we propose a time-varying network analysis based on adaptive directed transfer function to uncover the dynamic brain network patterns during the inter-ictal discharging. In addition, an algorithm based on the time-varying outflow of information derived from the network analysis is developed to detect the epileptogenic zone. The analysis performed revealed the time-varying network patterns during different stages of inter-ictal discharging; the epileptogenic zone was activated prior to the discharge onset then worked as the source to propagate the activity to other brain regions. Consistence between the epileptogenic zones detected by our proposed approach and the actual epileptogenic zones proved that time-varying network analysis could not only reveal the underlying neural mechanism of epilepsy, but also function as a useful tool in detecting the epileptogenic zone based on the EEGs in the inter-ictal discharging.

Cerebellar Directed Optogenetic Intervention Inhibits Spontaneous Hippocampal Seizures in a Mouse Model of Temporal Lobe Epilepsy1 2

PubMed Central

Szabo, Gergely G.; Armstrong, Caren; Oijala, Mikko; Soltesz, Ivan

2014-01-01

Abstract Cover Figure Krook-Magnuson et al. report a bidirectional functional connectivity between the hippocampus and the cerebellum in a mouse model of temporal lobe epilepsy, and demonstrate that cerebellar directed on-demand optogenetic intervention can stop seizures recorded from the hippocampus. Temporal lobe epilepsy is often medically refractory and new targets for intervention are needed. We used a mouse model of temporal lobe epilepsy, on-line seizure detection, and responsive optogenetic intervention to investigate the potential for cerebellar control of spontaneous temporal lobe seizures. Cerebellar targeted intervention inhibited spontaneous temporal lobe seizures during the chronic phase of the disorder. We further report that the direction of modulation as well as the location of intervention within the cerebellum can affect the outcome of intervention. Specifically, on-demand optogenetic excitation or inhibition of parvalbumin-expressing neurons, including Purkinje cells, in the lateral or midline cerebellum results in a decrease in seizure duration. In contrast, a consistent reduction in spontaneous seizure frequency occurs uniquely with on-demand optogenetic excitation of the midline cerebellum, and was not seen with intervention directly targeting the hippocampal formation. These findings demonstrate that the cerebellum is a powerful modulator of temporal lobe epilepsy, and that intervention targeting the cerebellum as a potential therapy for epilepsy should be revisited. PMID:25599088

Seizures are common in term infants undergoing head cooling.

PubMed

Yap, Vivien; Engel, Murray; Takenouchi, Toshiki; Perlman, Jeffrey M

2009-11-01

Selective head cooling was used to treat infants at risk of developing encephalopathy within 6 hours as part of a practice plan. Amplitude-integrated electroencephalography and raw, single-channel electroencephalography tracings were performed continuously during cooling. Routine electroencephalography was performed intermittently during, and video electroencephalography immediately after, selective head cooling. Magnetic resonance imaging was performed at the end of week 1. We sought a better delineation of the occurrence and timing of clinical and electrographic seizures during selective head cooling. Twenty term infants are described. Eleven received chest compressions, all at pH <7. Upon admission, encephalopathy was characterized clinically as moderate (n = 13) or severe (n = 7), and by amplitude-integrated electroencephalography as moderate (n = 8), severe (n = 6), or indeterminate (n = 6). Clinical seizures (n = 18) were most prominent on day 1. Amplitude-integrated electroencephalography seizures (n = 9) were evident upon admission and on day 1 (n = 19), and were continuous between 24-36 hours (n = 9). Amplitude-integrated electroencephalography seizures were confirmed by routine electroencephalography. Magnetic resonance imaging was abnormal in nine infants, with predominantly bilateral involvement of the basal ganglia (n = 8). Magnesium was at

A Phase-Locked Loop Epilepsy Network Emulator.

PubMed

Watson, P D; Horecka, K M; Cohen, N J; Ratnam, R

2016-10-15

Most seizure forecasting employs statistical learning techniques that lack a representation of the network interactions that give rise to seizures. We present an epilepsy network emulator (ENE) that uses a network of interconnected phase-locked loops (PLLs) to model synchronous, circuit-level oscillations between electrocorticography (ECoG) electrodes. Using ECoG data from a canine-epilepsy model (Davis et al. 2011) and a physiological entropy measure (approximate entropy or ApEn, Pincus 1995), we demonstrate the entropy of the emulator phases increases dramatically during ictal periods across all ECoG recording sites and across all animals in the sample. Further, this increase precedes the observable voltage spikes that characterize seizure activity in the ECoG data. These results suggest that the ENE is sensitive to phase-domain information in the neural circuits measured by ECoG and that an increase in the entropy of this measure coincides with increasing likelihood of seizure activity. Understanding this unpredictable phase-domain electrical activity present in ECoG recordings may provide a target for seizure detection and feedback control.

Seizure Action Plans Do Not Reduce Health Care Utilization in Pediatric Epilepsy Patients.

PubMed

Roundy, Lindsi M; Filloux, Francis M; Kerr, Lynne; Rimer, Alyssa; Bonkowsky, Joshua L

2016-03-01

Management of pediatric epilepsy requires complex coordination of care. We hypothesized that an improved seizure management care plan would reduce health care utilization and improve outcomes. The authors conducted a cohort study with historical controls of 120 epilepsy patients before and after implementation of a "Seizure Action Plan." The authors evaluated for differences in health care utilization including emergency department visits, hospitalizations, clinic visits, telephone calls, and the percentage of emergency department visits that resulted in hospitalization in patients who did or did not have a Seizure Action Plan. The authors found that there was no decrease in these measures of health care utilization, and in fact the number of follow-up clinic visits was increased in the group with Seizure Action Plans (4.2 vs 3.3, P = .006). However, the study was underpowered to detect smaller differences. This study suggests that pediatric epilepsy quality improvement measures may require alternative approaches to reduce health care utilization and improve outcomes. © The Author(s) 2015.

Seizure Action Plans Do Not Reduce Health Care Utilization in Pediatric Epilepsy Patients

PubMed Central

Roundy, Lindsi M.; Filloux, Francis M.; Kerr, Lynne; Rimer, Alyssa; Bonkowsky, Joshua L.

2015-01-01

Management of pediatric epilepsy requires complex coordination of care. We hypothesized that an improved seizure management care plan would reduce healthcare utilization and improve outcomes. We conducted a cohort study with historical controls of 120 epilepsy patients before and after implementation of a “Seizure Action Plan.” We evaluated for differences in healthcare utilization including emergency department visits, hospitalizations, clinic visits, telephone calls, and the percentage of emergency department visits that resulted in hospitalization in patients who did or did not have a Seizure Action Plan. We found that there was no decrease in these measures of healthcare utilization, and in fact the number of follow-up clinic visits was increased in the group with Seizure Action Plans (4.2 versus 3.3, p 0.006). However, the study was underpowered to detect smaller differences. Our study suggests that pediatric epilepsy quality improvement measures may require alternative approaches to reduce healthcare utilization and improve outcomes. PMID:26245799

Epileptic Seizure Prediction Using a New Similarity Index for Chaotic Signals

NASA Astrophysics Data System (ADS)

Niknazar, Hamid; Nasrabadi, Ali Motie

Epileptic seizures are generated by abnormal activity of neurons. The prediction of epileptic seizures is an important issue in the field of neurology, since it may improve the quality of life of patients suffering from drug resistant epilepsy. In this study a new similarity index based on symbolic dynamic techniques which can be used for extracting behavior of chaotic time series is presented. Using Freiburg EEG dataset, it is found that the method is able to detect the behavioral changes of the neural activity prior to epileptic seizures, so it can be used for prediction of epileptic seizure. A sensitivity of 63.75% with 0.33 false positive rate (FPR) in all 21 patients and sensitivity of 96.66% with 0.33 FPR in eight patients were achieved using the proposed method. Moreover, the method was evaluated by applying on Logistic and Tent map with different parameters to demonstrate its robustness and ability in determining similarity between two time series with the same chaotic characterization.

The role of hypnosis in the detection of psychogenic seizures.

PubMed

Martínez-Taboas, Alfonso

2002-07-01

In this preliminary clinical investigation, hypnosis was used in the differential diagnosis of epileptic versus psychogenic seizures (PS). Eight patients with a clinical profile suggesting the presence of PS were given a hypnotic suggestion in which they had to go back in time to the exact moment of their last seizure. They were then asked to concentrate their attention on any unusual feeling or bodily sensation. All 8 patients presented a PS during the age regression protocol. In 6 cases, independent testimony from family members corroborated the morphological similarity of the induced attack and the ones presented in their natural environment. Also, the seizures ended abruptly after a command was given to stop them. A control group of 5 epileptic subjects did not present any signs of discomfort or seizure behavior during the hypnotic protocol. It is argued that a simple procedure as the one described in this investigation can be useful as a diagnostic tool in the differentiation of epileptic from PS attacks.

A review of channel selection algorithms for EEG signal processing

NASA Astrophysics Data System (ADS)

Alotaiby, Turky; El-Samie, Fathi E. Abd; Alshebeili, Saleh A.; Ahmad, Ishtiaq

2015-12-01

Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

Identifying Seizure Onset Zone From the Causal Connectivity Inferred Using Directed Information

NASA Astrophysics Data System (ADS)

Malladi, Rakesh; Kalamangalam, Giridhar; Tandon, Nitin; Aazhang, Behnaam

2016-10-01

In this paper, we developed a model-based and a data-driven estimator for directed information (DI) to infer the causal connectivity graph between electrocorticographic (ECoG) signals recorded from brain and to identify the seizure onset zone (SOZ) in epileptic patients. Directed information, an information theoretic quantity, is a general metric to infer causal connectivity between time-series and is not restricted to a particular class of models unlike the popular metrics based on Granger causality or transfer entropy. The proposed estimators are shown to be almost surely convergent. Causal connectivity between ECoG electrodes in five epileptic patients is inferred using the proposed DI estimators, after validating their performance on simulated data. We then proposed a model-based and a data-driven SOZ identification algorithm to identify SOZ from the causal connectivity inferred using model-based and data-driven DI estimators respectively. The data-driven SOZ identification outperforms the model-based SOZ identification algorithm when benchmarked against visual analysis by neurologist, the current clinical gold standard. The causal connectivity analysis presented here is the first step towards developing novel non-surgical treatments for epilepsy.

Clinical outcomes of VNS therapy with AspireSR® (including cardiac-based seizure detection) at a large complex epilepsy and surgery centre.

PubMed

Hamilton, Preci; Soryal, Imad; Dhahri, Prince; Wimalachandra, Welege; Leat, Anna; Hughes, Denise; Toghill, Nicole; Hodson, James; Sawlani, Vijay; Hayton, Tom; Samarasekera, Shanika; Bagary, Manny; McCorry, Dougall; Chelvarajah, Ramesh

2018-05-01

To compare the efficacy of AspireSR ® to preceding VNS battery models for battery replacements, and to determine the efficacy of the AspireSR ® for new implants. Data were collected retrospectively from patients with epilepsy who had VNS AspireSR ® implanted over a three-year period between June 2014 and June 2017 by a single surgeon. Cases were divided into two cohorts, those in whom the VNS was a new insertion, and those in whom the VNS battery was changed from a previous model to AspireSR ® . Within each group, the seizure burden was compared between the periods before and after insertion of AspireSR ® . Fifty-one patients with a newly inserted AspireSR ® VNS model had a significant reduction in seizure frequency (p < 0.001), with 59% (n = 30) reporting ≥50% reduction. Of the 62 patients who had an existing VNS, 53% (n = 33) reported ≥50% reduction in seizure burden when the original VNS was inserted. After the battery was changed to the AspireSR ® , 71% (n = 44) reported a further reduction of ≥50% in their seizure burden. The size of this reduction was at least as large as that resulting from the insertion of their existing VNS in 98% (61/62) of patients. The results suggest that approximately 70% of patients with existing VNS insertions could have significant additional benefit from cardiac based seizure detection and closed loop stimulation from the AspireSR ® device. For new insertions, the AspireSR ® device has efficacy in 59% of patients. The 'rule of thirds' used in counseling patients may need to be modified accordingly. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.

PubMed

Truong, Nhan Duy; Nguyen, Anh Duy; Kuhlmann, Levin; Bonyadi, Mohammad Reza; Yang, Jiawei; Ippolito, Samuel; Kavehei, Omid

2018-05-07

Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved high performance. However, to achieve high sensitivity and a low false prediction rate, many of these studies relied on handcraft feature extraction and/or tailored feature extraction, which is performed for each patient independently. This approach, however, is not generalizable, and requires significant modifications for each new patient within a new dataset. In this article, we apply convolutional neural networks to different intracranial and scalp electroencephalogram (EEG) datasets and propose a generalized retrospective and patient-specific seizure prediction method. We use the short-time Fourier transform on 30-s EEG windows to extract information in both the frequency domain and the time domain. The algorithm automatically generates optimized features for each patient to best classify preictal and interictal segments. The method can be applied to any other patient from any dataset without the need for manual feature extraction. The proposed approach achieves sensitivity of 81.4%, 81.2%, and 75% and a false prediction rate of 0.06/h, 0.16/h, and 0.21/h on the Freiburg Hospital intracranial EEG dataset, the Boston Children's Hospital-MIT scalp EEG dataset, and the American Epilepsy Society Seizure Prediction Challenge dataset, respectively. Our prediction method is also statistically better than an unspecific random predictor for most of the patients in all three datasets. Copyright © 2018 Elsevier Ltd. All rights reserved.

Pregabalin attenuates excitotoxicity in diabetes.

PubMed

Huang, Chin-Wei; Lai, Ming-Chi; Cheng, Juei-Tang; Tsai, Jing-Jane; Huang, Chao-Ching; Wu, Sheng-Nan

2013-01-01

Diabetes can exacerbate seizures and worsen seizure-related brain damage. In the present study, we aimed to determine whether the standard antiepileptic drug pregabalin (PGB) protects against pilocarpine-induced seizures and excitotoxicity in diabetes. Adult male Sprague-Dawley rats were divided into either a streptozotocin (STZ)-induced diabetes group or a normal saline (NS) group. Both groups were further divided into subgroups that were treated intravenously with either PGB (15 mg/kg) or a vehicle; all groups were treated with subcutaneous pilocarpine (60 mg/kg) to induce seizures. To evaluate spontaneous recurrent seizures (SRS), PGB-pretreated rats were fed rat chow containing oral PGB (450 mg) for 28 consecutive days; vehicle-pretreated rats were fed regular chow. SRS frequency was monitored for 2 weeks from post-status epilepticus day 15. We evaluated both acute neuronal loss and chronic mossy fiber sprouting in the CA3 area. In addition, we performed patch clamp recordings to study evoked excitatory postsynaptic currents (eEPSCs) in hippocampal CA1 neurons for both vehicle-treated rats with SRS. Finally, we used an RNA interference knockdown method for Kir6.2 in a hippocampal cell line to evaluate PGB's effects in the presence of high-dose ATP. We found that compared to vehicle-treated rats, PGB-treated rats showed less severe acute seizure activity, reduced acute neuronal loss, and chronic mossy fiber sprouting. In the vehicle-treated STZ rats, eEPSC amplitude was significantly lower after PGB administration, but glibenclamide reversed this effect. The RNA interference study confirmed that PGB could counteract the ATP-sensitive potassium channel (KATP)-closing effect of high-dose ATP. By opening KATP, PGB protects against neuronal excitotoxicity, and is therefore a potential antiepileptogenic in diabetes. These findings might help develop a clinical algorithm for treating patients with epilepsy and comorbid metabolic disorders.

Dynamic timecourse of typical childhood absence seizures: EEG, behavior and fMRI

PubMed Central

Bai, X; Vestal, M; Berman, R; Negishi, M; Spann, M; Vega, C; Desalvo, M; Novotny, EJ; Constable, RT; Blumenfeld, H

2010-01-01

Absence seizures are 5–10 second episodes of impaired consciousness accompanied by 3–4Hz generalized spike-and-wave discharge on electroencephalography (EEG). The timecourse of functional magnetic resonance imaging (fMRI) changes in absence seizures in relation to EEG and behavior is not known. We acquired simultaneous EEG-fMRI in 88 typical childhood absence seizures from 9 pediatric patients. We investigated behavior concurrently using a continuous performance task (CPT) or simpler repetitive tapping task (RTT). EEG time-frequency analysis revealed abrupt onset and end of 3–4 Hz spike-wave discharges with a mean duration of 6.6 s. Behavioral analysis also showed rapid onset and end of deficits associated with electrographic seizure start and end. In contrast, we observed small early fMRI increases in the orbital/medial frontal and medial/lateral parietal cortex >5s before seizure onset, followed by profound fMRI decreases continuing >20s after seizure end. This timecourse differed markedly from the hemodynamic response function (HRF) model used in conventional fMRI analysis, consisting of large increases beginning after electrical event onset, followed by small fMRI decreases. Other regions, such as the lateral frontal cortex, showed more balanced fMRI increases followed by approximately equal decreases. The thalamus showed delayed increases after seizure onset followed by small decreases, most closely resembling the HRF model. These findings reveal a complex and long lasting sequence of fMRI changes in absence seizures, which are not detectible by conventional HRF modeling in many regions. These results may be important mechanistically for seizure initiation and termination and may also contribute to changes in EEG and behavior. PMID:20427649

A meta-analysis of predictors of seizure freedom in the surgical management of focal cortical dysplasia.

PubMed

Rowland, Nathan C; Englot, Dario J; Cage, Tene A; Sughrue, Michael E; Barbaro, Nicholas M; Chang, Edward F

2012-05-01

Focal cortical dysplasia (FCD) is one of the most common causes of medically refractory epilepsy leading to surgery. However, seizure control outcomes reported in isolated surgical series are highly variable. As a result, it is not clear which variables are most crucial in predicting seizure freedom following surgery for FCD. The authors' aim was to determine the prognostic factors for seizure control in FCD by performing a meta-analysis of the published literature. A MEDLINE search of the published literature yielded 37 studies that met inclusion and exclusion criteria. Seven potential prognostic variables were determined from these studies and were dichotomized for analysis. For each variable, individual studies were weighted by inverse variance and combined to generate an odds ratio favoring seizure freedom. The methods complied with a standardized meta-analysis reporting protocol. Two thousand fourteen patients were included in the analysis. The overall rate of seizure freedom (Engel Class I) among patients undergoing surgery for FCD in the cohort of studies was 55.8% ± 16.2%. Partial seizures, a temporal location, detection with MRI, and a Type II Palmini histological classification were associated with higher rates of postoperative seizure control. As a treatment-related factor, complete resection of the anatomical or electrographic abnormality was the most important predictor overall of seizure freedom. Neither age nor electroencephalographic localization of the ictal onset significantly affected seizure freedom after surgery. Using a large population cohort pooled from the published literature, an analysis identified important factors that are prognostic in patients with epilepsy due to FCD. The most important of these factors-diagnostic imaging and resection-provide modalities through which improvements in the impact of FCD can be effected.

Assessment of the usefulness of magnetic resonance brain imaging in patients presenting with acute seizures.

PubMed

Olszewska, D A; Costello, D J

2014-12-01

Magnetic Resonance Imaging (MRI) is increasingly available as a tool for assessment of patients presenting to acute services with seizures. We set out to prospectively determine the usefulness of early MRI brain in a cohort of patients presenting with acute seizures. We examined the MR imaging studies performed in patients admitted solely because of acute seizures to Cork University Hospital over a 12-month period. The main aim of the study was to determine if the MRI established the proximate cause for the patient's recent seizure. We identified 91 patients who underwent MRI brain within 48 h of admission for seizures. Of the 91 studies, 51 were normal (56 %). The remaining 40 studies were abnormal as follows: microvascular disease (usually moderate/severe) (n = 19), post-traumatic gliosis (n = 7), remote symptomatic lesion (n = 6), primary brain tumour (n = 5), venous sinus thrombosis (n = 3), developmental lesion (n = 3), post-surgical gliosis (n = 3) and single cases of demyelination, unilateral hippocampal sclerosis, lobar haemorrhage and metastatic malignant melanoma. Abnormalities in diffusion-weighted sequences that were attributable to prolonged ictal activity were seen in nine patients, all of who had significant ongoing clinical deficits, most commonly delirium. Of the 40 patients with abnormal MRI studies, seven patients had unremarkable CT brain. MR brain imaging revealed the underlying cause for acute seizures in 44 % of patients. CT brain imaging failed to detect the cause of the acute seizures in 19 % of patients in whom subsequent MRI established the cause. This study emphasises the importance of obtaining optimal imaging in people admitted with acute seizures.

Cerebral perfusion alterations in epileptic patients during peri-ictal and post-ictal phase: PASL vs DSC-MRI.

PubMed

Pizzini, Francesca B; Farace, Paolo; Manganotti, Paolo; Zoccatelli, Giada; Bongiovanni, Luigi G; Golay, Xavier; Beltramello, Alberto; Osculati, Antonio; Bertini, Giuseppe; Fabene, Paolo F

2013-07-01

Non-invasive pulsed arterial spin labeling (PASL) MRI is a method to study brain perfusion that does not require the administration of a contrast agent, which makes it a valuable diagnostic tool as it reduces cost and side effects. The purpose of the present study was to establish the viability of PASL as an alternative to dynamic susceptibility contrast (DSC-MRI) and other perfusion imaging methods in characterizing changes in perfusion patterns caused by seizures in epileptic patients. We evaluated 19 patients with PASL. Of these, the 9 affected by high-frequency seizures were observed during the peri-ictal period (within 5hours since the last seizure), while the 10 patients affected by low-frequency seizures were observed in the post-ictal period. For comparison, 17/19 patients were also evaluated with DSC-MRI and CBF/CBV. PASL imaging showed focal vascular changes, which allowed the classification of patients in three categories: 8 patients characterized by increased perfusion, 4 patients with normal perfusion and 7 patients with decreased perfusion. PASL perfusion imaging findings were comparable to those obtained by DSC-MRI. Since PASL is a) sensitive to vascular alterations induced by epileptic seizures, b) comparable to DSC-MRI for detecting perfusion asymmetries, c) potentially capable of detecting time-related perfusion changes, it can be recommended for repeated evaluations, to identify the epileptic focus, and in follow-up and/or therapy-response assessment. Copyright © 2013 Elsevier Inc. All rights reserved.

Whole-brain MRI phenotyping in dysplasia-related frontal lobe epilepsy.

PubMed

Hong, Seok-Jun; Bernhardt, Boris C; Schrader, Dewi S; Bernasconi, Neda; Bernasconi, Andrea

2016-02-16

To perform whole-brain morphometry in patients with frontal lobe epilepsy and evaluate the utility of group-level patterns for individualized diagnosis and prognosis. We compared MRI-based cortical thickness and folding complexity between 2 frontal lobe epilepsy cohorts with histologically verified focal cortical dysplasia (FCD) (13 type I; 28 type II) and 41 closely matched controls. Pattern learning algorithms evaluated the utility of group-level findings to predict histologic FCD subtype, the side of the seizure focus, and postsurgical seizure outcome in single individuals. Relative to controls, FCD type I displayed multilobar cortical thinning that was most marked in ipsilateral frontal cortices. Conversely, type II showed thickening in temporal and postcentral cortices. Cortical folding also diverged, with increased complexity in prefrontal cortices in type I and decreases in type II. Group-level findings successfully guided automated FCD subtype classification (type I: 100%; type II: 96%), seizure focus lateralization (type I: 92%; type II: 86%), and outcome prediction (type I: 92%; type II: 82%). FCD subtypes relate to diverse whole-brain structural phenotypes. While cortical thickening in type II may indicate delayed pruning, a thin cortex in type I likely results from combined effects of seizure excitotoxicity and the primary malformation. Group-level patterns have a high translational value in guiding individualized diagnostics. © 2016 American Academy of Neurology.

CDKL5 variant in a boy with infantile epileptic encephalopathy: case report.

PubMed

Wong, Virginia Chun-Nei; Kwong, Anna Ka-Yee

2015-04-01

A Chinese boy presented at 18 months with intractable epilepsy, developmental delay and autistic features. He had multiple seizure types, including absence, myoclonic seizures, limb spasm and tonic seizures. His seizures were finally controlled at 3 years of age with clonazepam and a short course of chloral hydrate incidentally given for his insomnia. Subsequently, he had improvement in his communication skills. A novel hemizygous missense variant (c.1649G>A; p.R550Q) in exon 12 of CDKL5 gene was detected for him, his asymptomatic mother and elder sister. His phenotype is less severe than other male cases. We recommend screening CDKL5 for boys with pharmarco-resistant epilepsy and a trial of benzodiazepines for Infantile Epileptic Encephalopathy (IEE). Copyright © 2014 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks.

PubMed

Sareen, Sanjay; Sood, Sandeep K; Gupta, Sunil Kumar

2016-11-01

Epilepsy is one of the most common neurological disorders which is characterized by the spontaneous and unforeseeable occurrence of seizures. An automatic prediction of seizure can protect the patients from accidents and save their life. In this article, we proposed a mobile-based framework that automatically predict seizures using the information contained in electroencephalography (EEG) signals. The wireless sensor technology is used to capture the EEG signals of patients. The cloud-based services are used to collect and analyze the EEG data from the patient's mobile phone. The features from the EEG signal are extracted using the fast Walsh-Hadamard transform (FWHT). The Higher Order Spectral Analysis (HOSA) is applied to FWHT coefficients in order to select the features set relevant to normal, preictal and ictal states of seizure. We subsequently exploit the selected features as input to a k-means classifier to detect epileptic seizure states in a reasonable time. The performance of the proposed model is tested on Amazon EC2 cloud and compared in terms of execution time and accuracy. The findings show that with selected HOS based features, we were able to achieve a classification accuracy of 94.6 %.

Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG.

PubMed

Shanir, P P Muhammed; Khan, Kashif Ahmad; Khan, Yusuf Uzzaman; Farooq, Omar; Adeli, Hojjat

2017-12-01

Epileptic neurological disorder of the brain is widely diagnosed using the electroencephalography (EEG) technique. EEG signals are nonstationary in nature and show abnormal neural activity during the ictal period. Seizures can be identified by analyzing and obtaining features of EEG signal that can detect these abnormal activities. The present work proposes a novel morphological feature extraction technique based on the local binary pattern (LBP) operator. LBP provides a unique decimal value to a sample point by weighing the binary outcomes after thresholding the neighboring samples with the present sample point. These LBP values assist in capturing the rising and falling edges of the EEG signal, thus providing a morphologically featured discriminating pattern for epilepsy detection. In the present work, the variability in the LBP values is measured by calculating the sum of absolute difference of the consecutive LBP values. Interquartile range is calculated over the preprocessed EEG signal to provide dispersion measure in the signal. For classification purpose, K-nearest neighbor classifier is used, and the performance is evaluated on 896.9 hours of data from CHB-MIT continuous EEG database. Mean accuracy of 99.7% and mean specificity of 99.8% is obtained with average false detection rate of 0.47/h and sensitivity of 99.2% for 136 seizures.

Portable device for detection of petit mal epilepsy

NASA Technical Reports Server (NTRS)

Smith, R. G.; Houge, J. C.; Webster, J. G.

1979-01-01

A portable device that analyzes the electroencephalogram to determine if petit mal epilepsy waveforms are present is developed and tested. Clinicians should find it useful in diagnosing seizure activity of their patients. The micropower, battery-operated, portable device indicates a seizure has occurred if three criteria are satisfied: (1) frequencies of 2.5-7 Hz, (2) large-amplitude waves, and (3) minimum number of waves per second. Levels and counts are adjustable, thus insuring high reliability against noise artifacts and permitting each subject to be individually fitted. The device has shown promise in giving the patient a possible mechanism of seizure control or suppression.

IB-12LUNG MASS AND IMMUNOSUPPRESSANT RESPONSIVE SEIZURES: VGKC AUTOIMMUNITY MASQUERADING AS ETIOLOGY OR ACTING AS REACTIVE MARKER?

PubMed Central

Umemura, Yoshie; Bujarski, Krzysztof; Ronan, Lara

2014-01-01

Voltage gated potassium channel complex antibody (VGKC Ab) has been associated with many neurological illnesses including seizures. VGKC Ab related seizures are less responsive to antiepileptic drugs alone, and often require immunosuppression to achieve seizure freedom. Recently, specific antigenic targets such as LGI1 and CASPR2 within VGKC have been found and have been associated with specific syndromes. It has also been noted that the term "VGKC Ab" itself is problematic because it groups LGI1 and CASPR2 mediated disorders with other illnesses with unknown antigenic specificity. We describe a case of faciobrachial dystonia seizures refractory to multiple antiepileptic agents. Video EEG monitoring revealed mesial frontal epileptic discharges and serum test was positive for VGKC Ab. Patient's seizures were controlled after intravenous corticosteroid then oral mycophenolate maintenance, however, seizures returned with immunotherapy taper and cessation. When the seizures recurred, serum VGKC Ab, CSF CASPR 2 and LGI1 Abs were negative. Interestingly, she was subsequently found to have a lung mass consistent with sarcoidosis. Seizure control was achieved again with restarting of mycophenolate. Most VGKC Ab related illnesses are autoimmune even though it is a part of readily available commercial paraneoplastic testing panels. There has been no previously reported VGKC Ab related seizures associated with sarcoidosis. Although sarcoidosis is also an autoimmune entity, it is of T-cell mediated condition. It has been proposed that VGKC Ab may be a part of normal autoantibodies found in otherwise healthy individuals, which can rise to detectable levels under some circumstances. This patient suffered recurrent seizures responsive to immunotherapy even though VGKC, LGI1, and CASPR2 Abs were negative, suggesting another underlying autoimmune pathogenesis and VGKC Ab may have been a mere reactive marker of such underlying process. This raises the question of clinical significance of these antibodies and whether they are causative or reactive of the pathology.

Comparison Of Efficacy Of Phenytoin And Levetiracetam For Prevention Of Early Post Traumatic Seizures.

PubMed

Khan, Shahbaz Ali; Bhatti, Sajid Nazir; Khan, Aftab Alam; Khan Afridi, Ehtisham Ahmed; Muhammad, Gul; Gul, Nasim; Zadran, Khalid Khan; Alam, Sudhair; Aurangzeb, Ahsan

2016-01-01

The incidence of early post-traumatic seizures after civilian traumatic brain injury ranges 4-25%. The control of early post-traumatic seizure is mandatory because these acute insults may add secondary damage to the already damaged brain with poor outcome. Prophylactic use of anti-epileptic drugs have been found to be have variable efficacy against early post-traumatic seizures. The objective of this study was to compare the efficacy of Phenytion and Levetiracetam in prevention of early post-traumatic seizures in moderate to severe traumatic brain injury. This randomized controlled trial was conducted in department of Neurosurgery, Ayub Medical College, Abbottabad from March, 2012 to March 2013. The patients with moderate to severe head injury were randomly allocated in two groups. Patients in group A were given phenytoin and patients in group B were given Levetiracetam. Patients were followed for one week to detect efficacy of drug in terms of early post traumatic seizures. The 154 patients included in the study were equally divided into two groups. Out of 154 patients 115 (74.7%) were male while 29 (25.3%) were females. Age of patients ranges from 7-48 (24.15±9.56) years. Ninety one (59.1%) patients had moderate head injury while 63 (40.9%) patients had severe head injury. Phenytoin was effective in preventing early post traumatic seizures in 73 (94.8%) patients whereas Levetiracetam effectively controlled seizures in 70 (90.95%) cases (p-value of .348). There is no statistically significant difference in the efficacy of Phenytoin and Levetiracetam in prophylaxis of early posttraumatic seizures in cases of moderate to severe traumatic brain injury.

Antiepileptic drug therapy in patients with autoimmune epilepsy

PubMed Central

López Chiriboga, A. Sebastian; Britton, Jeffrey W.

2017-01-01

Objective: We aimed to report the pattern of usage and efficacy of antiepileptic drugs (AEDs) in patients with autoimmune epilepsy (AE). Methods: We retrospectively studied the Mayo Clinic's electronic medical record of patients with AE in which seizures were the main presenting feature. Clinical data, including demographics, seizure characteristics, type of AED and immunotherapy used, presence of neural antibody, and treatment outcomes, were reviewed. Results: The medical records of 252 adult patients diagnosed with autoimmune encephalitis and paraneoplastic disorders were reviewed. Seizure was the initial presentation in 50 patients (20%). Serum and/or CSF autoantibodies were detected in 41 (82%) patients, and 38 (76%) patients had neural autoantibodies. The majority (n = 43, 86%) received at least 1 form of immunotherapy in combination with AEDs, while the remainder received AEDs alone. Twenty-seven patients (54%) became seizure free: 18 (36%) with immunotherapy, 5 (10%) with AEDs alone, and 4 (8%) with AEDs after immunotherapy failure. Levetiracetam was the most commonly used (42/50); however, it was associated with 0% seizure-free response. AED seizure-free responses occurred with carbamazepine (n = 3) [3/16, 18.8%], lacosamide (n = 3) [3/18, 16.6%] with phenytoin (n = 1) [1/8, 12.5%], or oxcarbazepine (n = 2) [2/11, 18.1%]. Regardless of the type of therapy, voltage-gated potassium channel-complex antibody–positive patients were more likely to become seizure free compared with glutamic acid decarboxylase 65 antibody–positive cases (12/17 vs 2/10, p = 0.0183). Conclusions: In select patients, AEDs alone were effective in controlling seizures. AEDs with sodium channel blocking properties resulted in seizure freedom in a few cases. Prospective studies are needed to clarify AED selection and to elucidate their immunomodulatory properties in AE. PMID:28680914

Outcome of epilepsy surgery in focal cortical dysplasia

PubMed Central

Kral, T; Clusmann, H; Blumcke, I; Fimmers, R; Ostertun, B; Kurthen, M; Schramm, J

2003-01-01

Objective: To describe the outcome of surgery in patients with drug resistant epilepsy and a histopathological diagnosis of focal cortical dysplasia. Methods and subjects: Analysis of histories and presurgical and follow up data was carried out in 53 patients with a histological diagnosis of focal cortical dysplasia. Their mean age was 24.0 years (range 5 to 46), and they included 14 children and adolescents. Mean age at seizure onset was 12.4 years (0.4 to 36) and mean seizure duration was 11.6 years (1 to 45). Results: The presurgical detection rate of focal cortical dysplasia with magnetic resonance imaging (MRI) was 96%. There were 24 temporal and 29 extratemporal resections; additional multiple subpial transections were done in 12 cases to prevent spread of seizure discharges. There was a 6% rate of complications with permanent neurological deficit, but no deaths. All resected specimens were classified by neuropathological criteria as focal cortical dysplasia. Balloon cells were seen in most cases of extratemporal focal cortical dysplasia. After a mean follow up of 50 months, 38 patients (72%) were seizure-free, two (4%) had less than two seizures a year, nine (17%) had a reduction of seizure frequency of more than 75%, and four (8%) had no improvement. Seizure outcome was similar after temporal and extratemporal surgery. The patients in need of multilobar surgery had the poorest outcome. Conclusions: Circumscribed lesionectomy of focal dysplastic lesions provides seizure relief in patients with chronic drug resistant temporal and extratemporal epilepsy. There was a trend for the best seizure outcome to be in patients with early presurgical evaluation and early surgery, and in whom lesions were identified on the preoperative MRI studies. PMID:12531945

Framing Neuro-Glia Coupling in Antiepileptic Drug Design.

PubMed

Kardos, Julianna; Szabó, Zsolt; Héja, László

2016-02-11

We delineate perspectives for the design and discovery of antiepileptic drugs (AEDs) with fewer side effects by focusing on astroglial modulation of spatiotemporal seizure dynamics. It is now recognized that the major inhibitory neurotransmitter of the brain, γ-aminobutyric acid (GABA), can be released through the reversal of astroglial GABA transporters. Synaptic spillover and subsequent glutamate (Glu) uptake in neighboring astrocytes evoke replacement of extracellular Glu for GABA, driving neurons away from the seizure threshold. Attenuation of synaptic signaling by this negative feedback through the interplay of Glu and GABA transporters of adjacent astroglia can result in shortened seizures. By contrast, long-range activation of astroglia through gap junctions may promote recurrent seizures on the model of pharmacoresistant temporal lobe epilepsy. From their first detection to our current understanding, we identify various targets that shape both short- and long-range neuro-astroglia coupling, as these are manifest in epilepsy phenomena and in the associated research promotions of AED.

Brain invasion and the risk of seizures in patients with meningioma.

PubMed

Hess, Katharina; Spille, Dorothee Cäcilia; Adeli, Alborz; Sporns, Peter B; Brokinkel, Caroline; Grauer, Oliver; Mawrin, Christian; Stummer, Walter; Paulus, Werner; Brokinkel, Benjamin

2018-04-27

OBJECTIVE Identification of risk factors for perioperative epilepsy remains crucial in the care of patients with meningioma. Moreover, associations of brain invasion with clinical and radiological variables have been largely unexplored. The authors hypothesized that invasion of the cortex and subsequent increased edema facilitate seizures, and they compared radiological data and perioperative seizures in patients with brain-invasive or noninvasive meningioma. METHODS Correlations of brain invasion with tumor and edema volumes and preoperative and postoperative seizures were analyzed in univariate and multivariate analyses. RESULTS Totals of 108 (61%) females and 68 (39%) males with a median age of 60 years and harboring totals of 92 (52%) grade I, 79 (45%) grade II, and 5 (3%) grade III tumors were included. Brain invasion was found in 38 (22%) patients and was absent in 138 (78%) patients. The tumors were located at the convexity in 72 (41%) patients, at the falx cerebri in 26 (15%), at the skull base in 69 (39%), in the posterior fossa in 7 (4%), and in the ventricle in 2 (1%); the median tumor and edema volumes were 13.73 cm 3 (range 0.81-162.22 cm 3 ) and 1.38 cm 3 (range 0.00-355.80 cm 3 ), respectively. As expected, edema volume increased with rising tumor volume (p < 0.001). Brain invasion was independent of tumor volume (p = 0.176) but strongly correlated with edema volume (p < 0.001). The mean edema volume in noninvasive tumors was 33.0 cm 3 , but in invasive tumors, it was 130.7 cm 3 (p = 0.008). The frequency of preoperative seizures was independent of the patients' age, sex, and tumor location; however, the frequency was 32% (n = 12) in patients with invasive meningioma and 15% (n = 21) in those with noninvasive meningioma (p = 0.033). In contrast, the probability of detecting brain invasion microscopically was increased more than 2-fold in patients with a history of preoperative seizures (OR 2.57, 95% CI 1.13-5.88; p = 0.025). In univariate analyses, the rate of preoperative seizures correlated slightly with tumor volume (p = 0.049) but strongly with edema volume (p = 0.014), whereas seizure semiology was found to be independent of brain invasion (p = 0.211). In multivariate analyses adjusted for age, sex, tumor location, tumor and edema volumes, and WHO grade, rising tumor volume (OR 1.02, 95% CI 1.00-1.03; p = 0.042) and especially brain invasion (OR 5.26, 95% CI 1.52-18.15; p = 0.009) were identified as independent predictors of preoperative seizures. Nine (5%) patients developed new seizures within a median follow-up time of 15 months after surgery. Development of postoperative epilepsy was independent of all clinical variables, including Simpson grade (p = 0.133), tumor location (p = 0.936), brain invasion (p = 0.408), and preoperative edema volume (p = 0.081), but was correlated with increasing preoperative tumor volume (p = 0.004). Postoperative seizure-free rates were similar among patients with invasive and those with noninvasive meningioma (p = 0.372). CONCLUSIONS Brain invasion was identified as a new and strong predictor for preoperative, but not postoperative, seizures. Although also associated with increased peritumoral edema, seizures in patients with invasive meningioma might be facilitated substantially by cortical invasion itself. Consideration of seizures in consultations between the neurosurgeon and neuropathologist can improve the microscopic detection of brain invasion.

A Phase-Locked Loop Epilepsy Network Emulator

PubMed Central

Watson, P.D.; Horecka, K. M.; Cohen, N.J.; Ratnam, R.

2015-01-01

Most seizure forecasting employs statistical learning techniques that lack a representation of the network interactions that give rise to seizures. We present an epilepsy network emulator (ENE) that uses a network of interconnected phase-locked loops (PLLs) to model synchronous, circuit-level oscillations between electrocorticography (ECoG) electrodes. Using ECoG data from a canine-epilepsy model (Davis et al. 2011) and a physiological entropy measure (approximate entropy or ApEn, Pincus 1995), we demonstrate the entropy of the emulator phases increases dramatically during ictal periods across all ECoG recording sites and across all animals in the sample. Further, this increase precedes the observable voltage spikes that characterize seizure activity in the ECoG data. These results suggest that the ENE is sensitive to phase-domain information in the neural circuits measured by ECoG and that an increase in the entropy of this measure coincides with increasing likelihood of seizure activity. Understanding this unpredictable phase-domain electrical activity present in ECoG recordings may provide a target for seizure detection and feedback control. PMID:26664133

A Simple and Effective Physical Characteristic Profiling Method for Methamphetamine Tablet Seized in China.

PubMed

Li, Tao; Hua, Zhendong; Meng, Xin; Liu, Cuimei

2018-03-01

Methamphetamine (MA) tablet production confers chemical and physical properties. This study developed a simple and effective physical characteristic profiling method for MA tablets with capital letter "WY" logos, which realized the discrimination between linked and unlinked seizures. Seventeen signature distances extracted from the "WY" logo were explored as factors for multivariate analysis and demonstrated to be effective to represent the features of tablets in the drug intelligence perspective. Receiver operating characteristic (ROC) curve was used to evaluate efficiency of different pretreatments and distance/correlation metrics, while "Standardization + Euclidean" and "Logarithm + Euclidean" algorithms outperformed the rest. Finally, hierarchical cluster analysis (HCA) was applied to the data set of 200 MA tablet seizures randomly selected from cases all around China in 2015, and 76% of them were classified into a group named after "WY-001." Moreover, the "WY-001" tablets occupied 51-80% tablet seizures from 2011 to 2015 in China, indicating the existence of a huge clandestine factory incessantly manufacturing MA tablets. © 2017 American Academy of Forensic Sciences.

Spatiotemporal source analysis in scalp EEG vs. intracerebral EEG and SPECT: a case study in a 2-year-old child.

PubMed

Aarabi, A; Grebe, R; Berquin, P; Bourel Ponchel, E; Jalin, C; Fohlen, M; Bulteau, C; Delalande, O; Gondry, C; Héberlé, C; Moullart, V; Wallois, F

2012-06-01

This case study aims to demonstrate that spatiotemporal spike discrimination and source analysis are effective to monitor the development of sources of epileptic activity in time and space. Therefore, they can provide clinically useful information allowing a better understanding of the pathophysiology of individual seizures with time- and space-resolved characteristics of successive epileptic states, including interictal, preictal, postictal, and ictal states. High spatial resolution scalp EEGs (HR-EEG) were acquired from a 2-year-old girl with refractory central epilepsy and single-focus seizures as confirmed by intracerebral EEG recordings and ictal single-photon emission computed tomography (SPECT). Evaluation of HR-EEG consists of the following three global steps: (1) creation of the initial head model, (2) automatic spike and seizure detection, and finally (3) source localization. During the source localization phase, epileptic states are determined to allow state-based spike detection and localization of underlying sources for each spike. In a final cluster analysis, localization results are integrated to determine the possible sources of epileptic activity. The results were compared with the cerebral locations identified by intracerebral EEG recordings and SPECT. The results obtained with this approach were concordant with those of MRI, SPECT and distribution of intracerebral potentials. Dipole cluster centres found for spikes in interictal, preictal, ictal and postictal states were situated an average of 6.3mm from the intracerebral contacts with the highest voltage. Both amplitude and shape of spikes change between states. Dispersion of the dipoles was higher in the preictal state than in the postictal state. Two clusters of spikes were identified. The centres of these clusters changed position periodically during the various epileptic states. High-resolution surface EEG evaluated by an advanced algorithmic approach can be used to investigate the spatiotemporal characteristics of sources located in the epileptic focus. The results were validated by standard methods, ensuring good spatial resolution by MRI and SPECT and optimal temporal resolution by intracerebral EEG. Surface EEG can be used to identify different spike clusters and sources of the successive epileptic states. The method that was used in this study will provide physicians with a better understanding of the pathophysiological characteristics of epileptic activities. In particular, this method may be useful for more effective positioning of implantable intracerebral electrodes. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Interleukin-1 Receptor in Seizure Susceptibility after Traumatic Injury to the Pediatric Brain

PubMed Central

O'Brien, Terence J.; Gimlin, Kayleen; Wright, David K.; Kim, Shi Eun; Casillas-Espinosa, Pablo M.; Webster, Kyria M.; Petrou, Steven; Noble-Haeusslein, Linda J.

2017-01-01

Epilepsy after pediatric traumatic brain injury (TBI) is associated with poor quality of life. This study aimed to characterize post-traumatic epilepsy in a mouse model of pediatric brain injury, and to evaluate the role of interleukin-1 (IL-1) signaling as a target for pharmacological intervention. Male mice received a controlled cortical impact or sham surgery at postnatal day 21, approximating a toddler-aged child. Mice were treated acutely with an IL-1 receptor antagonist (IL-1Ra; 100 mg/kg, s.c.) or vehicle. Spontaneous and evoked seizures were evaluated from video-EEG recordings. Behavioral assays tested for functional outcomes, postmortem analyses assessed neuropathology, and brain atrophy was detected by ex vivo magnetic resonance imaging. At 2 weeks and 3 months post-injury, TBI mice showed an elevated seizure response to the convulsant pentylenetetrazol compared with sham mice, associated with abnormal hippocampal mossy fiber sprouting. A robust increase in IL-1β and IL-1 receptor were detected after TBI. IL-1Ra treatment reduced seizure susceptibility 2 weeks after TBI compared with vehicle, and a reduction in hippocampal astrogliosis. In a chronic study, IL-1Ra-TBI mice showed improved spatial memory at 4 months post-injury. At 5 months, most TBI mice exhibited spontaneous seizures during a 7 d video-EEG recording period. At 6 months, IL-1Ra-TBI mice had fewer evoked seizures compared with vehicle controls, coinciding with greater preservation of cortical tissue. Findings demonstrate this model's utility to delineate mechanisms underlying epileptogenesis after pediatric brain injury, and provide evidence of IL-1 signaling as a mediator of post-traumatic astrogliosis and seizure susceptibility. SIGNIFICANCE STATEMENT Epilepsy is a common cause of morbidity after traumatic brain injury in early childhood. However, a limited understanding of how epilepsy develops, particularly in the immature brain, likely contributes to the lack of efficacious treatments. In this preclinical study, we first demonstrate that a mouse model of traumatic injury to the pediatric brain reproduces many neuropathological and seizure-like hallmarks characteristic of epilepsy. Second, we demonstrate that targeting the acute inflammatory response reduces cognitive impairments, the degree of neuropathology, and seizure susceptibility, after pediatric brain injury in mice. These findings provide evidence that inflammatory cytokine signaling is a key process underlying epilepsy development after an acquired brain insult, which represents a feasible therapeutic target to improve quality of life for survivors. PMID:28724747

Ictal high frequency oscillations distinguish two types of seizure territories in humans

PubMed Central

Weiss, Shennan A.; Banks, Garrett P.; McKhann, Guy M.; Goodman, Robert R.; Emerson, Ronald G.; Trevelyan, Andrew J.

2013-01-01

High frequency oscillations have been proposed as a clinically useful biomarker of seizure generating sites. We used a unique set of human microelectrode array recordings (four patients, 10 seizures), in which propagating seizure wavefronts could be readily identified, to investigate the basis of ictal high frequency activity at the cortical (subdural) surface. Sustained, repetitive transient increases in high gamma (80–150 Hz) amplitude, phase-locked to the low-frequency (1–25 Hz) ictal rhythm, correlated with strong multi-unit firing bursts synchronized across the core territory of the seizure. These repetitive high frequency oscillations were seen in recordings from subdural electrodes adjacent to the microelectrode array several seconds after seizure onset, following ictal wavefront passage. Conversely, microelectrode recordings demonstrating only low-level, heterogeneous neural firing correlated with a lack of high frequency oscillations in adjacent subdural recording sites, despite the presence of a strong low-frequency signature. Previously, we reported that this pattern indicates a failure of the seizure to invade the area, because of a feedforward inhibitory veto mechanism. Because multi-unit firing rate and high gamma amplitude are closely related, high frequency oscillations can be used as a surrogate marker to distinguish the core seizure territory from the surrounding penumbra. We developed an efficient measure to detect delayed-onset, sustained ictal high frequency oscillations based on cross-frequency coupling between high gamma amplitude and the low-frequency (1–25 Hz) ictal rhythm. When applied to the broader subdural recording, this measure consistently predicted the timing or failure of ictal invasion, and revealed a surprisingly small and slowly spreading seizure core surrounded by a far larger penumbral territory. Our findings thus establish an underlying neural mechanism for delayed-onset, sustained ictal high frequency oscillations, and provide a practical, efficient method for using them to identify the small ictal core regions. Our observations suggest that it may be possible to reduce substantially the extent of cortical resections in epilepsy surgery procedures without compromising seizure control. PMID:24176977

Intraoperative MRI-guided resection of focal cortical dysplasia in pediatric patients: technique and outcomes.

PubMed

Sacino, Matthew F; Ho, Cheng-Ying; Murnick, Jonathan; Tsuchida, Tammy; Magge, Suresh N; Keating, Robert F; Gaillard, William D; Oluigbo, Chima O

2016-06-01

OBJECTIVE Previous meta-analysis has demonstrated that the most important factor in seizure freedom following surgery for focal cortical dysplasia (FCD) is completeness of resection. However, intraoperative detection of epileptogenic dysplastic cortical tissue remains a challenge, potentially leading to a partial resection and the need for reoperation. The objective of this study was to determine the role of intraoperative MRI (iMRI) in the intraoperative detection and localization of FCD as well as its impact on surgical decision making, completeness of resection, and seizure control outcomes. METHODS The authors retrospectively reviewed the medical records of pediatric patients who underwent iMRI-assisted resection of FCD at the Children's National Health System between January 2014 and April 2015. Data reviewed included demographics, length of surgery, details of iMRI acquisition, postoperative seizure freedom, and complications. Postsurgical seizure outcome was assessed utilizing the Engel Epilepsy Surgery Outcome Scale. RESULTS Twelve consecutive pediatric patients (8 females and 4 males) underwent iMRI-guided resection of FCD lesions. The mean age at the time of surgery was 8.8 years ± 1.6 years (range 0.7 to 18.8 years), and the mean duration of follow up was 3.5 months ± 1.0 month. The mean age at seizure onset was 2.8 years ± 1.0 year (range birth to 9.0 years). Two patients had Type 1 FCD, 5 patients had Type 2A FCD, 2 patients had Type 2B FCD, and 3 patients had FCD of undetermined classification. iMRI findings impacted intraoperative surgical decision making in 5 (42%) of the 12 patients, who then underwent further exploration of the resection cavity. At the time of the last postoperative follow-up, 11 (92%) of the 12 patients were seizure free (Engel Class I). No patients underwent reoperation following iMRI-guided surgery. CONCLUSIONS iMRI-guided resection of FCD in pediatric patients precluded the need for repeat surgery. Furthermore, it resulted in the achievement of complete resection in all the patients, leading to a high rate of postoperative seizure freedom.

Utilization of Quantitative EEG Trends for Critical Care Continuous EEG Monitoring: A Survey of Neurophysiologists.

PubMed

Swisher, Christa B; Sinha, Saurabh R

2016-12-01

Quantitative EEG (QEEG) can be used to assist with review of large amounts of data generated by critical care continuous EEG monitoring. This study aimed to identify current practices regarding the use of QEEG in critical care continuous EEG monitoring of critical care patients. An online survey was sent to 796 members of the American Clinical Neurophysiology Society (ACNS), instructing only neurophysiologists to participate. The survey was completed by 75 neurophysiologists that use QEEG in their practice. Survey respondents reported that neurophysiologists and neurophysiology fellows are most likely to serve as QEEG readers (97% and 52%, respectively). However, 21% of respondents reported nonneurophysiologists are also involved with QEEG interpretation. The majority of nonneurophysiologist QEEG data review is aimed to alert neurophysiologists to periods of concern, but 22% reported that nonneurophysiologists use QEEG to directly guide clinical care. Quantitative EEG was used most frequently for seizure detection (92%) and burst suppression monitoring (59%). A smaller number of respondents use QEEG for monitoring the depth of sedation (29%), ischemia detection (28%), vasospasm detection (28%) and prognosis after cardiac arrest (21%). About half of the respondents do not review every page of the raw critical care continuous EEG record when using QEEG. Respondents prefer a panel of QEEG trends displayed as hemispheric data, when applicable. There is substantial variability regarding QEEG trend preferences for seizure detection and ischemia detection. QEEG is being used by neurophysiologists and nonneurophysiologists for applications beyond seizure detection, but practice patterns vary widely. There is a need for standardization of QEEG methods and practices.

Epilepsy

MedlinePlus

... made great strides in detecting patterns of abnormal electrical activity in the brain that cause epileptic seizures. A technology to measure brain activity, called electroencephalography (EEG), became ...

Development of minimally invasive surgery for intractable epilepsy

NASA Astrophysics Data System (ADS)

Yamakawa, Takeshi

2009-04-01

Epilepsy is a chronic brain disorder characterized by recurrent seizures. The seizure is shot down by the surgical removal of the region which is so called "epileptogenc focus". However, the accuracy to detect the focus is not good (order of cm). Thus the extirpation of focus with significant margin causes the removal of normal brain and leads to the severe aftereffects such as restricted vision, motor dysfunction, disorder of memory, and so on. To cope with this problem, we should develop the technology of (1) detecting the epileptogenic focus, and (2) necrotizing the epileptogenic focus excluding normal brain by (a) colliquative necrosis with flash freezing and melting or (b) cauterizing by focused laser beam.

Surgical Management and Long-Term Seizure Outcome After Surgery for Temporal Lobe Epilepsy Associated with Cerebral Cavernous Malformations.

PubMed

Yang, Peng-Fan; Pei, Jia-Sheng; Jia, Yan-Zeng; Lin, Qiao; Xiao, Hui; Zhang, Ting-Ting; Zhong, Zhong-Hui

2018-02-01

Operative strategies for cerebral cavernous malformation (CCM)-associated temporal lobe epilepsy and timing of surgical intervention continue to be debated. This study aimed to establish an algorithm to evaluate the efficacy of surgical intervention strategies, to maximize positive surgical outcomes and minimize postsurgical neurologic deficits. 47 patients having undergone operation for CCM-associated temporal lobe epilepsy were retrospectively reviewed. They had received a diagnostic series for seizure localization, including long-term video electroencephalography (vEEG), high-resolution magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET-CT). In patients with mesial temporal lobe CCMs, the involved structures (amygdala, hippocampus, or parahippocampal gyrus) were resected in addition to the lesions. Patients with neocortical epileptogenic CCM underwent extended lesionectomy guided by intraoperative electrocorticography; further performance of amygdalohippocampectomy depended on the extent of hippocampal epileptogenicity. The study cohort contained 28 patients with drug-resistant epilepsy (DRE), 12 with chronic epilepsy (CE), and 7 with sporadic seizure (SS). Normal temporal lobe metabolism was seen in 7/7 patients of the SS group. Hypometabolism was found in all patients with chronic disease except for those with posterior inferior and middle temporal gyrus cavernous malformations (CMs). Of the 31 patients with superficial neocortical CCM, 7 had normal PET without hippocampal sclerosis, 14 had ipsilateral temporal lobe hypometabolism without hippocampal sclerosis, and 10 had obvious hippocampal sclerosis and hypometabolism. Seizure freedom in DRE, CE, and SS was 82.1%, 75%, and 100%, respectively. A significant difference was found between lesion laterality and postoperative seizure control; the rate was lower in left-sided cases because of less aggressive resection. Our study demonstrates that the data from the presurgical evaluation, particularly regarding CM location, responsiveness to antiepileptic drugs, and temporal lobe metabolism, are crucial parameters for choosing surgical approaches to CCM-associated temporal lobe epilepsy. By this operative strategy, patients may receive maximized seizure control and minimized postsurgical neurologic sequelae. Copyright © 2017 Elsevier Inc. All rights reserved.

Supervised filters for EEG signal in naturally occurring epilepsy forecasting.

PubMed

Muñoz-Almaraz, Francisco Javier; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma; Pardo, Juan

2017-01-01

Nearly 1% of the global population has Epilepsy. Forecasting epileptic seizures with an acceptable confidence level, could improve the disease treatment and thus the lifestyle of the people who suffer it. To do that the electroencephalogram (EEG) signal is usually studied through spectral power band filtering, but this paper proposes an alternative novel method of preprocessing the EEG signal based on supervised filters. Such filters have been employed in a machine learning algorithm, such as the K-Nearest Neighbor (KNN), to improve the prediction of seizures. The proposed solution extends with this novel approach an algorithm that was submitted to win the third prize of an international Data Science challenge promoted by Kaggle contest platform and the American Epilepsy Society, the Epilepsy Foundation, National Institutes of Health (NIH) and Mayo Clinic. A formal description of these preprocessing methods is presented and a detailed analysis in terms of Receiver Operating Characteristics (ROC) curve and Area Under ROC curve is performed. The obtained results show statistical significant improvements when compared with the spectral power band filtering (PBF) typical baseline. A trend between performance and the dataset size is observed, suggesting that the supervised filters bring better information, compared to the conventional PBF filters, as the dataset grows in terms of monitored variables (sensors) and time length. The paper demonstrates a better accuracy in forecasting when new filters are employed and its main contribution is in the field of machine learning algorithms to develop more accurate predictive systems.

Supervised filters for EEG signal in naturally occurring epilepsy forecasting

PubMed Central

2017-01-01

Nearly 1% of the global population has Epilepsy. Forecasting epileptic seizures with an acceptable confidence level, could improve the disease treatment and thus the lifestyle of the people who suffer it. To do that the electroencephalogram (EEG) signal is usually studied through spectral power band filtering, but this paper proposes an alternative novel method of preprocessing the EEG signal based on supervised filters. Such filters have been employed in a machine learning algorithm, such as the K-Nearest Neighbor (KNN), to improve the prediction of seizures. The proposed solution extends with this novel approach an algorithm that was submitted to win the third prize of an international Data Science challenge promoted by Kaggle contest platform and the American Epilepsy Society, the Epilepsy Foundation, National Institutes of Health (NIH) and Mayo Clinic. A formal description of these preprocessing methods is presented and a detailed analysis in terms of Receiver Operating Characteristics (ROC) curve and Area Under ROC curve is performed. The obtained results show statistical significant improvements when compared with the spectral power band filtering (PBF) typical baseline. A trend between performance and the dataset size is observed, suggesting that the supervised filters bring better information, compared to the conventional PBF filters, as the dataset grows in terms of monitored variables (sensors) and time length. The paper demonstrates a better accuracy in forecasting when new filters are employed and its main contribution is in the field of machine learning algorithms to develop more accurate predictive systems. PMID:28632737

Characteristic MRI findings in hyperglycaemia-induced seizures: diagnostic value of contrast-enhanced fluid-attenuated inversion recovery imaging.

PubMed

Lee, E J; Kim, K K; Lee, E K; Lee, J E

2016-12-01

To describe characteristic magnetic resonance imaging (MRI) abnormalities in hyperglycaemia-induced seizures, and evaluate the diagnostic value of contrast-enhanced fluid-attenuated inversion recovery (FLAIR) imaging. Possible underlying mechanisms of this condition are also discussed. Eleven patients with hyperglycaemia-induced seizures and MRI abnormalities were retrospectively studied. Clinical manifestations, laboratory findings, MRI findings, and clinical outcomes were analysed. All patients, except one, presented with focal seizures, simple or complex partial seizures, or negative motor seizures. All patients had long-standing uncontrolled diabetes mellitus. The MRI abnormalities observed acutely were focal subcortical hypointensities on T2-weighted imaging and FLAIR imaging in all patients with overlying cortical gyral T2 hyperintensities in five. Focal overlying cortical or leptomeningeal enhancement on contrast-enhanced T1-weighted imaging or contrast-enhanced FLAIR imaging was observed in all patients. Contrast-enhanced FLAIR imaging was superior to contrast-enhanced T1-weighted imaging for detecting characteristic cortical or leptomeningeal enhancement. Diffusion-weighted imaging showed mildly restricted diffusion in four of five patients with cortical gyral T2 hyperintensity. In nine patients, the lesions were localised in the parietal or parieto-occipital lobes. The other two patients showed localised precentral gyral lesions. After treatment, the neurological symptoms, including the seizures, improved in all patients. On clinical recovery, the subcortical T2 hypointensity, gyral or leptomeningeal enhancement, and overlying cortical T2 hyperintensities resolved. Recognition of these radiological abnormalities in patients with hyperglycaemia-induced seizures is important in restricting unwarranted investigations and initiating early therapy. These patients generally have a good prognosis. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Genetic Deletion of the Neuronal Glutamate Transporter, EAAC1, Results in Decreased Neuronal Death after Pilocarpine-Induced Status Epilepticus

PubMed Central

Lane, Meredith C.; Jackson, Joshua G.; Krizman, Elizabeth N.; Rothstein, Jeffery D.; Porter, Brenda E.; Robinson, Michael B.

2014-01-01

Excitatory amino acid carrier 1 (EAAC1, also called EAAT3) is a Na+-dependent glutamate transporter expressed by both glutamatergic and GABAergic neurons. It provides precursors for the syntheses of glutathione and GABA and contributes to the clearance of synaptically released glutamate. Mice deleted of EAAC1 are more susceptible to neurodegeneration in models of ischemia, Parkinson’s disease, and aging. Antisense knock-down of EAAC1 causes an absence seizure-like phenotype. Additionally, EAAC1 expression increases after chemonvulsant-induced seizures in rodent models and in tissue specimens from patients with refractory epilepsy. The goal of the present study was to determine if the absence of EAAC1 affects the sensitivity of mice to seizure-induced cell death. A chemoconvulsant dose of pilocarpine was administered to EAAC1−/− mice and to wild-type controls. Although EAAC1−/− mice experienced increased latency to seizure onset, no significant differences in behavioral seizure severity or mortality were observed. We examined EAAC1 immunofluorescence 24 hours after pilocarpine administration and confirmed that pilocarpine causes an increase in EAAC1 protein. Forty-eight hours after induction of seizures, cell death was measured in hippocampus and in cortex using Fluoro-Jade C. Surprisingly, there was ~2-fold more cell death in area CA1 of wild-type mice than in the corresponding regions of the EAAC1−/− mice. Together, these studies indicate that absence of EAAC1 results in either a decrease in pilocarpine-induced seizures that is not detectable by behavioral criteria (surprising, since EAAC1 provides glutamate for GABA synthesis), or that the absence of EAAC1 results in less pilocarpine/seizure-induced cell death, possible explanations as discussed. PMID:24334055

Selective head cooling during neonatal seizures prevents postictal cerebral vascular dysfunction without reducing epileptiform activity

PubMed Central

Harsono, Mimily; Pourcyrous, Massroor; Jolly, Elliott J.; de Jongh Curry, Amy; Fedinec, Alexander L.; Liu, Jianxiong; Basuroy, Shyamali; Zhuang, Daming; Leffler, Charles W.

2016-01-01

Epileptic seizures in neonates cause cerebrovascular injury and impairment of cerebral blood flow (CBF) regulation. In the bicuculline model of seizures in newborn pigs, we tested the hypothesis that selective head cooling prevents deleterious effects of seizures on cerebral vascular functions. Preventive or therapeutic ictal head cooling was achieved by placing two head ice packs during the preictal and/or ictal states, respectively, for the ∼2-h period of seizures. Head cooling lowered the brain and core temperatures to 25.6 ± 0.3 and 33.5 ± 0.1°C, respectively. Head cooling had no anticonvulsant effects, as it did not affect the bicuculline-evoked electroencephalogram parameters, including amplitude, duration, spectral power, and spike frequency distribution. Acute and long-term cerebral vascular effects of seizures in the normothermic and head-cooled groups were tested during the immediate (2–4 h) and delayed (48 h) postictal periods. Seizure-induced cerebral vascular injury during the immediate postictal period was detected as terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling-positive staining of cerebral arterioles and a surge of brain-derived circulating endothelial cells in peripheral blood in the normothermic group, but not in the head-cooled groups. During the delayed postictal period, endothelium-dependent cerebral vasodilator responses were greatly reduced in the normothermic group, indicating impaired CBF regulation. Preventive or therapeutic ictal head cooling mitigated the endothelial injury and greatly reduced loss of postictal cerebral vasodilator functions. Overall, head cooling during seizures is a clinically relevant approach to protecting the neonatal brain by preventing cerebrovascular injury and the loss of the endothelium-dependent control of CBF without reducing epileptiform activity. PMID:27591217

Long term effects on epileptiform activity with vagus nerve stimulation in children.

PubMed

Hallböök, Tove; Lundgren, Johan; Blennow, Gösta; Strömblad, Lars-Göran; Rosén, Ingmar

2005-12-01

We report long-term effects of vagus nerve stimulation (VNS) on epileptiform activity in 15 children, and how these changes are related to activity stage and to clinical effects on seizure reduction, seizure severity (NHS3) and quality of life (QOL). Initially, and after 3 and 9 months of VNS-treatment, 15 children were investigated with 24 h ambulatory EEG monitoring for spike detection. The number of interictal epileptiform discharges (IEDs) and the inter spike intervals (ISIs) were analysed during 2 h in the awake state, and 1h of rapid eye movement (REM)-, spindle- and delta-sleep, respectively. Total number and duration of electrographic seizure episodes were also analysed. At 9 months the total number of IEDs was significantly reduced (p=0.04). There was a tendency of reduction in all activity stages, and significantly so in delta-sleep (p=0.008). Total electrographic seizure number was significantly reduced in the 24 h EEG at 3 and 9 months (p=0.03, 0.05). There was a significant concordance in direction of changes in epileptiform activity and electrographic seizures at 9 months (p=0.04). Concordance in direction of changes was seen in 9 of 15 children between clinical seizures and IED (p>0.3), in 10 of 15 children between QOL and IED (p=0.3) and in 8 of 15 children between NHS3 and IED (p>0.3). There was no direct correlation between the extent of improvement in these clinical data and the degree of spike reduction. This study shows that VNS reduces IEDs especially in REM and delta sleep, as well as the number of electrographic seizures. It also shows a concordance between reduction in IEDs and electrographic seizures.

Ethosuximide and Phenytoin Dose-Dependently Attenuate Acute Nonconvulsive Seizures after Traumatic Brain Injury in Rats

PubMed Central

Shear, Deborah A.; Potter, Brittney; Marcsisin, Sean R.; Sousa, Jason; Melendez, Victor; Tortella, Frank C.; Lu, Xi-Chun M.

2013-01-01

Abstract Acute seizures frequently occur following severe traumatic brain injury (TBI) and have been associated with poor patient prognosis. Silent or nonconvulsive seizures (NCS) manifest in the absence of motor convulsion, can only be detected via continuous electroencephalographic (EEG) recordings, and are often unidentified and untreated. Identification of effective anti-epileptic drugs (AED) against post-traumatic NCS remains crucial to improve neurological outcome. Here, we assessed the anti-seizure profile of ethosuximide (ETX, 12.5–187.5 mg/kg) and phenytoin (PHT, 5–30 mg/kg) in a spontaneously occurring NCS model associated with penetrating ballistic-like brain injury (PBBI). Rats were divided between two drug cohorts, PHT or ETX, and randomly assigned to one of four doses or vehicle within each cohort. Following PBBI, NCS were detected by continuous EEG monitoring for 72 h post-injury. Drug efficacy was evaluated on NCS parameters of incidence, frequency, episode duration, total duration, and onset latency. Both PHT and ETX attenuated NCS in a dose-dependent manner. In vehicle-treated animals, 69–73% experienced NCS (averaging 9–10 episodes/rat) with average onset of NCS occurring at 30 h post-injury. Compared with control treatment, the two highest PHT and ETX doses significantly reduced NCS incidence to 13–40%, reduced NCS frequency (1.8–6.2 episodes/rat), and delayed seizure onset: <20% of treated animals exhibited NCS within the first 48 h. NCS durations were also dose-dependently mitigated. For the first time, we demonstrate that ETX and PHT are effective against spontaneously occurring NCS following PBBI, and suggest that these AEDs may be effective at treating post-traumatic NCS. PMID:23822888

Mapping and mining interictal pathological gamma (30–100 Hz) oscillations with clinical intracranial EEG in patients with epilepsy

PubMed Central

Smart, Otis; Maus, Douglas; Marsh, Eric; Dlugos, Dennis; Litt, Brian; Meador, Kimford

2012-01-01

Localizing an epileptic network is essential for guiding neurosurgery and antiepileptic medical devices as well as elucidating mechanisms that may explain seizure-generation and epilepsy. There is increasing evidence that pathological oscillations may be specific to diseased networks in patients with epilepsy and that these oscillations may be a key biomarker for generating and indentifying epileptic networks. We present a semi-automated method that detects, maps, and mines pathological gamma (30–100 Hz) oscillations (PGOs) in human epileptic brain to possibly localize epileptic networks. We apply the method to standard clinical iEEG (<100 Hz) with interictal PGOs and seizures from six patients with medically refractory epilepsy. We demonstrate that electrodes with consistent PGO discharges do not always coincide with clinically determined seizure onset zone (SOZ) electrodes but at times PGO-dense electrodes include secondary seizure-areas (SS) or even areas without seizures (NS). In 4/5 patients with epilepsy surgery, we observe poor (Engel Class 4) post-surgical outcomes and identify more PGO-activity in SS or NS than in SOZ. Additional studies are needed to further clarify the role of PGOs in epileptic brain. PMID:23105174

Dynamic analysis of heartbeat rate signals of epileptics using multidimensional phase space reconstruction approach

NASA Astrophysics Data System (ADS)

Su, Zhi-Yuan; Wu, Tzuyin; Yang, Po-Hua; Wang, Yeng-Tseng

2008-04-01

The heartbeat rate signal provides an invaluable means of assessing the sympathetic-parasympathetic balance of the human autonomic nervous system and thus represents an ideal diagnostic mechanism for detecting a variety of disorders such as epilepsy, cardiac disease and so forth. The current study analyses the dynamics of the heartbeat rate signal of known epilepsy sufferers in order to obtain a detailed understanding of the heart rate pattern during a seizure event. In the proposed approach, the ECG signals are converted into heartbeat rate signals and the embedology theorem is then used to construct the corresponding multidimensional phase space. The dynamics of the heartbeat rate signal are then analyzed before, during and after an epileptic seizure by examining the maximum Lyapunov exponent and the correlation dimension of the attractors in the reconstructed phase space. In general, the results reveal that the heartbeat rate signal transits from an aperiodic, highly-complex behaviour before an epileptic seizure to a low dimensional chaotic motion during the seizure event. Following the seizure, the signal trajectories return to a highly-complex state, and the complex signal patterns associated with normal physiological conditions reappear.

Electroencephalography in Mesial Temporal Lobe Epilepsy: A Review

PubMed Central

Javidan, Manouchehr

2012-01-01

Electroencephalography (EEG) has an important role in the diagnosis and classification of epilepsy. It can provide information for predicting the response to antiseizure drugs and to identify the surgically remediable epilepsies. In temporal lobe epilepsy (TLE) seizures could originate in the medial or lateral neocortical temporal region, and many of these patients are refractory to medical treatment. However, majority of patients have had excellent results after surgery and this often relies on the EEG and magnetic resonance imaging (MRI) data in presurgical evaluation. If the scalp EEG data is insufficient or discordant, invasive EEG recording with placement of intracranial electrodes could identify the seizure focus prior to surgery. This paper highlights the general information regarding the use of EEG in epilepsy, EEG patterns resembling epileptiform discharges, and the interictal, ictal and postictal findings in mesial temporal lobe epilepsy using scalp and intracranial recordings prior to surgery. The utility of the automated seizure detection and computerized mathematical models for increasing yield of non-invasive localization is discussed. This paper also describes the sensitivity, specificity, and predictive value of EEG for seizure recurrence after withdrawal of medications following seizure freedom with medical and surgical therapy. PMID:22957235

Early-onset seizures due to mosaic exonic deletions of CDKL5 in a male and two females.

PubMed

Bartnik, Magdalena; Derwińska, Katarzyna; Gos, Monika; Obersztyn, Ewa; Kołodziejska, Katarzyna E; Erez, Ayelet; Szpecht-Potocka, Agnieszka; Fang, Ping; Terczyńska, Iwona; Mierzewska, Hanna; Lohr, Naomi J; Bellus, Gary A; Reimschisel, Tyler; Bocian, Ewa; Mazurczak, Tadeusz; Cheung, Sau Wai; Stankiewicz, Paweł

2011-05-01

Mutations in the CDKL5 gene have been associated with an X-linked dominant early infantile epileptic encephalopathy-2. The clinical presentation is usually of severe encephalopathy with refractory seizures and Rett syndrome (RTT)-like phenotype. We attempted to assess the role of mosaic intragenic copy number variation in CDKL5. We have used comparative genomic hybridization with a custom-designed clinical oligonucleotide array targeting exons of selected disease and candidate genes, including CDKL5. We have identified mosaic exonic deletions of CDKL5 in one male and two females with developmental delay and medically intractable seizures. These three mosaic changes represent 60% of all deletions detected in 12,000 patients analyzed by array comparative genomic hybridization and involving the exonic portion of CDKL5. We report the first case of an exonic deletion of CDKL5 in a male and emphasize the importance of underappreciated mosaic exonic copy number variation in patients with early-onset seizures and RTT-like features of both genders.

Human photosensitivity: from pathophysiology to treatment.

PubMed

Verrotti, A; Tocco, A M; Salladini, C; Latini, G; Chiarelli, F

2005-11-01

Photosensitivity is a condition detected on the electroencephalography (EEG) as a paroxysmal reaction to Intermittent Photic Stimulation (IPS). This EEG response, elicited by IPS or by other visual stimuli of daily life, is called Photo Paroxysmal Response (PPR). PPRs are well documented in epileptic and non-epileptic subjects. Photosensitivity rarely in normal individuals evolves into epilepsy. Photosensitive epilepsy is a rare refex epilepsy characterized by seizures in photosensitive individuals. The development of modern technology has increased the exposition to potential seizure precipitants in people of all ages, but especially in children and adolescents. Actually, videogames, computers and televisions are the most common triggers in daily life of susceptible persons. The mechanisms of generation of PPR are poorly understood, but genetic factors play an important rule. The control of visually induced seizures has, generally a good prognosis. In patients known to be visually sensitive, avoidance of obvious source and stimulus modifications are very important and useful to seizure prevention, but in the large majority of patients with epilepsy and photosensitivity antiepileptic drugs are needed.

Identification of serum miRNAs differentially expressed in human epilepsy at seizure onset and post-seizure.

PubMed

Sun, Jijun; Cheng, Weidong; Liu, Lifeng; Tao, Shuxin; Xia, Zhangyong; Qi, Lifeng; Huang, Min

2016-12-01

MicroRNAs (miRNAs) function as potential novel biomarkers for disease detection due to their marked stability in the blood and the characteristics of their expression profile in several diseases. In the present study, microarray‑based serum miRNA profiling was performed on serum obtained from three patients with epilepsy at diagnosis and from three healthy individuals as controls. This was followed by reverse transcription‑quantitative polymerase chain reaction analysis in a separate cohort of 35 health volunteers and 90 patients with epilepsy. The correlations between miRNAs and clinical parameters were analyzed. The array results showed that 15 miRNAs were overexpressed and 10 miRNAs were underexpressed (>2‑fold) in the patients with epilepsy. In addition, four miRNAs, including miR‑30a, miR‑378, miR‑106b and miR‑15a were found to be overexpressed in the serum of patients at seizure onset, compared with post‑seizure. When the patients were at seizure onset, the expression of miR‑30a was positively associated with seizure frequency. No significant differences were found between miR‑30a and gender, age or number of years following diagnosis. The expression levels of miR‑378, miR‑106b and mir‑15a were not associated with the clinical parameters in the patients with seizures. Calcium/calmodulin‑dependent protein kinase type IV was a target of miR‑30a, and its expression was increased following seizure and was negatively correlated with miR‑30a in the patients with epilepsy. The present study provided the first evidence, to the best of our knowledge, that the expression levels of miR‑378, miR‑30a, miR‑106b and miR‑15a were enhanced in epileptic patients with seizures. miR-30a may be useful for prognostic prediction in epilepsy.

Combined process automation for large-scale EEG analysis.

PubMed

Sfondouris, John L; Quebedeaux, Tabitha M; Holdgraf, Chris; Musto, Alberto E

2012-01-01

Epileptogenesis is a dynamic process producing increased seizure susceptibility. Electroencephalography (EEG) data provides information critical in understanding the evolution of epileptiform changes throughout epileptic foci. We designed an algorithm to facilitate efficient large-scale EEG analysis via linked automation of multiple data processing steps. Using EEG recordings obtained from electrical stimulation studies, the following steps of EEG analysis were automated: (1) alignment and isolation of pre- and post-stimulation intervals, (2) generation of user-defined band frequency waveforms, (3) spike-sorting, (4) quantification of spike and burst data and (5) power spectral density analysis. This algorithm allows for quicker, more efficient EEG analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

[A case of non-photosensitive, self-induced epileptic seizures with pacygyria].

PubMed

Nagai, H; Shikata, A; Sato, N; Takeuchi, Y; Sawada, T

1998-09-01

We report an 11-year-old boy with a non-photosensitive epileptic self-induced seizures, pacygyria and familial ataxia. His grandmother and aunts had dysarthria, and his mother had developed progressive ataxia and myoclonus since 40 years old. His older sister had ataxia, mental retardation and epilepsy. As for the boy, motor developmental delay with muscle hypertonicity of left extremities was recognized at the age of 5 months. Mental retardation and ataxia was recognized at the age of 3 years and slight mental regression is recognized at the age of 11 years. No special findings were detected in an examination of his blood and cerebrospinal fluid, including amino acids, lysosomal enzymes activity and genetic analysis for dentatorubralpallidoluysian atrophy. Brain magnetic resonance imaging revealed pachygyria of the right cerebral cortecies. At the age of two, he began to induce seizures with impairment of consciousness in himself by waving his right hand over his face which was directed toward a source of bright light. At the age of seven, he developed spontaneous seizures with impairment of consciousness. An EEG showed frequent spikes in the occipital areas, on the right and left sides occurring either independently or synchronously. Intermittent photic stimulation and pattern stimulation did not induce a paroxysmal discharge in EEG. Ictal EEG suggested that the origin of the seizures was the occipital lobe. Treatment with valporate and zonisamide was effective in reducing the seizures. The findings of our case imply the pathogenesis of self-induced seizures and the relationship between PME and neuronal migration disorders.

Neural Progenitor Cells Rptor Ablation Impairs Development but Benefits to Seizure-Induced Behavioral Abnormalities.

PubMed

Chen, Ling-Lin; Wu, Mei-Ling; Zhu, Feng; Kai, Jie-Jing; Dong, Jing-Yin; Wu, Xi-Mei; Zeng, Ling-Hui

2016-12-01

Previous study suggests that mTOR signaling pathway may play an important role in epileptogenesis. The present work was designed to explore the contribution of raptor protein to the development of epilepsy and comorbidities. Mice with conditional knockout of raptor protein were generated by cross-bred Rptor flox/flox mice with nestin-CRE mice. The expression of raptor protein was analyzed by Western blotting in brain tissue samples. Neuronal death and mossy fiber sprouting were detected by FJB staining and Timm staining, respectively. Spontaneous seizures were recorded by EEG-video system. Morris water maze, open field test, and excitability test were used to study the behaviors of Rptor CKO mice. As the consequence of deleting Rptor, downstream proteins of raptor in mTORC1 signaling were partly blocked. Rptor CKO mice exhibited decrease in body and brain weight under 7 weeks old and accordingly, cortical layer thickness. After kainic acid (KA)-induced status epilepticus, overactivation of mTORC1 signaling was markedly reversed in Rptor CKO mice. Although low frequency of spontaneous seizure and seldom neuronal cell death were observed in both Rptor CKO and control littermates, KA seizure-induced mossy fiber spouting were attenuated in Rptor CKO mice. Additionally, cognitive-deficit and anxiety-like behavior after KA-induced seizures were partly reversed in Rptor CKO mice. Loss of the Rptor gene in mice neural progenitor cells affects normal development in young age and may contribute to alleviate KA seizure-induced behavioral abnormalities, suggesting that raptor protein plays an important role in seizure comorbidities. © 2016 John Wiley & Sons Ltd.

Distribution entropy analysis of epileptic EEG signals.

PubMed

Li, Peng; Yan, Chang; Karmakar, Chandan; Liu, Changchun

2015-01-01

It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p <; 0.05) increased DistEn for the interictal class in compassion with the normal class, whereas both analyses using relatively long EEG signals failed in tracking this difference between them, which may be due to a nonstationarity effect on entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.

Markerless video analysis for movement quantification in pediatric epilepsy monitoring.

PubMed

Lu, Haiping; Eng, How-Lung; Mandal, Bappaditya; Chan, Derrick W S; Ng, Yen-Ling

2011-01-01

This paper proposes a markerless video analytic system for quantifying body part movements in pediatric epilepsy monitoring. The system utilizes colored pajamas worn by a patient in bed to extract body part movement trajectories, from which various features can be obtained for seizure detection and analysis. Hence, it is non-intrusive and it requires no sensor/marker to be attached to the patient's body. It takes raw video sequences as input and a simple user-initialization indicates the body parts to be examined. In background/foreground modeling, Gaussian mixture models are employed in conjunction with HSV-based modeling. Body part detection follows a coarse-to-fine paradigm with graph-cut-based segmentation. Finally, body part parameters are estimated with domain knowledge guidance. Experimental studies are reported on sequences captured in an Epilepsy Monitoring Unit at a local hospital. The results demonstrate the feasibility of the proposed system in pediatric epilepsy monitoring and seizure detection.

Real-time phase correlation based integrated system for seizure detection

NASA Astrophysics Data System (ADS)

Romaine, James B.; Delgado-Restituto, Manuel; Leñero-Bardallo, Juan A.; Rodríguez-Vázquez, Ángel

2017-05-01

This paper reports a low area, low power, integer-based digital processor for the calculation of phase synchronization between two neural signals. The processor calculates the phase-frequency content of a signal by identifying the specific time periods associated with two consecutive minima. The simplicity of this phase-frequency content identifier allows for the digital processor to utilize only basic digital blocks, such as registers, counters, adders and subtractors, without incorporating any complex multiplication and or division algorithms. In fact, the processor, fabricated in a 0.18μm CMOS process, only occupies an area of 0.0625μm2 and consumes 12.5nW from a 1.2V supply voltage when operated at 128kHz. These low-area, low-power features make the proposed processor a valuable computing element in closed loop neural prosthesis for the treatment of neural diseases, such as epilepsy, or for extracting functional connectivity maps between different recording sites in the brain.

Substance Abuse and the HIV Situation in Malaysia

PubMed Central

Singh, Darshan; Chawarski, Marek C.; Schottenfeld, Richard; Vicknasingam, Balasingam

2014-01-01

Heroin continues to be the main drug used in Malaysia, while amphetamine-type stimulants (ATS) have been recently identified as a growing problem. A cumulative total of 300,241 drug users were detected between 1988 and 2006. It is also estimated that Malaysia has 170,000 injecting drug users. HIV prevalence among drug users in the country ranges from 25% to 45%. Currently, there are approximately 380 general medical practice offices that offer agonist maintenance treatments for approximately 10,000 patients. There are 27,756 active patients in 333 general medical practice offices and government-run methadone maintenance treatment (MMT) centers. The Needle Syringe Exchange Program (NSEP) reached out to 34,244 injection drug users (IDUs) in 2011. In the last 2 years (2011 and 2012) the number of detected drug addicts decreased from 11,194 to 9015. The arrests made by the police related to opiate and cannabis use increased from 41,363 to 63,466 between the years 2008 and 2010, but decreased since 2010. An almost four-fold increase in the number of ATS and ketamine users was detected from 2006 (21,653 users) 2012 (76,812). Between 2004 and 2010, the yearly seizures for heroin ranged between 156 to 270 kg. However, in 2010 and 2011, heroin seizures showed a significant increase of 445kg and 410.02 kg, respectively. There has been a seizure of between 600 to 1000kg of syabu yearly from 2009 to 2012. Similar to heroin, increased seizures for Yaba have also been observed over the last 2 years. A significant increase has also been recorded for the seizures of ecstasy pills from 2011 (47,761 pills) to 2012 (634,573 pills). The cumulative number of reported HIV infections since 1986 is 94,841. In 2011, sexual activity superseded injection drug use as the main transmission factor for the epidemic. HIV in the country mainly involves males, as they constitute 90% of cumulative HIV cases and a majority of those individuals are IDUs. However, HIV infection trends are shifting from males to females. There are 37,306 people living with HIV (PLHIV) who are eligible for treatment, and 14,002 PLHIV were receiving antiretroviral treatment (ART) in 2011. The decreasing trend of heroin users who have been detected and arrested could be due to the introduction of medical treatments and harm reduction approaches for drug users, resulting in fewer drug users being arrested. However, we are unable to say with certainty why there has been an increase in heroin seizures in the country. There has been an increasing trend in both ATS users and seizures. A new trend of co-occurring opiate dependence and ATS underscores the need to develop and implement effective treatments for ATS, co-occurring opiate and ATS, and polysubstance abuse disorders. The low numbers of NSEP clients being tested for HIV underscores our caution in interpreting the decline of HIV infections among drug users and the importance of focusing on providing education, prevention, treatment, and outreach to those who are not in treatment. PMID:25278737

Substance Abuse and the HIV Situation in Malaysia.

PubMed

Singh, Darshan; Chawarski, Marek C; Schottenfeld, Richard; Vicknasingam, Balasingam

2013-12-01

Heroin continues to be the main drug used in Malaysia, while amphetamine-type stimulants (ATS) have been recently identified as a growing problem. A cumulative total of 300,241 drug users were detected between 1988 and 2006. It is also estimated that Malaysia has 170,000 injecting drug users. HIV prevalence among drug users in the country ranges from 25% to 45%. Currently, there are approximately 380 general medical practice offices that offer agonist maintenance treatments for approximately 10,000 patients. There are 27,756 active patients in 333 general medical practice offices and government-run methadone maintenance treatment (MMT) centers. The Needle Syringe Exchange Program (NSEP) reached out to 34,244 injection drug users (IDUs) in 2011. In the last 2 years (2011 and 2012) the number of detected drug addicts decreased from 11,194 to 9015. The arrests made by the police related to opiate and cannabis use increased from 41,363 to 63,466 between the years 2008 and 2010, but decreased since 2010. An almost four-fold increase in the number of ATS and ketamine users was detected from 2006 (21,653 users) 2012 (76,812). Between 2004 and 2010, the yearly seizures for heroin ranged between 156 to 270 kg. However, in 2010 and 2011, heroin seizures showed a significant increase of 445kg and 410.02 kg, respectively. There has been a seizure of between 600 to 1000kg of syabu yearly from 2009 to 2012. Similar to heroin, increased seizures for Yaba have also been observed over the last 2 years. A significant increase has also been recorded for the seizures of ecstasy pills from 2011 (47,761 pills) to 2012 (634,573 pills). The cumulative number of reported HIV infections since 1986 is 94,841. In 2011, sexual activity superseded injection drug use as the main transmission factor for the epidemic. HIV in the country mainly involves males, as they constitute 90% of cumulative HIV cases and a majority of those individuals are IDUs. However, HIV infection trends are shifting from males to females. There are 37,306 people living with HIV (PLHIV) who are eligible for treatment, and 14,002 PLHIV were receiving antiretroviral treatment (ART) in 2011. The decreasing trend of heroin users who have been detected and arrested could be due to the introduction of medical treatments and harm reduction approaches for drug users, resulting in fewer drug users being arrested. However, we are unable to say with certainty why there has been an increase in heroin seizures in the country. There has been an increasing trend in both ATS users and seizures. A new trend of co-occurring opiate dependence and ATS underscores the need to develop and implement effective treatments for ATS, co-occurring opiate and ATS, and polysubstance abuse disorders. The low numbers of NSEP clients being tested for HIV underscores our caution in interpreting the decline of HIV infections among drug users and the importance of focusing on providing education, prevention, treatment, and outreach to those who are not in treatment.

[Current Perspective on Voltage-gated Potassium Channel Complex Antibody Associated Diseases].

PubMed

Watanabe, Osamu

2018-04-01

Voltage-gated potassium channel (VGKC) complex auto-antibodies were initially identified in Isaacs' syndrome (IS), which is characterized by muscle cramps and neuromyotonia. These antibodies were subsequently identified in patients with Morvan's syndrome (MoS), which includes IS in conjunction with psychosis, insomnia, and dysautonomia. The antibodies have also been detected in a patient with limbic encephalopathy (LE) presenting with prominent amnesia and frequent seizures. Typical cases of LE have adult-onset, with frequent, brief dystonic seizures that predominantly affect the arms and ipsilateral face, and has recently been termed faciobrachial dystonic seizures. Autoantibodies against the extracellular domains of VGKC complex proteins, leucine-rich glioma-inactivated 1 (LGI1), and contactin-associated protein-2 (Caspr2), occur in patients with IS, MoS, and LE. However, routine testing has detected VGKC complex antibodies without LGI1 or Caspr2 reactivities (double-negative) in patients with other diseases, such as Creutzfeldt-Jakob disease and amyotrophic lateral sclerosis. Furthermore, double-negative VGKC complex antibodies are often directed against cytosolic epitopes of Kv1 subunits. Therefore, these antibodies should no longer be classified as neuronal-surface antibodies and lacking pathogenic potential. Novel information has been generated regarding autoantibody disruption of the physiological functions of target proteins. LGI1 antibodies neutralize the interaction between LGI1 and ADAM22, thereby reducing the synaptic AMPA receptors. It may be that the main action is on inhibitory neurons, explaining why the loss of AMPA receptors causes amnesia, neuronal excitability and seizures.

Classification of Multiple Seizure-Like States in Three Different Rodent Models of Epileptogenesis.

PubMed

Guirgis, Mirna; Serletis, Demitre; Zhang, Jane; Florez, Carlos; Dian, Joshua A; Carlen, Peter L; Bardakjian, Berj L

2014-01-01

Epilepsy is a dynamical disease and its effects are evident in over fifty million people worldwide. This study focused on objective classification of the multiple states involved in the brain's epileptiform activity. Four datasets from three different rodent hippocampal preparations were explored, wherein seizure-like-events (SLE) were induced by the perfusion of a low - Mg(2+) /high-K(+) solution or 4-Aminopyridine. Local field potentials were recorded from CA3 pyramidal neurons and interneurons and modeled as Markov processes. Specifically, hidden Markov models (HMM) were used to determine the nature of the states present. Properties of the Hilbert transform were used to construct the feature spaces for HMM training. By sequentially applying the HMM training algorithm, multiple states were identified both in episodes of SLE and nonSLE activity. Specifically, preSLE and postSLE states were differentiated and multiple inner SLE states were identified. This was accomplished using features extracted from the lower frequencies (1-4 Hz, 4-8 Hz) alongside those of both the low- (40-100 Hz) and high-gamma (100-200 Hz) of the recorded electrical activity. The learning paradigm of this HMM-based system eliminates the inherent bias associated with other learning algorithms that depend on predetermined state segmentation and renders it an appropriate candidate for SLE classification.

Forensic intelligence for medicine anti-counterfeiting.

PubMed

Dégardin, Klara; Roggo, Yves; Margot, Pierre

2015-03-01

Medicine counterfeiting is a crime that has increased in recent years and now involves the whole world. Health and economic repercussions have led pharmaceutical industries and agencies to develop many measures to protect genuine medicines and differentiate them from counterfeits. Detecting counterfeit is chemically relatively simple for the specialists, but much more information can be gained from the analyses in a forensic intelligence perspective. Analytical data can feed criminal investigation and law enforcement by detecting and understanding the criminal phenomenon. Profiling seizures using chemical and packaging data constitutes a strong way to detect organised production and industrialised forms of criminality, and is the focus of this paper. Thirty-three seizures of a commonly counterfeited type of capsule have been studied. The results of the packaging and chemical analyses were gathered within an organised database. Strong linkage was found between the seizures at the different production steps, indicating the presence of a main counterfeit network dominating the market. The interpretation of the links with circumstantial data provided information about the production and the distribution of counterfeits coming from this network. This forensic intelligence perspective has the potential to be generalised to other types of products. This may be the only reliable approach to help the understanding of the organised crime phenomenon behind counterfeiting and to enable efficient strategic and operational decision making in an attempt to dismantle counterfeit network. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

The use of organic and inorganic impurities found in MDMA police seizures in a drug intelligence perspective.

PubMed

Morelato, Marie; Beavis, Alison; Tahtouh, Mark; Ribaux, Olivier; Kirkbride, Paul; Roux, Claude

2014-01-01

Traditional forensic drug profiling involves numerous analytical techniques, and the whole process is typically costly and may be time consuming. The aim of this study was to investigate the possibility of prioritising techniques utilised at the Australian Federal Police (AFP) for the chemical profiling of 3,4-methylenedioxymethylamphetamine (MDMA). The outcome would provide the AFP with the ability to obtain more timely and valuable results that could be used in an intelligence perspective. Correlation coefficients were used to obtain a similarity degree between a population of linked samples (within seizures) and a population of unlinked samples (between different seizures) and discrimination between the two populations was ultimately achieved. The results showed that gas chromatography-mass spectrometry (GC-MS) was well suited as a single technique to detect links between seizures and could be used in priority for operational intelligence purposes. Furthermore, the method was applied to seizures known or suspected (through their case information) to be linked to each other to assess the chemical similarity between samples. It was found that half of the seizures previously linked by the case number were also linked by the chemical profile. This procedure was also able to highlight links between cases that were previously unsuspected and retrospectively confirmed by circumstantial information. The findings are finally discussed in the broader forensic intelligence context, with a focus on how they could be successfully incorporated into investigations and in an intelligence-led policing perspective in order to understand trafficking markets. © 2014.

miRNA Expression Profile after Status Epilepticus and Hippocampal Neuroprotection by Targeting miR-132

PubMed Central

Jimenez-Mateos, Eva M.; Bray, Isabella; Sanz-Rodriguez, Amaya; Engel, Tobias; McKiernan, Ross C.; Mouri, Genshin; Tanaka, Katsuhiro; Sano, Takanori; Saugstad, Julie A.; Simon, Roger P.; Stallings, Raymond L.; Henshall, David C.

2011-01-01

When an otherwise harmful insult to the brain is preceded by a brief, noninjurious stimulus, the brain becomes tolerant, and the resulting damage is reduced. Epileptic tolerance develops when brief seizures precede an episode of prolonged seizures (status epilepticus). MicroRNAs (miRNAs) are small, noncoding RNAs that function as post-transcriptional regulators of gene expression. We investigated how prior seizure preconditioning affects the miRNA response to status epilepticus evoked by intra-amygdalar kainic acid in mice. The miRNA was extracted from the ipsilateral CA3 subfield 24 hours after focal-onset status epilepticus in animals that had previously received either seizure preconditioning (tolerance) or no preconditioning (injury), and mature miRNA levels were measured using TaqMan low-density arrays. Expression of 21 miRNAs was increased, relative to control, after status epilepticus alone, and expression of 12 miRNAs was decreased. Increased miR-132 levels were matched with increased binding to Argonaute-2, a constituent of the RNA-induced silencing complex. In tolerant animals, expression responses of >40% of the injury-group-detected miRNAs differed, being either unchanged relative to control or down-regulated, and this included miR-132. In vivo microinjection of locked nucleic acid-modified oligonucleotides (antagomirs) against miR-132 depleted hippocampal miR-132 levels and reduced seizure-induced neuronal death. Thus, our data strongly suggest that miRNAs are important regulators of seizure-induced neuronal death. PMID:21945804

Methamphetamine-induced neuronal necrosis: the role of electrographic seizure discharges

PubMed Central

Fujikawa, Denson G.; Pais, Emil S.; Aviles, Ernesto R.; Hsieh, Kung-Chiao; Bashir, Muhammad Tariq

2016-01-01

We have evidence that methamphetamine (METH)-induced neuronal death is morphologically necrotic, not apoptotic, as is currently believed, and that electrographic seizures may be responsible. We administered 40 mg/kg i.p. to 12 male C57BL/6 mice and monitored EEGs continuously and rectal temperatures every 15 min, keeping rectal temperatures <41.0 °C. Seven of the 12 mice had repetitive electrographic seizure discharges (RESDs) and 5 did not. The RESDs were often not accompanied by behavioral signs of seizures–i.e., they were often not accompanied by clonic forelimb movements. The 7 mice with RESDs had acidophilic neurons (the H&E light-microscopic equivalent of necrotic neurons by ultrastructural examination) in all of 7 brain regions (hippocampal CA1, CA2, CA3 and hilus, amygdala, piriform cortex and entorhinal cortex), the same brain regions damaged following generalized seizures, 24 h after METH administration. The 5 mice without RESDs had a few acidophilic neurons in 4 of the 7 brain regions, but those with RESDs had significantly more in 6 of the 7 brain regions. Maximum rectal temperatures were comparable in mice with and without RESDs, so that cannot explain the difference between the two groups with respect to METH-induced neuronal death. Our data show that METH-induced neuronal death is morphologically necrotic, that EEGs must be recorded to detect electrographic seizure activity in rodents without behavioral evidence of seizures, and that RESDs may be responsible for METH-induced neuronal death. PMID:26562800

Ictal EEG/fMRI study of vertiginous seizures.

PubMed

Morano, Alessandra; Carnì, Marco; Casciato, Sara; Vaudano, Anna Elisabetta; Fattouch, Jinane; Fanella, Martina; Albini, Mariarita; Basili, Luca Manfredi; Lucignani, Giulia; Scapeccia, Marco; Tomassi, Regina; Di Castro, Elisabetta; Colonnese, Claudio; Giallonardo, Anna Teresa; Di Bonaventura, Carlo

2017-03-01

Vertigo and dizziness are extremely common complaints, related to either peripheral or central nervous system disorders. Among the latter, epilepsy has to be taken into consideration: indeed, vertigo may be part of the initial aura of a focal epileptic seizure in association with other signs/symptoms, or represent the only ictal manifestation, a rare phenomenon known as "vertiginous" or "vestibular" seizure. These ictal symptoms are usually related to a discharge arising from/involving temporal or parietal areas, which are supposed to be a crucial component of the so-called "vestibular cortex". In this paper, we describe three patients suffering from drug-resistant focal epilepsy, symptomatic of malformations of cortical development or perinatal hypoxic/ischemic lesions located in the posterior regions, who presented clusters of vertiginous seizures. The high recurrence rate of such events, recorded during video-EEG monitoring sessions, offered the opportunity to perform an ictal EEG/fMRI study to identify seizure-related hemodynamic changes. The ictal EEG/fMRI revealed the main activation clusters in the temporo-parieto-occipital regions, which are widely recognized to be involved in the processing of vestibular information. Interestingly, ictal deactivation was also detected in the ipsilateral cerebellar hemisphere, suggesting the ictal involvement of cortical-subcortical structures known to be part of the vestibular integration network. Copyright © 2016 Elsevier Inc. All rights reserved.

High-Frequency Oscillations Recorded on the Scalp of Patients With Epilepsy Using Tripolar Concentric Ring Electrodes.

PubMed

Besio, Walter G; Martínez-Juárez, Iris E; Makeyev, Oleksandr; Gaitanis, John N; Blum, Andrew S; Fisher, Robert S; Medvedev, Andrei V

2014-01-01

Epilepsy is the second most prevalent neurological disorder ([Formula: see text]% prevalence) affecting [Formula: see text] million people worldwide with up to 75% from developing countries. The conventional electroencephalogram is plagued with artifacts from movements, muscles, and other sources. Tripolar concentric ring electrodes automatically attenuate muscle artifacts and provide improved signal quality. We performed basic experiments in healthy humans to show that tripolar concentric ring electrodes can indeed record the physiological alpha waves while eyes are closed. We then conducted concurrent recordings with conventional disc electrodes and tripolar concentric ring electrodes from patients with epilepsy. We found that we could detect high frequency oscillations, a marker for early seizure development and epileptogenic zone, on the scalp surface that appeared to become more narrow-band just prior to seizures. High frequency oscillations preceding seizures were present in an average of 35.5% of tripolar concentric ring electrode data channels for all the patients with epilepsy whose seizures were recorded and absent in the corresponding conventional disc electrode data. An average of 78.2% of channels that contained high frequency oscillations were within the seizure onset or irritative zones determined independently by three epileptologists based on conventional disc electrode data and videos.

Implementation of an established algorithm and modifications for the identification of epilepsy patients in the veterans health administration.

PubMed

Rehman, Rizwana; Everhart, Amanda; Frontera, Alfred T; Kelly, Pamela R; Lopez, Maria; Riley, Denise; Sajan, Sheela; Schooff, David M; Tran, Tung T; Husain, Aatif M

2016-11-01

Identification of epilepsy patients from administrative data in large managed healthcare organizations is a challenging task. The objectives of this report are to describe the implementation of an established algorithm and different modifications for the estimation of epilepsy prevalence in the Veterans Health Administration (VHA). For the prevalence estimation during a given time period patients prescribed anti-epileptic drugs and having seizure diagnoses on clinical encounters were identified. In contrast to the established algorithm, which required inclusion of diagnoses data from the time period of interest only, variants were tested by considering diagnoses data beyond prevalence period for improving sensitivity. One variant excluded data from diagnostic EEG and LTM clinics to improve specificity. Another modification also required documentation of seizures on the problem list (electronic list of patients' established diagnoses). Of the variants tested, the one excluding information from diagnostic clinics and extending time beyond base period of interest for clinical encounters was determined to be superior. It can be inferred that the number of patients receiving care for epilepsy in the VHA ranges between 74,000 and 87,000. In the wake of the recent implementation of ICD-10 codes in the VHA, minor tweaks are needed for future prevalence estimation due to significant efforts presented. This review is not only beneficial for researchers interested in VHA related data but can also be helpful for managed healthcare organizations involved in epilepsy care aiming at accurate identification of patients from large administrative databases. Published by Elsevier B.V.

SCN3A deficiency associated with increased seizure susceptibility

PubMed Central

Lamar, Tyra; Vanoye, Carlos G.; Calhoun, Jeffrey; Wong, Jennifer C.; Dutton, Stacey B.B.; Jorge, Benjamin S.; Velinov, Milen; Escayg, Andrew; Kearney, Jennifer A.

2017-01-01

Mutations in voltage-gated sodium channels expressed highly in the brain (SCN1A, SCN2A, SCN3A, and SCN8A) are responsible for an increasing number of epilepsy syndromes. In particular, mutations in the SCN3A gene, encoding the pore-forming Nav1.3 α subunit, have been identified in patients with focal epilepsy. Biophysical characterization of epilepsy-associated SCN3A variants suggests that both gain- and loss-of-function SCN3A mutations may lead to increased seizure susceptibility. In this report, we identified a novel SCN3A variant (L247P) by whole exome sequencing of a child with focal epilepsy, developmental delay, and autonomic nervous system dysfunction. Voltage clamp analysis showed no detectable sodium current in a heterologous expression system expressing the SCN3A-L247P variant. Furthermore, cell surface biotinylation demonstrated a reduction in the amount of SCN3A-L247P at the cell surface, suggesting the SCN3A-L247P variant is a trafficking-deficient mutant. To further explore the possible clinical consequences of reduced SCN3A activity, we investigated the effect of a hypomorphic Scn3a allele (Scn3aHyp) on seizure susceptibility and behavior using a gene trap mouse line. Heterozygous Scn3a mutant mice (Scn3a+/Hyp) did not exhibit spontaneous seizures nor were they susceptible to hyperthermia-induced seizures. However, they displayed increased susceptibility to electroconvulsive (6 Hz) and chemiconvulsive (flurothyl and kainic acid) induced seizures. Scn3a+/Hyp mice also exhibited deficits in locomotor activity and motor learning. Taken together, these results provide evidence that loss-of-function of SCN3A caused by reduced protein expression or deficient trafficking to the plasma membrane may contribute to increased seizure susceptibility. PMID:28235671

Segmentation of the Thalamus Based on BOLD Frequencies Affected in Temporal Lobe Epilepsy

PubMed Central

Morgan, Victoria L.; Rogers, Baxter P.; Abou-Khalil, Bassel

2015-01-01

Objective Temporal lobe epilepsy is associated with functional changes throughout the brain, particularly including a putative seizure propagation network involving the hippocampus, insula and thalamus. We identified a specified frequency range where functional connectivity in this network was related to duration of disease. Then, to identify specific thalamic nuclei involved in seizure propagation, we determined the subregions of the thalamus that have increased resting functional oscillations in this frequency range. Methods Resting-state functional MRI (fMRI) was acquired from twenty unilateral TLE (14 right, 6 left) patients and twenty healthy controls who were each age and gender matched to a specific patient. Wavelet based functional MRI connectivity mapping across the network was computed at each frequency to determine those frequencies where connectivity significantly decreases with duration of disease consistent with impairment due to repeated seizures. The voxel-wise power of the spontaneous blood oxygenation fluctuations of this frequency band was computed in the thalamus of each subject. Results Functional connectivity was impaired in the proposed seizure propagation network over a specific range (0.0067–0.013 Hz and 0.024–0.032 Hz) of blood oxygenation oscillations. Increased power in this frequency band (<0.032 Hz) was detected bilaterally in the pulvinar and anterior nucleus of the thalamus of healthy controls, and was increased over the ipsilateral thalamus compared to the contralateral thalamus in TLE. Significance This study identified frequencies of impaired connectivity in a TLE seizure propagation network and used them to localize the anterior nucleus and pulvinar of the thalamus as subregions most susceptible to TLE seizures. Further examinations of these frequencies in healthy and TLE subjects may provide unique information relating to the mechanism of seizure propagation and potential treatment using electrical stimulation. PMID:26360535

Prevalence and correlates of major depressive disorder (MDD) among adolescent patients with epilepsy attending a Nigerian neuropsychiatric hospital.

PubMed

Fela-Thomas, Ayodele; Akinhanmi, Akinwande; Esan, Oluyomi

2016-01-01

A high prevalence of mood disorders exists in patients with epilepsy. In most cases, this is not detected and, consequently, not treated. This study aimed to determine the prevalence and correlates of major depressive disorder (MDD) among adolescents with epilepsy attending a child and adolescent clinic in Nigeria. We recruited 156 participants consecutively for the study. Adherence was assessed using the 8-item Morisky Medication Adherence Questionnaire, while the K-SADS was used to assess the presence of major depressive disorder. Seizure control was evaluated by the frequency of seizures within a year. Major depressive disorder (DSM-IV criteria) was diagnosed in 28.2% of the participants. The age of participants (p=0.013), seizure control (p=0.03), medication adherence (p=0.045), frequency of seizures in the preceding 4weeks (p<0.001), and duration of illness (p<0.001) were all significantly associated with the presence of MDD. Participants with seizures occurring more than once weekly in the preceding 4weeks were 16 times more likely to have a MDD compared with those with no seizures in the preceding 4weeks (p<0.001, 95% C.I. [4.13, 65.43]), while participants with a duration of illness more than 10years were more than four times likely to have MDD compared with those with an illness duration of 5-10years (p<0.01, 95% C.I. [0.07, 0.70]). The prevalence of MDD among patients with epilepsy was high. Poor seizure control, poor medication adherence, and long duration of illness were associated with the presence of MDD among such patients. Intervention should focus on ensuring good seizure control and optimal adherence in order to mitigate the impact of MDD in patients with epilepsy. Copyright © 2015 Elsevier Inc. All rights reserved.

Epilepsy Surgery

MedlinePlus

... monitor the brain's activity and detect abnormalities. Single-photon emission computerized tomography (SPECT). The scan image varies ... off anti-seizure drugs after a year or two. By Mayo Clinic Staff . Mayo Clinic Footer Legal ...

High-frequency oscillations in epilepsy and surgical outcome. A meta-analysis

PubMed Central

Höller, Yvonne; Kutil, Raoul; Klaffenböck, Lukas; Thomschewski, Aljoscha; Höller, Peter M.; Bathke, Arne C.; Jacobs, Julia; Taylor, Alexandra C.; Nardone, Raffaele; Trinka, Eugen

2015-01-01

High frequency oscillations (HFOs) are estimated as a potential marker for epileptogenicity. Current research strives for valid evidence that these HFOs could aid the delineation of the to-be resected area in patients with refractory epilepsy and improve surgical outcomes. In the present meta-analysis, we evaluated the relation between resection of regions from which HFOs can be detected and outcome after epilepsy surgery. We conducted a systematic review of all studies that related the resection of HFO-generating areas to postsurgical outcome. We related the outcome (seizure freedom) to resection ratio, that is, the ratio between the number of channels on which HFOs were detected and, among these, the number of channels that were inside the resected area. We compared the resection ratio between seizure free and not seizure free patients. In total, 11 studies were included. In 10 studies, ripples (80–200 Hz) were analyzed, and in 7 studies, fast ripples (>200 Hz) were studied. We found comparable differences (dif) and largely overlapping confidence intervals (CI) in resection ratios between outcome groups for ripples (dif = 0.18; CI: 0.10–0.27) and fast ripples (dif = 0.17; CI: 0.01–0.33). Subgroup analysis showed that automated detection (dif = 0.22; CI: 0.03–0.41) was comparable to visual detection (dif = 0.17; CI: 0.08–0.27). Considering frequency of HFOs (dif = 0.24; CI: 0.09–0.38) was related more strongly to outcome than considering each electrode that was showing HFOs (dif = 0.15; CI = 0.03–0.27). The effect sizes found in the meta-analysis are small but significant. Automated detection and application of a detection threshold in order to detect channels with a frequent occurrence of HFOs is important to yield a marker that could be useful in presurgical evaluation. In order to compare studies with different methodological approaches, detailed and standardized reporting is warranted. PMID:26539097

Epilepsy surgery in patients with malformations of cortical development.

PubMed

Lüders, Hans; Schuele, Stephan U

2006-04-01

Patients with malformations of cortical development often suffer from intractable focal epilepsy. This review considers recent progress in the selection and seizure outcome of patients undergoing resective epilepsy surgery for this condition. Patients with malformations of cortical development restricted to part or even a whole hemisphere may be candidates for epilepsy surgery even when, due to microscopic malformations, magnetic resonance imaging shows no detectable lesion. Despite recent advances in structural and functional imaging, the majority of patients with this condition undergo invasive evaluation. Patients with focal cortical dysplasia, with and without a detectable lesion on magnetic resonance imaging, often have a favorable outcome with epilepsy surgery. The underlying pathological substrate seems to be a better predictor for surgical outcome in patients with focal cortical dysplasia than the presence of a lesion on magnetic resonance imaging. Epilepsy surgery can be offered in a highly selected subgroup of patients with unilateral nodular heterotopia. Seizures in hemimegalencephaly may respond favorably to hemispherectomy, although most children will continue to have seizures and significant functional impairments. Patients with focal epilepsy due to malformations of cortical development are often intractable to medical management. Resective epilepsy surgery can be beneficial, particularly for patients with focal cortical dysplasia and unilateral hemispheric malformations.

Focal status epilepticus and progressive dyskinesia: A novel phenotype for glycine receptor antibody-mediated neurological disease in children.

PubMed

Chan, D W S; Thomas, T; Lim, M; Ling, S; Woodhall, M; Vincent, A

2017-03-01

Antibody-associated disorders of the central nervous system are increasingly recognised in adults and children. Some are known to be paraneoplastic, whereas in others an infective trigger is postulated. They include disorders associated with antibodies to N-methyl-d-aspartate receptor (NMDAR), voltage-gated potassium channel-complexes (VGKC-complex), GABA B receptor or glycine receptor (GlyR). With antibodies to NMDAR or VGKC-complexes, distinct clinical patterns are well characterised, but as more antibodies are discovered, the spectra of associated disorders are evolving. GlyR antibodies have been detected in patients with progressive encephalopathy with rigidity and myoclonus (PERM), or stiff man syndrome, both rare but disabling conditions. We report a case of a young child with focal seizures and progressive dyskinesia in whom GlyR antibodies were detected. Anticonvulsants and immunotherapy were effective in treating both the seizures and movement disorder with good neurological outcome and with a decline in the patient's serum GlyR-Ab titres. Glycine receptor antibodies are associated with focal status epilepticus and seizures, encephalopathy and progressive dyskinesia and should be evaluated in autoimmune encephalitis. Copyright © 2016 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

Epilepsy

PubMed Central

Saipetch, Chutima; Sachs, Ezekiel

2016-01-01

Abstract Purpose of review: Technological advance has revolutionized epilepsy management recently. Herein, we review some recent developments. Recent findings: Responsive neurostimulation (Food and Drug Administration [FDA]-approved 2013) works by continuous analysis of brain rhythms and direct brain stimulation on detecting patterns thought to be epileptogenic, thereby aborting seizures. Cardio-responsive vagus nerve stimulation (FDA-approved 2015) is an improvement over traditional vagus nerve stimulation systems, taking advantage of the fact that 80% of seizures are associated with tachycardia. Automated tachycardia detection leads to vagus nerve stimulation to abort seizures. In MRI-guided stereotactic laser ablation (developed 2012), a directed laser emitting fiberoptic catheter is used to ablate epileptogenic lesions. The procedure can be completed in 3 to 4 hours, potentially under local anesthesia and with next-day discharge. Perampanel (FDA-approved 2012) is a promising new class of AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid)-antagonist antiseizure therapy. Meanwhile, a millennia-old remedy for epilepsy, cannabis, is staging a comeback with recent legal and social permissiveness accelerating research into this use. Summary: The coming years will demonstrate how these recent advances in device and drug management will improve the care of epilepsy. PMID:29443283

Intrathecal immunoglobulin synthesis in patients with symptomatic epilepsy and epilepsy of unknown etiology ('cryptogenic').

PubMed

Fauser, S; Soellner, C; Bien, C G; Tumani, H

2017-09-01

To compare the frequency of intrathecal immunoglobulin (Ig) synthesis in patients with symptomatic epilepsy and epilepsy of unknown etiology ('cryptogenic'). Patients with epileptic (n = 301) and non-epileptic (n = 10) seizures were retrospectively screened for autochthonous intrathecal Ig synthesis and oligoclonal bands (OCBs) in the cerebrospinal fluid. Intrathecal IgG/OCBs were detected in 8% of patients with epilepsies of unknown etiology, 5% of patients with first seizures of unknown cause and 0-4% of patients with epilepsy due to brain tumors, cerebrovascular disease or other etiologies. Intrathecal IgG/OCBs were not seen in patients with psychogenic seizures. Identical OCBs in serum and cerebrospinal fluid were more common in all patient groups (10-40% depending on underlying etiology). Intrathecal IgG synthesis/OCBs were observed slightly more frequently in patients with 'cryptogenic' epilepsy and with first seizures of unknown etiology than in other patient groups. However, this remained an infrequent finding and thus we could not confirm humoral immunity as a leading disease mechanism in patients with epilepsy in general or with unknown etiology in particular. © 2017 EAN.

Mu-opiate receptors measured by positron emission tomography are increased in temporal lobe epilepsy.

PubMed

Frost, J J; Mayberg, H S; Fisher, R S; Douglass, K H; Dannals, R F; Links, J M; Wilson, A A; Ravert, H T; Rosenbaum, A E; Snyder, S H

1988-03-01

Neurochemical studies in animal models of epilepsy have demonstrated the importance of multiple neurotransmitters and their receptors in mediating seizures. The role of opiate receptors and endogenous opioid peptides in seizure mechanisms is well developed and is the basis for measuring opiate receptors in patients with epilepsy. Patients with complex partial seizures due to unilateral temporal seizure foci were studied by positron emission tomography using 11C-carfentanil to measure mu-opiate receptors and 18F-fluoro-deoxy-D-glucose to measure glucose utilization. Opiate receptor binding is greater in the temporal neocortex on the side of the electrical focus than on the opposite side. Modeling studies indicate that the increase in binding is due to an increase in affinity or the number of unoccupied receptors. No significant asymmetry of 11C-carfentanil binding was detected in the amygdala or hippocampus. Glucose utilization correlated inversely with 11C-carfentanil binding in the temporal neocortex. Increased opiate receptors in the temporal neocortex may represent a tonic anticonvulsant system that limits the spread of electrical activity from other temporal lobe structures.

How do cognition, emotion, and epileptogenesis meet? A study of emotional cognitive bias in temporal lobe epilepsy.

PubMed

Lanteaume, Laura; Bartolomei, Fabrice; Bastien-Toniazzo, Mireille

2009-06-01

Emotional distress is one of the most frequently reported seizure precipitants in epilepsy, but little is known about its causes and processes. Interestingly, it is now accepted that emotional distress, such as anxiety, may be accompanied by evolutionary adaptation, or abnormal attentional vigilance toward threatening stimuli. The goal of this research was to study the link between emotional seizure precipitants and pathological attention-related biases toward threat in temporal lobe epilepsy (TLE). To this aim, patients were asked to report the extent to which seizures were elicited or not by emotional precipitants, allowing distinction of two groups: "Emo-TLE" group and "Other-TLE" group. Attentional biases were investigated by comparing patients' emotional Stroop and dot detection paradigms with those of healthy individuals (control group). We found that the Emo-TLE group was characterized by attentional bias toward threatening stimuli compared with neutral stimuli and compared with the other two groups. We thus hypothesize that attentional biases related to threat in patients with TLE may sustain emotional vulnerability and seizure occurrence.

Neurofibromin Regulates Seizure Attacks in the Rat Pilocarpine-Induced Model of Epilepsy.

PubMed

Ren, Min; Li, Kunyi; Wang, Dan; Guo, Jiamei; Li, Jing; Yang, Guang; Long, Xianghua; Shen, Wenjing; Hu, Rong; Wang, Xuefeng; Zeng, Kebin

2016-11-01

Studies have shown that neurofibromin (NF1) restricts GABA release at inhibitory synapses and regulates dendritic spine formation, which may play an important role in temporal lobe epilepsy (TLE). NF1 expression was detected by double-label immunofluorescence, immunohistochemistry, and western blot analysis in the brains of pilocarpine-induced epilepsy model rats at 6 h, 24 h, 72 h, 7 days, 14 days, 30 days, and 60 days after kindling. NF1 was localized primarily in the nucleus and cytoplasm of neurons. NF1 protein levels significantly increased in the chronic phase (from 7 days until 60 days) in this epileptic rat model. After NF1 expression was knocked down by specific siRNA, the effects of kindling with pilocarpine were evaluated on the 7th day after kindling. The onset latencies of pilocarpine-induced seizures were elevated, and the seizure frequency and duration were reduced in these rats. Our study demonstrates that NF1 promoted seizure attacks in rats with pilocarpine-induced epilepsy.

SeizAlert could give patients 4.5 hour warning of seizure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dr. Lee Hively and Kara Kruse

2009-01-16

One percent of Americans, 3 million people, suffer from epilepsy. And their lives are about to be dramatically changed by scientists at Oak Ridge National Laboratory. For 15 years, Dr. Lee Hively has been working on "SeizAlert", a seizure-detecting device that resembles a common PDA. "It allows us to analyze scalp brain waves and give us up to 4.5 hours' forewarning of that event," he said. With the help of partner Kara Kruse, he's now able to help patients predict the previously unpredictable.

SeizAlert could give patients 4.5 hour warning of seizure

ScienceCinema

Dr. Lee Hively and Kara Kruse

2017-12-09

One percent of Americans, 3 million people, suffer from epilepsy. And their lives are about to be dramatically changed by scientists at Oak Ridge National Laboratory. For 15 years, Dr. Lee Hively has been working on "SeizAlert", a seizure-detecting device that resembles a common PDA. "It allows us to analyze scalp brain waves and give us up to 4.5 hours' forewarning of that event," he said. With the help of partner Kara Kruse, he's now able to help patients predict the previously unpredictable.

Vagus Nerve Stimulation for Electrographic Status Epilepticus in Slow-Wave Sleep.

PubMed

Carosella, Christopher M; Greiner, Hansel M; Byars, Anna W; Arthur, Todd M; Leach, James L; Turner, Michele; Holland, Katherine D; Mangano, Francesco T; Arya, Ravindra

2016-07-01

Electrographic status epilepticus in slow sleep or continuous spike and waves during slow-wave sleep is an epileptic encephalopathy characterized by seizures, neurocognitive regression, and significant activation of epileptiform discharges during nonrapid eye movement sleep. There is no consensus on the diagnostic criteria and evidence-based optimal treatment algorithm for children with electrographic status epilepticus in slow sleep. We describe a 12-year-old girl with drug-resistant electrographic status epilepticus in slow wave sleep that was successfully treated with vagus nerve stimulation. Her clinical presentation, presurgical evaluation, decision-making, and course after vagus nerve stimulator implantation are described in detail. After vagus nerve stimulator implantation, the girl remained seizure free for more than a year, resolved the electrographic status epilepticus in slow sleep pattern on electroencephalography, and exhibited significant cognitive improvement. Vagus nerve stimulation may be considered for electrographic status epilepticus in slow sleep. Copyright © 2016 Elsevier Inc. All rights reserved.

Standardized Treatment of Neonatal Status Epilepticus Improves Outcome.

PubMed

Harris, Mandy L; Malloy, Katherine M; Lawson, Sheena N; Rose, Rebecca S; Buss, William F; Mietzsch, Ulrike

2016-12-01

We aimed to decrease practice variation in treatment of neonatal status epilepticus by implementing a standardized protocol. Our primary goal was to achieve 80% adherence to the algorithm within 12 months. Secondary outcome measures included serum phenobarbital concentrations, number of patients progressing from seizures to status epilepticus, and length of hospital stay. Data collection occurred for 6 months prior and 12 months following protocol implementation. Adherence of 80% within 12 months was partially achieved in patients diagnosed in our hospital; in pretreated patients, adherence was not achieved. Maximum phenobarbital concentrations were decreased (56.8 vs 41.0 µg/mL), fewer patients progressed from seizures to status epilepticus (46% vs 36%), and hospital length of stay decreased by 9.7 days in survivors. In conclusion, standardized, protocol-driven treatment of neonatal status epilepticus improves consistency and short-term outcome. © The Author(s) 2016.

Clinical outcomes associated with brand-to-generic phenytoin interchange.

PubMed

Kinikar, Shilpa A; Delate, Thomas; Menaker-Wiener, C Mindy; Bentley, William H

2012-05-01

Concerns that antiepileptic brand-to-generic interchange results in disruption of seizure control are widespread. However, little within-patient evidence exists examining such interchanges. To compare within-patient seizure control before and after the interchange of a branded to a single-source generic phenytoin among patients with seizures in a managed care organization. This was a pre-post, self-controlled, retrospective study. Adults with a history of seizure who used Dilantin Kapseals 100 mg extended phenytoin sodium, USP, capsules and whose therapy was interchanged to Taro Pharmaceuticals' AB-rated generic extended phenytoin sodium capsules, USP, 100 mg between July 2007 and May 2008 were included. Study outcomes included the comparisons of the proportions of patients with at least emergency department (ED) visit/inpatient hospitalization and medical office visit/nonoffice consultation for acute seizure in the 6 months before and after interchange. Outcomes were confirmed with manual chart reviews and adjusted for potential confounding medication use. A total of 222 patients were included in the study. Patients were primarily middle-aged (mean 56 years), equally mixed by sex (47% female); most had nonintractable seizures. The majority of patients (~70%) were on phenytoin as monotherapy and had equivalent rates of purchases for potentially confounding medications in both pre- and postinterchange time periods (all p > 0.05). Low serum concentrations were detected more often in the postinterchange study period (adjusted p < 0.001). Despite this, there were low proportions of patients with confirmed seizure events that resulted in an ED visit/inpatient hospitalization in both pre- and postinterchange periods (both 6.3%, adjusted p = 0.937). The proportion of patients with confirmed seizure events diagnosed at a medical office visit was not significantly different between the preinterchange and postinterchange periods (12.2% vs 11.3%, adjusted p = 0.545). No increased proportion of seizures was observed within patients when branded phenytoin was interchanged to an AB-rated, single-source, generic equivalent. More rigorous studies should be conducted to more thoroughly evaluate patient tolerability and drug efficacy when antiepileptic drugs are interchanged from brand to generic formulations.

Comparison of background EEG activity of different groups of patients with idiopathic epilepsy using Shannon spectral entropy and cluster-based permutation statistical testing

PubMed Central

Artieda, Julio; Iriarte, Jorge

2017-01-01

Idiopathic epilepsy is characterized by generalized seizures with no apparent cause. One of its main problems is the lack of biomarkers to monitor the evolution of patients. The only tools they can use are limited to inspecting the amount of seizures during previous periods of time and assessing the existence of interictal discharges. As a result, there is a need for improving the tools to assist the diagnosis and follow up of these patients. The goal of the present study is to compare and find a way to differentiate between two groups of patients suffering from idiopathic epilepsy, one group that could be followed-up by means of specific electroencephalographic (EEG) signatures (intercritical activity present), and another one that could not due to the absence of these markers. To do that, we analyzed the background EEG activity of each in the absence of seizures and epileptic intercritical activity. We used the Shannon spectral entropy (SSE) as a metric to discriminate between the two groups and performed permutation-based statistical tests to detect the set of frequencies that show significant differences. By constraining the spectral entropy estimation to the [6.25–12.89) Hz range, we detect statistical differences (at below 0.05 alpha-level) between both types of epileptic patients at all available recording channels. Interestingly, entropy values follow a trend that is inversely related to the elapsed time from the last seizure. Indeed, this trend shows asymptotical convergence to the SSE values measured in a group of healthy subjects, which present SSE values lower than any of the two groups of patients. All these results suggest that the SSE, measured in a specific range of frequencies, could serve to follow up the evolution of patients suffering from idiopathic epilepsy. Future studies remain to be conducted in order to assess the predictive value of this approach for the anticipation of seizures. PMID:28922360

Seizures and Encephalitis in Myelin Oligodendrocyte Glycoprotein IgG Disease vs Aquaporin 4 IgG Disease.

PubMed

Hamid, Shahd H M; Whittam, Dan; Saviour, Mariyam; Alorainy, Amal; Mutch, Kerry; Linaker, Samantha; Solomon, Tom; Bhojak, Maneesh; Woodhall, Mark; Waters, Patrick; Appleton, Richard; Duddy, Martin; Jacob, Anu

2018-01-01

Antibodies to myelin oligodendrocyte glycoprotein IgG (MOG-IgG) are increasingly detected in patients with non-multiple sclerosis-related demyelination, some of whom manifest a neuromyelitis optica (NMO) phenotype. Cortical involvement, encephalopathy, and seizures are rare in aquaporin 4 antibody (AQP4-IgG)-related NMO in the white European population. However, the authors encountered several patients with seizures associated with MOG-IgG disease. To compare incidence of seizures and encephalitis-like presentation, or both between AQP4-IgG-positive and MOG-IgG-positive patients. Retrospective case series of all patients who were seropositive for MOG-IgG (n = 34) and the last 100 patients with AQP4-IgG disease (NMO spectrum disorder) seen in the NMO service between January 2013 and December 2016, and analysis was completed January 4, 2017. All patients were seen in a tertiary neurological center, The Walton Centre NHS Foundation Trust in Liverpool, England. The difference in seizure frequency between the AQP4-IgG-positive and MOG-IgG-positive patient groups was determined. Thirty-four patients with MOG-IgG disease (20 female) with a median age at analysis of 30.5 years (interquartile range [IQR], 15-69 years), and 100 AQP4-IgG-positive patients (86 female) with a median age at analysis of 54 years (IQR, 12-91 years) were studied. Most patients were of white race. Five of the 34 patients with MOG-IgG (14.7%) had seizures compared with 1 patient with AQP4-IgG (2-sided P < .008, Fisher test). On magnetic resonance imaging, all 5 MOG-IgG-positive patients had inflammatory cortical brain lesions associated with the seizures. In 3 of the 5 MOG-IgG-positive patients, seizures occurred as part of the index event. Four of the 5 presented with encephalopathy and seizures, and disease relapsed in all 5 patients. Four of these patients were receiving immunosuppressant medication at last follow-up, and 3 continued to take antiepileptic medication. In contrast, the only AQP4-IgG-positive patient with seizures had a diagnosis of complex partial epilepsy preceding the onset of NMO by several years and experienced no encephalitic illness; her magnetic resonance imaging results demonstrated no cortical, subcortical, or basal ganglia involvement. Patients with MOG-IgG-associated disease were more likely to have seizures and encephalitis-like presentation than patients with AQP4-IgG-associated disease.

Astrocyte-neuronal interactions in epileptogenesis.

PubMed

Hadera, Mussie Ghezu; Eloqayli, Haytham; Jaradat, Saied; Nehlig, Astrid; Sonnewald, Ursula

2015-07-01

Pentylenetetrazol, kainic acid, or pilocarpine can be used to induce seizures in animal models of epilepsy. The present Review describes disturbances in astrocyte-neuron interactions in the acute, latent, and chronic phases analyzed by magnetic resonance spectroscopy of brain tissue extracts from rats injected with [1-(13)C]glucose and [1,2-(13)C]acetate. The most consistent change after onset of seizures was the decrease in (13)C labeling of glutamate (GLU) from [1-(13) C]glucose regardless of brain area, severity, or duration of the period with seizures and toxin used. In most cases this decrease was accompanied by a reduction in glutamine (GLN) labeling from [1-(13)C]glucose, presumably as a direct consequence of the reduction in labeling of GLU and the GLU-GLN cycle. Amounts of GLN were never changed. Reduction in the content of N-acetyl aspartate (NAA) was first detectable some time after status epilepticus but before the occurrence of spontaneous seizures. This decrease can be an indication of neuronal death and/or mitochondrial impairment and might indicate beginning gliosis. It is known that gliosis occurs in the chronic phase of temporal lobe epilepsy in hippocampus, but astrocyte metabolism appears normal in this phase, indicating that the gliotic astrocytes have a somewhat reduced metabolism per volume. A decrease in (13)C labeling of GLU from [1-(13)C]glucose is a very sensitive measure for the onset of epileptogenesis, whereas reduction of NAA is first detectable later. In the chronic phases of the hippocampal formation, astrocyte metabolism is upregulated given that the number of neurons is reduced. © 2015 Wiley Periodicals, Inc.

Stereotactic radiosurgery for the treatment of mesial temporal lobe epilepsy.

PubMed

Feng, E-S; Sui, C-B; Wang, T-X; Sun, G-L

2016-12-01

Stereotactic radiosurgery (RS) is a potential option for some patients with temporal lobe epilepsy (TLE). The aim of this meta-analysis was to determine the pooled seizure-free rate and the time interval to seizure cessation in patients with lesions in the mesial temporal lobe, and who were eligible for either stereotactic or gamma knife RS. We searched the Medline, Cochrane, EMBASE, and Google Scholar databases using combinations of the following terms: RS, stereotactic radiosurgery, gamma knife, and TLE. We screened 103 articles and selected 13 for inclusion in the meta-analysis. Significant study heterogeneity was detected; however, the included studies displayed an acceptable level of quality. We show that approximately half of the patients were seizure free over a follow-up period that ranged from 6 months to 9 years [pooled estimate: 50.9% (95% confidence interval: 0.381-0.636)], with an average of 14 months to seizure cessation [pooled estimate: 14.08 months (95% confidence interval: 11.95-12.22 months)]. Nine of 13 included studies reported data for adverse events (AEs), which included visual field deficits and headache (the two most common AEs), verbal memory impairment, psychosis, psychogenic non-epileptic seizures, and dysphasia. Patients in the individual studies experienced AEs at rates that ranged from 8%, for non-epileptic seizures, to 85%, for headache. Our findings indicate that RS may have similar or slightly less efficacy in some patients compared with invasive surgery. Randomized controlled trials of both treatment regimens should be undertaken to generate an evidence base for patient decision-making. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

miRNA Expression profile after status epilepticus and hippocampal neuroprotection by targeting miR-132.

PubMed

Jimenez-Mateos, Eva M; Bray, Isabella; Sanz-Rodriguez, Amaya; Engel, Tobias; McKiernan, Ross C; Mouri, Genshin; Tanaka, Katsuhiro; Sano, Takanori; Saugstad, Julie A; Simon, Roger P; Stallings, Raymond L; Henshall, David C

2011-11-01

When an otherwise harmful insult to the brain is preceded by a brief, noninjurious stimulus, the brain becomes tolerant, and the resulting damage is reduced. Epileptic tolerance develops when brief seizures precede an episode of prolonged seizures (status epilepticus). MicroRNAs (miRNAs) are small, noncoding RNAs that function as post-transcriptional regulators of gene expression. We investigated how prior seizure preconditioning affects the miRNA response to status epilepticus evoked by intra-amygdalar kainic acid in mice. The miRNA was extracted from the ipsilateral CA3 subfield 24 hours after focal-onset status epilepticus in animals that had previously received either seizure preconditioning (tolerance) or no preconditioning (injury), and mature miRNA levels were measured using TaqMan low-density arrays. Expression of 21 miRNAs was increased, relative to control, after status epilepticus alone, and expression of 12 miRNAs was decreased. Increased miR-132 levels were matched with increased binding to Argonaute-2, a constituent of the RNA-induced silencing complex. In tolerant animals, expression responses of >40% of the injury-group-detected miRNAs differed, being either unchanged relative to control or down-regulated, and this included miR-132. In vivo microinjection of locked nucleic acid-modified oligonucleotides (antagomirs) against miR-132 depleted hippocampal miR-132 levels and reduced seizure-induced neuronal death. Thus, our data strongly suggest that miRNAs are important regulators of seizure-induced neuronal death. Copyright © 2011 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

Bipolar electrocoagulation on cortex after AVMs lesionectomy for seizure control.

PubMed

Cao, Yong; Wang, Rong; Yang, Lijun; Bai, Qin; Wang, Shuo; Zhao, Jizong

2011-01-01

The findings of previous studies remain controversial on the optimal management required for effective seizure control after surgical excision of arteriovenous malformations (AVMs). We evaluated the efficacy of additional bipolar electrocoagulation on the electrically positive cortex guided by intraoperative electrocorticography (ECoG) for controlling cerebral AVMs-related epilepsy. Sixty consecutive patients with seizure due to cerebral AVMs, who underwent surgical excision of cerebral AVMs and intraoperative ECoG, were assessed. The AVMs and surrounding hemosiderin stained tissue were completely removed, and bipolar electrocoagulation was applied on the surrounding cerebral cortex where epileptic discharges were monitored via intraoperative ECoG. Patients were followed up at three to six months after the surgery and then annually. We evaluated seizure outcome by using Engel's classification and postoperative complications. Forty-nine patients (81.6%) were detected of epileptic discharges before and after AVMs excision. These patients underwent the removal of AVMs plus bipolar electrocoagulation on spike-positive site cortex. After electrocoagulation, 45 patients' epileptic discharges disappeared, while four obviously diminished. Fifty-five of 60 patients (91.7%) had follow-up lasting at least 22 months (mean 51.1 months; range 22-93 months). Determined by the Engel Seizure Outcome Scale, 39 patients (70.9%) were Class I, seven (12.7%) Class II, five (9.0%) Class III, and four (7.2%) Class IV. Even after the complete removal of AVM and surrounding gliotic and hemosiderin stained tissue, a high-frequency residual spike remained on the surrounding cerebral cortex. Effective surgical seizure control can be achieved by carrying out additional bipolar electrocoagulation on the cortex guided by the intraoperative ECoG.

Category-Specific Naming and Recognition Deficits in Temporal Lobe Epilepsy Surgical Patients

PubMed Central

Drane, Daniel L.; Ojemann, George A.; Aylward, Elizabeth; Ojemann, Jeffrey G.; Johnson, L. Clark; Silbergeld, Daniel L.; Miller, John W.; Tranel, Daniel

2008-01-01

Objective Based upon Damasio's “Convergence Zone” model of semantic memory, we predicted that epilepsy surgical patients with anterior temporal lobe (TL) seizure onset would exhibit a pattern of category-specific naming and recognition deficits not observed in patients with seizures arising elsewhere. Methods We assessed epilepsy patients with unilateral seizure onset of anterior TL or other origin (n = 22), pre- or postoperatively, using a set of category-specific items and a conventional measure of visual naming (Boston Naming Test: BNT). Results Category-specific naming deficits were exhibited by patients with dominant anterior TL seizure onset/resection for famous faces and animals, while category-specific recognition deficits for these same categories were exhibited by patients with nondominant anterior TL onset/resection. Patients with other seizure onset did not exhibit category-specific deficits. Naming and recognition deficits were frequently not detected by the BNT, which samples only a limited range of stimuli. Interpretation Consistent with the “convergence zone” framework, results suggest that the nondominant anterior TL plays a major role in binding sensory information into conceptual percepts for certain stimuli, while dominant TL regions function to provide a link to verbal labels for these percepts. Although observed category-specific deficits were striking, they were often missed by the BNT, suggesting that they are more prevalent than recognized in both pre- and postsurgical epilepsy patients. Systematic investigation of these deficits could lead to more refined models of semantic memory, aid in the localization of seizures, and contribute to modifications in surgical technique and patient selection in epilepsy surgery to improve neurocognitive outcome. PMID:18206185

A Realistic Seizure Prediction Study Based on Multiclass SVM.

PubMed

Direito, Bruno; Teixeira, César A; Sales, Francisco; Castelo-Branco, Miguel; Dourado, António

2017-05-01

A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

Cross-entropy optimization for neuromodulation.

PubMed

Brar, Harleen K; Yunpeng Pan; Mahmoudi, Babak; Theodorou, Evangelos A

2016-08-01

This study presents a reinforcement learning approach for the optimization of the proportional-integral gains of the feedback controller represented in a computational model of epilepsy. The chaotic oscillator model provides a feedback control systems view of the dynamics of an epileptic brain with an internal feedback controller representative of the natural seizure suppression mechanism within the brain circuitry. Normal and pathological brain activity is simulated in this model by adjusting the feedback gain values of the internal controller. With insufficient gains, the internal controller cannot provide enough feedback to the brain dynamics causing an increase in correlation between different brain sites. This increase in synchronization results in the destabilization of the brain dynamics, which is representative of an epileptic seizure. To provide compensation for an insufficient internal controller an external controller is designed using proportional-integral feedback control strategy. A cross-entropy optimization algorithm is applied to the chaotic oscillator network model to learn the optimal feedback gains for the external controller instead of hand-tuning the gains to provide sufficient control to the pathological brain and prevent seizure generation. The correlation between the dynamics of neural activity within different brain sites is calculated for experimental data to show similar dynamics of epileptic neural activity as simulated by the network of chaotic oscillators.

VEGF Receptor-2 (Flk-1) Overexpression in Mice Counteracts Focal Epileptic Seizures

PubMed Central

Nikitidou, Litsa; Kanter-Schlifke, Irene; Dhondt, Joke; Carmeliet, Peter; Lambrechts, Diether; Kokaia, Mérab

2012-01-01

Vascular endothelial growth factor (VEGF) was first described as an angiogenic agent, but has recently also been shown to exert various neurotrophic and neuroprotective effects in the nervous system. These effects of VEGF are mainly mediated by its receptor, VEGFR-2, which is also referred to as the fetal liver kinase receptor 1 (Flk-1). VEGF is up-regulated in neurons and glial cells after epileptic seizures and counteracts seizure-induced neurodegeneration. In vitro, VEGF administration suppresses ictal and interictal epileptiform activity caused by AP4 and 0 Mg2+ via Flk-1 receptor. We therefore explored whether increased VEGF signaling through Flk-1 overexpression may regulate epileptogenesis and ictogenesis in vivo. To this extent, we used transgenic mice overexpressing Flk-1 postnatally in neurons. Intriguingly, Flk-1 overexpressing mice were characterized by an elevated threshold for seizure induction and a decreased duration of focal afterdischarges, indicating anti-ictal action. On the other hand, the kindling progression in these mice was similar to wild-type controls. No significant effects on blood vessels or glia cells, as assessed by Glut1 and GFAP immunohistochemistry, were detected. These results suggest that increased VEGF signaling via overexpression of Flk-1 receptors may directly affect seizure activity even without altering angiogenesis. Thus, Flk-1 could be considered as a novel target for developing future gene therapy strategies against ictal epileptic activity. PMID:22808185

The effect of CXCR2 inhibition on seizure activity in the pilocarpine epilepsy mouse model.

PubMed

Xu, Tao; Yu, Xinyuan; Wang, Teng; Liu, Ying; Liu, Xi; Ou, Shu; Chen, Yangmei

2017-09-01

C-X-C motif chemokine receptor 2 (CXCR2) is one of the most well characterized chemokine receptors and is a potential target for treating brain pathologies involving inflammatory processes, including epilepsy. However, the role of CXCR2 in epilepsy has not been investigated, and whether CXCR2 modulates seizure activity in temporal lobe epilepsy (TLE) remains unknown. In this study, we aimed to determine the potential role of CXCR2 in intractable TLE patients and in pilocarpine-induced epileptic mice. Here, through Western blotting and semi-quantitative immunohistochemistry, we detected that CXCR2 protein expression was up-regulated (by nearly 50%) in the temporal neocortex of TLE patients and in the hippocampus and adjacent temporal cortex of pilocarpine mice model. Double-label immunofluorescence and immunohistochemical analysis indicated that CXCR2 was expressed in neurons. To investigate the effect of the CXCR2 selective antagonist SB225002 on seizure activity, SB225002 was i.p. administered during the latency window of spontaneous recurrent seizures (SRSs). This treatment increased (by nearly 40%) the latency of SRSs and reduced (by nearly 50%) the frequency of SRSs during the chronic period of epilepsy. This study suggests that CXCR2 plays a critical role in modifying epileptic seizure activity and that CXCR2 blockade could be a potential molecular therapeutic target for epilepsy. Copyright © 2017 Elsevier Inc. All rights reserved.

Gamma frequency SSVEP components differentiate children with febrile seizures from normal controls.

PubMed

Birca, Ala; Carmant, Lionel; Lortie, Anne; Vannasing, Phetsamone; Lassonde, Maryse

2008-11-01

Gamma band electroencephalography (EEG) abnormalities have been reported in patients with epilepsy. We aimed to investigate whether patients with febrile seizures (FS) show abnormalities of the gamma frequency steady-state visual evoked potential (SSVEP) components evoked by intermittent photic stimulation (IPS). We analyzed the magnitude and phase alignment of the 50-100 Hz SSVEP components elicited by IPS from 12 FS patients, 5 siblings of FS patients, and 15 control children between 6 and 36 months of age. Patients with FS showed significantly higher SSVEP magnitude and phase alignment values when compared to both the siblings and control groups. Detected abnormalities could either represent the direct consequence of seizures or indicate a preexisting tendency to hypersynchrony in FS patients. Future prospective studies could assess whether SSVEP abnormalities are associated with complex rather than simple FS, or have a prognostic value for the development of epilepsy following FS.

Lack of antibodies to NMDAR or VGKC-complex in GAD and cardiolipin antibody-positive refractory epilepsy.

PubMed

Liimatainen, Suvi; Peltola, Jukka; Hietaharju, Aki; Sabater, Lidia; Lang, Bethan

2014-03-01

Over the last few years autoantibodies against neuronal proteins have been identified in several forms of autoimmune encephalitis and epilepsy. NMDA receptor (NMDAR) and voltage gated potassium channel (VGKC) complex antibodies are mainly associated with limbic encephalitis (LE) whereas glutamic acid decarboxylase antibodies (GADA) and anticardiolipin (ACL) antibodies are more commonly detected in patients with chronic epilepsy. Clinical features vary between these antibodies suggesting the specificity of different neuronal antibodies in seizures. Serum samples of 14 GADA positive and 24 ACL positive patients with refractory epilepsy were analyzed for the presence of VGKC or NMDAR antibodies. No positive VGKC or NMDAR antibodies were found in these patients. The results confirm the different significance of these neuronal antibodies in seizure disorders. Different autoantibodies have different significance in seizures and probably have different pathophysiological mechanisms of actions. Copyright © 2014 Elsevier B.V. All rights reserved.

DOORS syndrome: phenotype, genotype and comparison with Coffin-Siris syndrome.

PubMed

Campeau, Philippe M; Hennekam, Raoul C

2014-09-01

DOORS syndrome (Deafness, Onychodystrophy, Osteodystrophy, mental Retardation, Seizures) is characterized mainly by sensorineural deafness, shortened terminal phalanges with small nails of hands and feet, intellectual deficiency, and seizures. Half of the patients with all clinical features have mutations in TBC1D24. We review here the manifestations of patients clinically diagnosed with DOORS syndrome. In this cohort of 32 families (36 patients) we detected 13 individuals from 10 families with TBC1D24 mutations. Subsequent whole exome sequencing in the cohort showed the same de novoSMARCB1 mutation (c.1130G>A), known to cause Coffin-Siris syndrome, in two patients. Distinguishing features include retinal anomalies, Dandy-Walker malformation, scoliosis, rocker bottom feet, respiratory difficulties and absence of seizures, and 2-oxoglutaric aciduria in the patients with the SMARCB1 mutation. We briefly discuss the heterogeneity of the DOORS syndrome phenotype and the differential diagnosis of this condition. © 2014 Wiley Periodicals, Inc.

Comparison of human and algorithmic target detection in passive infrared imagery

NASA Astrophysics Data System (ADS)

Weber, Bruce A.; Hutchinson, Meredith

2003-09-01

We have designed an experiment that compares the performance of human observers and a scale-insensitive target detection algorithm that uses pixel level information for the detection of ground targets in passive infrared imagery. The test database contains targets near clutter whose detectability ranged from easy to very difficult. Results indicate that human observers detect more "easy-to-detect" targets, and with far fewer false alarms, than the algorithm. For "difficult-to-detect" targets, human and algorithm detection rates are considerably degraded, and algorithm false alarms excessive. Analysis of detections as a function of observer confidence shows that algorithm confidence attribution does not correspond to human attribution, and does not adequately correlate with correct detections. The best target detection score for any human observer was 84%, as compared to 55% for the algorithm for the same false alarm rate. At 81%, the maximum detection score for the algorithm, the same human observer had 6 false alarms per frame as compared to 29 for the algorithm. Detector ROC curves and observer-confidence analysis benchmarks the algorithm and provides insights into algorithm deficiencies and possible paths to improvement.

Genetic testing in benign familial epilepsies of the first year of life: clinical and diagnostic significance.

PubMed

Zara, Federico; Specchio, Nicola; Striano, Pasquale; Robbiano, Angela; Gennaro, Elena; Paravidino, Roberta; Vanni, Nicola; Beccaria, Francesca; Capovilla, Giuseppe; Bianchi, Amedeo; Caffi, Lorella; Cardilli, Viviana; Darra, Francesca; Bernardina, Bernardo Dalla; Fusco, Lucia; Gaggero, Roberto; Giordano, Lucio; Guerrini, Renzo; Incorpora, Gemma; Mastrangelo, Massimo; Spaccini, Luigina; Laverda, Anna Maria; Vecchi, Marilena; Vanadia, Francesca; Veggiotti, Pierangelo; Viri, Maurizio; Occhi, Guya; Budetta, Mauro; Taglialatela, Maurizio; Coviello, Domenico A; Vigevano, Federico; Minetti, Carlo

2013-03-01

To dissect the genetics of benign familial epilepsies of the first year of life and to assess the extent of the genetic overlap between benign familial neonatal seizures (BFNS), benign familial neonatal-infantile seizures (BFNIS), and benign familial infantile seizures (BFIS). Families with at least two first-degree relatives affected by focal seizures starting within the first year of life and normal development before seizure onset were included. Families were classified as BFNS when all family members experienced neonatal seizures, BFNIS when the onset of seizures in family members was between 1 and 4 months of age or showed both neonatal and infantile seizures, and BFIS when the onset of seizures was after 4 months of age in all family members. SCN2A, KCNQ2, KCNQ3, PPRT2 point mutations were analyzed by direct sequencing of amplified genomic DNA. Genomic deletions involving KCNQ2 and KCNQ3 were analyzed by multiple-dependent probe amplification method. A total of 46 families including 165 affected members were collected. Eight families were classified as BFNS, 9 as BFNIS, and 29 as BFIS. Genetic analysis led to the identification of 41 mutations, 14 affecting KCNQ2, 1 affecting KCNQ3, 5 affecting SCN2A, and 21 affecting PRRT2. The detection rate of mutations in the entire cohort was 89%. In BFNS, mutations specifically involve KCNQ2. In BFNIS two genes are involved (KCNQ2, six families; SCN2A, two families). BFIS families are the most genetically heterogeneous, with all four genes involved, although about 70% of them carry a PRRT2 mutation. Our data highlight the important role of KCNQ2 in the entire spectrum of disorders, although progressively decreasing as the age of onset advances. The occurrence of afebrile seizures during follow-up is associated with KCNQ2 mutations and may represent a predictive factor. In addition, we showed that KCNQ3 mutations might be also involved in families with infantile seizures. Taken together our data indicate an important role of K-channel genes beyond the typical neonatal epilepsies. The identification of a novel SCN2A mutation in a family with infantile seizures with onset between 6 and 8 months provides further confirmation that this gene is not specifically associated with BFNIS and is also involved in families with a delayed age of onset. Our data indicate that PRRT2 mutations are clustered in families with BFIS. Paroxysmal kinesigenic dyskinesia emerges as a distinctive feature of PRRT2 families, although uncommon in our series. We showed that the age of onset of seizures is significantly correlated with underlying genetics, as about 90% of the typical BFNS families are linked to KCNQ2 compared to only 3% of the BFIS families, for which PRRT2 represents the major gene. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

Prediction of gene-phenotype associations in humans, mice, and plants using phenologs.

PubMed

Woods, John O; Singh-Blom, Ulf Martin; Laurent, Jon M; McGary, Kriston L; Marcotte, Edward M

2013-06-21

Phenotypes and diseases may be related to seemingly dissimilar phenotypes in other species by means of the orthology of underlying genes. Such "orthologous phenotypes," or "phenologs," are examples of deep homology, and may be used to predict additional candidate disease genes. In this work, we develop an unsupervised algorithm for ranking phenolog-based candidate disease genes through the integration of predictions from the k nearest neighbor phenologs, comparing classifiers and weighting functions by cross-validation. We also improve upon the original method by extending the theory to paralogous phenotypes. Our algorithm makes use of additional phenotype data--from chicken, zebrafish, and E. coli, as well as new datasets for C. elegans--establishing that several types of annotations may be treated as phenotypes. We demonstrate the use of our algorithm to predict novel candidate genes for human atrial fibrillation (such as HRH2, ATP4A, ATP4B, and HOPX) and epilepsy (e.g., PAX6 and NKX2-1). We suggest gene candidates for pharmacologically-induced seizures in mouse, solely based on orthologous phenotypes from E. coli. We also explore the prediction of plant gene-phenotype associations, as for the Arabidopsis response to vernalization phenotype. We are able to rank gene predictions for a significant portion of the diseases in the Online Mendelian Inheritance in Man database. Additionally, our method suggests candidate genes for mammalian seizures based only on bacterial phenotypes and gene orthology. We demonstrate that phenotype information may come from diverse sources, including drug sensitivities, gene ontology biological processes, and in situ hybridization annotations. Finally, we offer testable candidates for a variety of human diseases, plant traits, and other classes of phenotypes across a wide array of species.

Influence analysis for high-dimensional time series with an application to epileptic seizure onset zone detection

PubMed Central

Flamm, Christoph; Graef, Andreas; Pirker, Susanne; Baumgartner, Christoph; Deistler, Manfred

2013-01-01

Granger causality is a useful concept for studying causal relations in networks. However, numerical problems occur when applying the corresponding methodology to high-dimensional time series showing co-movement, e.g. EEG recordings or economic data. In order to deal with these shortcomings, we propose a novel method for the causal analysis of such multivariate time series based on Granger causality and factor models. We present the theoretical background, successfully assess our methodology with the help of simulated data and show a potential application in EEG analysis of epileptic seizures. PMID:23354014

HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.

PubMed

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2017-08-01

Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Common time-frequency analysis of local field potential and pyramidal cell activity in seizure-like events of the rat hippocampus

NASA Astrophysics Data System (ADS)

Cotic, M.; Chiu, A. W. L.; Jahromi, S. S.; Carlen, P. L.; Bardakjian, B. L.

2011-08-01

To study cell-field dynamics, physiologists simultaneously record local field potentials and the activity of individual cells from animals performing cognitive tasks, during various brain states or under pathological conditions. However, apart from spike shape and spike timing analyses, few studies have focused on elucidating the common time-frequency structure of local field activity relative to surrounding cells across different periods of phenomena. We have used two algorithms, multi-window time frequency analysis and wavelet phase coherence (WPC), to study common intracellular-extracellular (I-E) spectral features in spontaneous seizure-like events (SLEs) from rat hippocampal slices in a low magnesium epilepsy model. Both algorithms were applied to 'pairs' of simultaneously observed I-E signals from slices in the CA1 hippocampal region. Analyses were performed over a frequency range of 1-100 Hz. I-E spectral commonality varied in frequency and time. Higher commonality was observed from 1 to 15 Hz, and lower commonality was observed in the 15-100 Hz frequency range. WPC was lower in the non-SLE region compared to SLE activity; however, there was no statistical difference in the 30-45 Hz band between SLE and non-SLE modes. This work provides evidence of strong commonality in various frequency bands of I-E SLEs in the rat hippocampus, not only during SLEs but also immediately before and after.

Seizures and epileptiform activity in the early stages of Alzheimer disease.

PubMed

Vossel, Keith A; Beagle, Alexander J; Rabinovici, Gil D; Shu, Huidy; Lee, Suzee E; Naasan, Georges; Hegde, Manu; Cornes, Susannah B; Henry, Maya L; Nelson, Alexandra B; Seeley, William W; Geschwind, Michael D; Gorno-Tempini, Maria L; Shih, Tina; Kirsch, Heidi E; Garcia, Paul A; Miller, Bruce L; Mucke, Lennart

2013-09-01

Epileptic activity associated with Alzheimer disease (AD) deserves increased attention because it has a harmful impact on these patients, can easily go unrecognized and untreated, and may reflect pathogenic processes that also contribute to other aspects of the illness. We report key features of AD-related seizures and epileptiform activity that are instructive for clinical practice and highlight similarities between AD and transgenic animal models of the disease. To describe common clinical characteristics and treatment outcomes of patients with amnestic mild cognitive impairment (aMCI) or early AD who also have epilepsy or subclinical epileptiform activity. Retrospective observational study from 2007 to 2012. SETTING Memory and Aging Center, University of California, San Francisco. We studied 54 patients with a diagnosis of aMCI plus epilepsy (n = 12), AD plus epilepsy (n = 35), and AD plus subclinical epileptiform activity (n = 7). Clinical and demographic data, electroencephalogram (EEG) readings, and treatment responses to antiepileptic medications. Patients with aMCI who had epilepsy presented with symptoms of cognitive decline 6.8 years earlier than patients with aMCI who did not have epilepsy (64.3 vs 71.1 years; P = .02). Patients with AD who had epilepsy presented with cognitive decline 5.5 years earlier than patients with AD who did not have epilepsy (64.8 vs 70.3 years; P = .001). Patients with AD who had subclinical epileptiform activity also had an early onset of cognitive decline (58.9 years). The timing of seizure onset in patients with aMCI and AD was nonuniform (P < .001), clustering near the onset of cognitive decline. Epilepsies were most often complex partial seizures (47%) and more than half were nonconvulsive (55%). Serial or extended EEG monitoring appeared to be more effective than routine EEG at detecting interictal and subclinical epileptiform activity. Epileptic foci were predominantly unilateral and temporal. Of the most commonly prescribed antiepileptics, treatment outcomes appeared to be better for lamotrigine and levetiracetam than for phenytoin. Common clinical features of patients with aMCI- or AD-associated epilepsy at our center included early age at onset of cognitive decline, early incidence of seizures in the disease course, unilateral temporal epileptic foci detected by serial/extended EEG, transient cognitive dysfunction, and good seizure control and tolerability with lamotrigine and levetiracetam. Careful identification and treatment of epilepsy in such patients may improve their clinical course.

Epileptic activity in Alzheimer’s disease: causes and clinical relevance

PubMed Central

Vossel, Keith A; Tartaglia, Maria C; Nygaard, Haakon B; Zeman, Adam Z; Miller, Bruce L

2018-01-01

Epileptic activity is frequently associated with Alzheimer’s disease; this association has therapeutic implications, because epileptic activity can occur at early disease stages and might contribute to pathogenesis. In clinical practice, seizures in patients with Alzheimer’s disease can easily go unrecognised because they usually present as non-motor seizures, and can overlap with other symptoms of the disease. In patients with Alzheimer’s disease, seizures can hasten cognitive decline, highlighting the clinical relevance of early recognition and treatment. Some evidence indicates that subclinical epileptiform activity in patients with Alzheimer’s disease, detected by extended neurophysiological monitoring, can also lead to accelerated cognitive decline. Treatment of clinical seizures in patients with Alzheimer’s disease with select antiepileptic drugs (AEDs), in low doses, is usually well tolerated and efficacious. Moreover, studies in mouse models of Alzheimer’s disease suggest that certain classes of AEDs that reduce network hyperexcitability have disease-modifying properties. These AEDs target mechanisms of epileptogenesis involving amyloid β and tau. Clinical trials targeting network hyperexcitability in patients with Alzheimer’s disease will identify whether AEDs or related strategies could improve their cognitive symptoms or slow decline. PMID:28327340

Microbiological detection of bacteria in animal products seized in baggage of international air passengers to Brazil.

PubMed

de Melo, Cristiano Barros; de Sá, Marcos Eielson Pinheiro; Sabino, Valéria Mourão; de Fatima Boechat-Fernandes, Maria; Santiago, Marco Túlio; Schwingel, Fábio Fraga; Freitas, Cleverson; Magioli, Carlos Alberto; Cabral-Pinto, Sergio; McManus, Concepta; Seixas, Luiza

2015-01-01

Airline travel favours the transmission of diseases, given the short time it takes to travel long distances. In this study, animal products without health certificates seized in international air passengers' baggage at Guarulhos (GRU) and Galeão (GIG) airports in Brazil underwent a microbiological evaluation. Analyses (1610) were carried out on 322 seizures to test for the presence of total and thermotolerant coliforms, as well as Staphylococcus aureus counts and the presence of Listeria monocytogenes and Salmonella. Most seizures analysed showed coliform contamination and coliforms were present above acceptable limits in 83.4% (40/48) of the products that had some type of contamination. The second most prevalent microorganism found was L. monocytogenes in 22.9% (11/48) and S. aureus was cultivated in 14.58% (7/48) of seizures. Among the items seized in the present work, Salmonella was found in one seizure of pig sausage. Contamination of animal products with microbiological pathogens of importance to public health and indicators of the bad quality of the food were shown in the present study. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Ketogenic diet change cPLA2/clusterin and autophagy related gene expression and correlate with cognitive deficits and hippocampal MFs sprouting following neonatal seizures.

PubMed

Ni, Hong; Zhao, Dong-Jing; Tian, Tian

2016-02-01

Because the ketogenic diet (KD) was affecting expression of energy metabolism- related genes in hippocampus and because lipid membrane peroxidation and its associated autophagy stress were also found to be involved in energy depletion, we hypothesized that KD might exert its neuroprotective action via lipid membrane peroxidation and autophagic signaling. Here, we tested this hypothesis by examining the long-term expression of lipid membrane peroxidation-related cPLA2 and clusterin, its downstream autophagy marker Beclin-1, LC3 and p62, as well as its execution molecule Cathepsin-E following neonatal seizures and chronic KD treatment. On postnatal day 9 (P9), 48 Sprague-Dawley rats were randomly assigned to two groups: flurothyl-induced recurrent seizures group and control group. On P28, they were further randomly divided into the seizure group without ketogenic diet (RS+ND), seizure plus ketogenic diet (RS+KD), the control group without ketogenic diet (NS+ND), and the control plus ketogenic diet (NS+KD). Morris water maze test was performed during P37-P43. Then mossy fiber sprouting and the protein levels were detected by Timm staining and Western blot analysis, respectively. Flurothyl-induced RS+ND rats show a long-term lower amount of cPLA2 and LC3II/I, and higher amount of clusterin, Beclin-1, p62 and Cathepsin-E which are in parallel with hippocampal mossy fiber sprouting and cognitive deficits. Furthermore, chronic KD treatment (RS+KD) is effective in restoring these molecular, neuropathological and cognitive changes. The results imply that a lipid membrane peroxidation and autophagy-associated pathway is involved in the aberrant hippocampal mossy fiber sprouting and cognitive deficits following neonatal seizures, which might be a potential target of KD for the treatment of neonatal seizure-induced brain damage. Copyright © 2015 Elsevier B.V. All rights reserved.

Segmentation of the thalamus based on BOLD frequencies affected in temporal lobe epilepsy.

PubMed

Morgan, Victoria L; Rogers, Baxter P; Abou-Khalil, Bassel

2015-11-01

Temporal lobe epilepsy is associated with functional changes throughout the brain, particularly including a putative seizure propagation network involving the hippocampus, insula, and thalamus. We identified a specified frequency range where functional connectivity in this network was related to duration of disease. Then, to identify specific thalamic nuclei involved in seizure propagation, we determined the subregions of the thalamus that have increased resting functional oscillations in this frequency range. Resting-state functional magnetic resonance imaging (fMRI) was acquired from 20 patients with unilateral temporal lobe epilepsy (TLE; 14 right and 6 left) and 20 healthy controls who were each age and gender matched to a specific patient. Wavelet-based fMRI connectivity mapping across the network was computed at each frequency to determine those frequencies where connectivity significantly decreases with duration of disease consistent with impairment due to repeated seizures. The voxel-wise power of the spontaneous blood oxygenation fluctuations of this frequency band was computed in the thalamus of each subject. Functional connectivity was impaired in the proposed seizure propagation network over a specific range (0.0067-0.013 Hz and 0.024-0.032 Hz) of blood oxygenation oscillations. Increased power in this frequency band (<0.032 Hz) was detected bilaterally in the pulvinar and anterior nucleus of the thalamus of healthy controls, and was increased over the ipsilateral thalamus compared to the contralateral thalamus in TLE. This study identified frequencies of impaired connectivity in a TLE seizure propagation network and used them to localize the anterior nucleus and pulvinar of the thalamus as subregions most susceptible to TLE seizures. Further examinations of these frequencies in healthy and TLE subjects may provide unique information relating to the mechanism of seizure propagation and potential treatment using electrical stimulation. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Clinical features of limbic encephalitis with LGI1 antibody

PubMed Central

Wang, Meiling; Cao, Xiaoyu; Liu, Qingxin; Ma, Wenbin; Guo, Xiaoqian; Liu, Xuewu

2017-01-01

Objective The objective of this study was to analyze the clinical manifestation, course, evolution, image manifestation, and treatments of LGI1 limbic encephalitis (LE). Patients and methods Studies confirmed that LE with the complex antibody of voltage-gated potassium channels is LGI1 LE. Since then, LE cases have been reported. In this study, 10 typical LE cases were searched in PubMed. These cases and one additional case, which we reported herein, were retrospectively analyzed. Results All the patients suffered from recent memory deterioration. The following cases were observed: eight with faciobrachial dystonic seizures (FBDS), six with different kinds of epileptic seizures (four complex partial seizures, one myoclonus seizure, and one generalized tonic–clonic seizure), four with FBDS and different kinds of epileptic seizures at the same time, five with mental disorders (one visual hallucination, one paranoia, one depression, one anxiety, and one dysphoria), five with hyponatremia, and two with sleep disorder. The brain MRI of nine patients revealed abnormalities in the mediotemporal lobe and the hippocampus. The LGI1 antibodies in the blood and/or cerebrospinal fluid (CSF) were positive. The content of the CSF protein of two patients increased slightly. The tumor marker of all the patients was normal, but capitate myxoma was detected in the combined pancreas duct of one patient. Gamma globulin and hormone treatments were administered to nine patients. Of these patients, six received a combination of antiepileptic drugs. The clinical symptoms of all the patients improved. Conclusion LGI1 LE is an autoimmune encephalitis whose clinical manifestations are memory deterioration, FBDS, epileptic seizure, mental disorders, and hyponatremia. Brain MRI shows that this autoimmune disease mainly involves the mediotemporal lobe and the hippocampus. This condition can also be manifested with other autoimmune encephalitis cases but can be rarely associated with tumors. After patients with LGI1 LE receive gamma globulin and hormone treatments, their clinical prognosis is good. PMID:28670128

Is There a Relation between EEG-Slow Waves and Memory Dysfunction in Epilepsy? A Critical Appraisal

PubMed Central

Höller, Yvonne; Trinka, Eugen

2015-01-01

Is there a relationship between peri-ictal slow waves, loss of consciousness, memory, and slow-wave sleep, in patients with different forms of epilepsy? We hypothesize that mechanisms, which result in peri-ictal slow-wave activity as detected by the electroencephalogram, could negatively affect memory processes. Slow waves (≤4 Hz) can be found in seizures with impairment of consciousness and also occur in focal seizures without impairment of consciousness but with inhibited access to memory functions. Peri-ictal slow waves are regarded as dysfunctional and are probably caused by mechanisms, which are essential to disturb the consolidation of memory entries in these patients. This is in strong contrast to physiological slow-wave activity during deep sleep, which is thought to group memory-consolidating fast oscillatory activity. In patients with epilepsy, slow waves may not only correlate with the peri-ictal clouding of consciousness, but could be the epiphenomenon of mechanisms, which interfere with normal brain function in a wider range. These mechanisms may have transient impacts on memory, such as temporary inhibition of memory systems, altered patterns of hippocampal–neocortical interactions during slow-wave sleep, or disturbed cross-frequency coupling of slow and fast oscillations. In addition, repeated tonic–clonic seizures over the years in uncontrolled chronic epilepsy may cause a progressive cognitive decline. This hypothesis can only be assessed in long-term prospective studies. These studies could disentangle the reversible short-term impacts of seizures, and the impacts of chronic uncontrolled seizures. Chronic uncontrolled seizures lead to irreversible memory impairment. By contrast, short-term impacts do not necessarily lead to a progressive cognitive decline but result in significantly impaired peri-ictal memory performance. PMID:26124717

Intraoperative Magnetic-Resonance Tomography and Neuronavigation During Resection of Focal Cortical Dysplasia Type II in Adult Epilepsy Surgery Offers Better Seizure Outcomes.

PubMed

Roessler, Karl; Kasper, Burkhard S; Heynold, Elisabeth; Coras, Roland; Sommer, Björn; Rampp, Stefan; Hamer, Hajo M; Blümcke, Ingmar; Buchfelder, Michael

2018-01-01

Focal cortical dysplasia (FCD) is one important cause of drug-resistant epilepsy potentially curable by epilepsy surgery. We investigated the options of using neuronavigation and intraoperative magnetic-resonance tomographical imaging (MRI) to avoid residual epileptogenic tissue during resection of patients with FCD II to improve seizure outcome. Altogether, 24 patients with FCD II diagnosed by MRI (16 female, 8 male; mean age 34 ± 10 years) suffered from drug-resistant electroclinical and focal epilepsy for a mean of 20.7 ± 5 years. Surgery was performed with preoperative stereoelectroencephalography (in 15 patients), neuronavigation, and intraoperative 1.5T-iopMRI in all 24 investigated patients. In 75% of patients (18/24), a complete resection was performed. In 89% (16/18) of completely resected patients, we documented an Engel I seizure outcome after a mean follow-up of 42 months. All incompletely resected patients had a worse outcome (Engel II-III, P < 0.0002). Patients with FCD IIB had also significant better seizure outcome compared with patients diagnosed as having FCD IIA (82% vs. 28%, P < 0.02). In 46% (11/24) of patients, intraoperative second-look surgeries due to residual lesions detected during the intraoperative MRI were performed. In these 11 patients, there were significant more completely seizure free patients (73% vs. 38% Engel IA), compared with 13 patients who finished surgery after the first intraoperative MRI (P < 0.05). Excellent seizure outcome after surgery of patients with FCD II positively correlated with the amount of resection, histologic subtype, and the use of intraoperative MRI, especially when intraoperative second-look surgeries were performed. Copyright © 2017 Elsevier Inc. All rights reserved.

Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy.

PubMed

Liu, Su; Gurses, Candan; Sha, Zhiyi; Quach, Michael M; Sencer, Altay; Bebek, Nerses; Curry, Daniel J; Prabhu, Sujit; Tummala, Sudhakar; Henry, Thomas R; Ince, Nuri F

2018-01-30

High-frequency oscillations in local field potentials recorded with intracranial EEG are putative biomarkers of seizure onset zones in epileptic brain. However, localized 80-500 Hz oscillations can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones, which limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 epilepsy patients and five control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified by using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent from the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step towards the translation of these signature events into valid clinical biomarkers.awx374media15721572971001. © The Author(s) (2018). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

[Evolution of ideas and techniques, and future prospects in epilepsy surgery].

PubMed

Mathon, B; Bédos-Ulvin, L; Baulac, M; Dupont, S; Navarro, V; Carpentier, A; Cornu, P; Clemenceau, S

2015-02-01

The aim of this article was to review and evaluate the published literature related to the outcome of epilepsy surgery, while placing it in an historical perspective, and to describe the future prospects in this field. Temporal lobe surgery achieves seizure freedom in about 70% of cases. Seizure outcome is similar in the pediatric population. Extratemporal resections impart good results to 40% to 60% of patients, with a better prognosis in the case of frontal lobe surgery. Pediatric hemispherotomy leads to seizure control in about 80% of children. Radiosurgery used as a treatment for temporal mesial epilepsy has an outcome quite similar to that obtained with surgical resection, but provides a neuropsychological advantage. Radiosurgery is also effective in 60% of children treated for seizures related to hypothalamic hamartoma. Regarding palliative surgery, callosotomy and multiple subpial transections show satisfactory outcomes in over 60% of cases. Neuromodulation techniques (vagus nerve stimulation and bilateral stimulation of the anterior nucleus of the thalamus) allow a 50% reduction of seizures in half of patients. Transcranial magnetic stimulation combined with electroencephalography seems a promising technique because of its diagnostic, prognostic and therapeutic applications. Transcranial ultrasound stimulation, which can reversibly control neuronal activity, is also under consideration. Concerning neuromodulation, trigeminal nerve stimulation may become an alternative to vagus nerve stimulation; while other targets of deep brain stimulation are being evaluated. Also, the possibility of coupling SEEG seizure focus detection with concomitant laser or radiofrequency focus destruction is under development. Constant evolution of epilepsy surgery has improved patient outcomes over time. Current research and development axes suggest the continuation of this trend and a reduction of the invasiveness of surgical procedures. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Utility of Continuous EEG Monitoring in Noncritically lll Hospitalized Patients.

PubMed

Billakota, Santoshi; Sinha, Saurabh R

2016-10-01

Continuous EEG (cEEG) monitoring is used in the intensive care unit (ICU) setting to detect seizures, especially nonconvulsive seizures and status epilepticus. The utility and impact of such monitoring in non-ICU patients are largely unknown. Hospitalized patients who were not in an ICU and underwent cEEG monitoring in the first half of 2011 and 2014 were identified. Reason for admission, admitting service (neurologic and nonneurologic), indication for cEEG, comorbid conditions, duration of recording, EEG findings, whether an event/seizure was recorded, and impact of EEG findings on management were reviewed. We evaluated the impact of the year of recording, admitting service, indication for cEEG, and neurologic comorbidity on the yield of recordings based on whether an event was captured and/or a change in antiepileptic drug management occurred. Two hundred forty-nine non-ICU patients had cEEG monitoring during these periods. The indication for cEEG was altered mental status (60.6%), observed seizures (26.5%), or observed spells (12.9%); 63.5% were on neuro-related services. The average duration of recording was 1.8 days. EEG findings included interictal epileptiform discharges (14.9%), periodic lateralized discharges (4%), and generalized periodic discharges (1.6%). Clinical events were recorded in 28.1% and seizures in 16.5%. The cEEG led to a change in antiepileptic drug management in 38.6% of patients. There was no impact of type of admitting service; there was no significant impact of indication for cEEG. In non-ICU patients, cEEG monitoring had a relatively high yield of event/seizures (similar to ICU) and impact on management. Temporal trends, admitting service, and indication for cEEG did not alter this.

Analysis of absence seizure generation using EEG spatial-temporal regularity measures.

PubMed

Mammone, Nadia; Labate, Domenico; Lay-Ekuakille, Aime; Morabito, Francesco C

2012-12-01

Epileptic seizures are thought to be generated and to evolve through an underlying anomaly of synchronization in the activity of groups of neuronal populations. The related dynamic scenario of state transitions is revealed by detecting changes in the dynamical properties of Electroencephalography (EEG) signals. The recruitment procedure ending with the crisis can be explored through a spatial-temporal plot from which to extract suitable descriptors that are able to monitor and quantify the evolving synchronization level from the EEG tracings. In this paper, a spatial-temporal analysis of EEG recordings based on the concept of permutation entropy (PE) is proposed. The performance of PE are tested on a database of 24 patients affected by absence (generalized) seizures. The results achieved are compared to the dynamical behavior of the EEG of 40 healthy subjects. Being PE a feature which is dependent on two parameters, an extensive study of the sensitivity of the performance of PE with respect to the parameters' setting was carried out on scalp EEG. Once the optimal PE configuration was determined, its ability to detect the different brain states was evaluated. According to the results here presented, it seems that the widely accepted model of "jump" transition to absence seizure should be in some cases coupled (or substituted) by a gradual transition model characteristic of self-organizing networks. Indeed, it appears that the transition to the epileptic status is heralded before the preictal state, ever since the interictal stages. As a matter of fact, within the limits of the analyzed database, the frontal-temporal scalp areas appear constantly associated to PE levels higher compared to the remaining electrodes, whereas the parieto-occipital areas appear associated to lower PE values. The EEG of healthy subjects neither shows any similar dynamic behavior nor exhibits any recurrent portrait in PE topography.

Spatial variation in automated burst suppression detection in pharmacologically induced coma.

PubMed

An, Jingzhi; Jonnalagadda, Durga; Moura, Valdery; Purdon, Patrick L; Brown, Emery N; Westover, M Brandon

2015-01-01

Burst suppression is actively studied as a control signal to guide anesthetic dosing in patients undergoing medically induced coma. The ability to automatically identify periods of EEG suppression and compactly summarize the depth of coma using the burst suppression probability (BSP) is crucial to effective and safe monitoring and control of medical coma. Current literature however does not explicitly account for the potential variation in burst suppression parameters across different scalp locations. In this study we analyzed standard 19-channel EEG recordings from 8 patients with refractory status epilepticus who underwent pharmacologically induced burst suppression as medical treatment for refractory seizures. We found that although burst suppression is generally considered a global phenomenon, BSP obtained using a previously validated algorithm varies systematically across different channels. A global representation of information from individual channels is proposed that takes into account the burst suppression characteristics recorded at multiple electrodes. BSP computed from this representative burst suppression pattern may be more resilient to noise and a better representation of the brain state of patients. Multichannel data integration may enhance the reliability of estimates of the depth of medical coma.

Melatonin improves sleep in children with epilepsy: randomized, double-blind cross-over study

PubMed Central

Jain, Sejal V; Horn, Paul S; Simakajornboon, Narong; Beebe, Dean W; Holland, Katherine; Byars, Anna W; Glauser, Tracy A

2015-01-01

Objective Insomnia, especially maintenance insomnia is widely prevalent in epilepsy. Although melatonin is commonly used, limited data address its efficacy. We performed a randomized, double-blind, placebo-controlled, cross-over study to identify the effects of melatonin on sleep and seizure control in children with epilepsy. Methods Eleven pre-pubertal, developmentally normal children aged 6–11 years with epilepsy were randomized by software algorithm to receive placebo or 9 mg sustained release melatonin for 4 weeks, followed by a 1-week washout and 4-week crossover condition. The pharmacy performed blinding; patients, parents and study staff other than a statistician were blinded. Primary outcomes were sleep onset latency and wakefulness after sleep onset (WASO) measured on polysomnography. Secondary outcomes included seizure frequency, epileptiform spike density per hour of sleep on EEG and reaction time measures on psychomotor vigilance task. Statistical tests appropriate for cross-over designs were used for analysis. Results Data were analyzed from ten subjects who completed the study. Melatonin decreased sleep latency (Mean difference (MD): 11.4 min, p= 0.02) and WASO (MD 22 min, p=0.04) as compared to placebo. No worsening of spike density or seizure frequency was seen. Additionally, Slow-wave sleep duration and REM latency were increased with melatonin and REM sleep duration was decreased. These changes were statistically significant. Worsening of headache was noted in one subject with migraine on melatonin. Conclusion Sustained-release melatonin resulted in statistically significant decreases in sleep latency and WASO. No clear effects on seizures were observed but the study was too small to allow any conclusions to be drawn in this regard. PMID:25862116

A KCNQ2 E515D mutation associated with benign familial neonatal seizures and continuous spike and waves during slow-wave sleep syndrome in Taiwan.

PubMed

Lee, Inn-Chi; Yang, Jiann-Jou; Li, Shuan-Yow

2017-09-01

Pediatric epilepsy caused by a KCNQ2 gene mutation usually manifests as benign familial neonatal seizures (BFNS) during the 1 st week of life. However, the exact mechanism, phenotype, and genotype of the KCNQ2 mutation are unclear. We studied the KCNQ2 genotype from 75 nonconsanguineous patients with childhood epilepsy without an identified cause (age range: from 2 days to 18 years) and from 55 healthy adult controls without epilepsy. KCNQ2 mutation variants were transfected into HEK293 cells to investigate what functional changes they induced. Four (5%) of the patients had the E515D KCNQ2 mutation, which the computer-based PolyPhen algorithm predicted to be deleterious. Their seizure outcomes were favorable, but three had an intellectual disability. Two patients with E515D presented with continuous spikes and waves during slow-wave sleep (CSWS), and the other two presented with BFNS. We also analyzed 10 affected family members with the same KCNQ2 mutation: all had epilepsy (8 had BFNS and 2 had CSWS). A functional analysis showed that the recordings of the E515D currents were significantly different (p<0.05), which suggested that channels with KCNQ2 E515D variants are less sensitive to voltage and require stronger depolarization to reach opening probabilities than those with the wild type or N780T (a benign polymorphism). KCNQ2 mutations can cause various phenotypes in children: they lead to BFNS and CSWS. We hypothesize that patients with the KCNQ2 E515D mutation are susceptible to seizures. Copyright © 2016. Published by Elsevier B.V.

Screening for Physical Problems in Classrooms for Severely Handicapped Students.

ERIC Educational Resources Information Center

Dever, Richard; Knapczyk, Dennis

1980-01-01

The authors present a screening device with which teachers of severely handicapped students may detect the presence of a physical problem. The screening approach covers vision, auditory problems, seizures, orthopedic problems, and pain. (CL)

Termination of seizure clusters is related to the duration of focal seizures.

PubMed

Ferastraoaru, Victor; Schulze-Bonhage, Andreas; Lipton, Richard B; Dümpelmann, Matthias; Legatt, Alan D; Blumberg, Julie; Haut, Sheryl R

2016-06-01

Clustered seizures are characterized by shorter than usual interseizure intervals and pose increased morbidity risk. This study examines the characteristics of seizures that cluster, with special attention to the final seizure in a cluster. This is a retrospective analysis of long-term inpatient monitoring data from the EPILEPSIAE project. Patients underwent presurgical evaluation from 2002 to 2009. Seizure clusters were defined by the occurrence of at least two consecutive seizures with interseizure intervals of <4 h. Other definitions of seizure clustering were examined in a sensitivity analysis. Seizures were classified into three contextually defined groups: isolated seizures (not meeting clustering criteria), terminal seizure (last seizure in a cluster), and intracluster seizures (any other seizures within a cluster). Seizure characteristics were compared among the three groups in terms of duration, type (focal seizures remaining restricted to one hemisphere vs. evolving bilaterally), seizure origin, and localization concordance among pairs of consecutive seizures. Among 92 subjects, 77 (83%) had at least one seizure cluster. The intracluster seizures were significantly shorter than the last seizure in a cluster (p = 0.011), whereas the last seizure in a cluster resembled the isolated seizures in terms of duration. Although focal only (unilateral), seizures were shorter than seizures that evolved bilaterally and there was no correlation between the seizure type and the seizure position in relation to a cluster (p = 0.762). Frontal and temporal lobe seizures were more likely to cluster compared with other localizations (p = 0.009). Seizure pairs that are part of a cluster were more likely to have a concordant origin than were isolated seizures. Results were similar for the 2 h definition of clustering, but not for the 8 h definition of clustering. We demonstrated that intracluster seizures are short relative to isolated seizures and terminal seizures. Frontal and temporal lobe seizures are more likely to cluster. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

Enzyme-inducing anticonvulsants increase plasma clearance of dexmedetomidine: a pharmacokinetic and pharmacodynamic study.

PubMed

Flexman, Alana M; Wong, Harvey; Riggs, K Wayne; Shih, Tina; Garcia, Paul A; Vacas, Susana; Talke, Pekka O

2014-05-01

Dexmedetomidine is useful during mapping of epileptic foci as it facilitates electrocorticography unlike most other anesthetic agents. Patients with seizure disorders taking enzyme-inducing anticonvulsants appear to be resistant to its sedative effects. The objective of the study was to compare the pharmacokinetic and pharmacodynamic profile of dexmedetomidine in healthy volunteers with volunteers with seizure disorders receiving enzyme-inducing anticonvulsant medications. Dexmedetomidine was administered using a step-wise, computer-controlled infusion to healthy volunteers (n = 8) and volunteers with seizure disorders (n = 8) taking phenytoin or carbamazapine. Sedation and dexmedetomidine plasma levels were assessed at baseline, during the infusion steps, and after discontinuation of the infusion. Sedation was assessed by using the Observer's Assessment of Alertness/Sedation Scale, Ramsay Sedation Scale, and Visual Analog Scale and processed electroencephalography (entropy) monitoring. Pharmacokinetic analysis was performed on both groups, and differences between groups were determined using the standard two-stage approach. A two-compartment model was fit to dexmedetomidine concentration-time data. Dexmedetomidine plasma clearance was 43% higher in the seizure group compared with the control group (42.7 vs. 29.9 l/h; P = 0.007). In contrast, distributional clearance and the volume of distribution of the central and peripheral compartments were similar between the groups. No difference in sedation was detected between the two groups during a controlled range of target plasma concentrations. This study demonstrates that subjects with seizure disorders taking enzyme-inducing anticonvulsant medications have an increased plasma clearance of dexmedetomidine as compared with healthy control subjects.

Neuropathologic features of the hippocampus and amygdala in cats with familial spontaneous epilepsy.

PubMed

Yu, Yoshihiko; Hasegawa, Daisuke; Hamamoto, Yuji; Mizoguchi, Shunta; Kuwabara, Takayuki; Fujiwara-Igarashi, Aki; Tsuboi, Masaya; Chambers, James Ken; Fujita, Michio; Uchida, Kazuyuki

2018-03-01

OBJECTIVE To investigate epilepsy-related neuropathologic changes in cats of a familial spontaneous epileptic strain (ie, familial spontaneous epileptic cats [FSECs]). ANIMALS 6 FSECs, 9 age-matched unrelated healthy control cats, and 2 nonaffected (without clinical seizures)dams and 1 nonaffected sire of FSECs. PROCEDURES Immunohistochemical analyses were used to evaluate hippocampal sclerosis, amygdaloid sclerosis, mossy fiber sprouting, and granule cell pathological changes. Values were compared between FSECs and control cats. RESULTS Significantly fewer neurons without gliosis were detected in the third subregion of the cornu ammonis (CA) of the dorsal and ventral aspects of the hippocampus as well as the central nucleus of the amygdala in FSECs versus control cats. Gliosis without neuronal loss was also observed in the CA4 subregion of the ventral aspect of the hippocampus. No changes in mossy fiber sprouting and granule cell pathological changes were detected. Moreover, similar changes were observed in the dams and sire without clinical seizures, although to a lesser extent. CONCLUSIONS AND CLINICAL RELEVANCE Findings suggested that the lower numbers of neurons in the CA3 subregion of the hippocampus and the central nucleus of the amygdala were endophenotypes of familial spontaneous epilepsy in cats. In contrast to results of other veterinary medicine reports, severe epilepsy-related neuropathologic changes (eg, hippocampal sclerosis, amygdaloid sclerosis, mossy fiber sprouting, and granule cell pathological changes) were not detected in FSECs. Despite the use of a small number of cats with infrequent seizures, these findings contributed new insights on the pathophysiologic mechanisms of genetic-related epilepsy in cats.

Amplitude-integrated EEG and the newborn infant.

PubMed

Shah, Divyen K; Mathur, Amit

2014-01-01

There is emerging recognition of the need for continuous long term electrographic monitoring of the encephalopathic neonate. While full-montage EEG with video remains the gold standard for monitoring, it is limited in application due to the complexity of lead application and specialized interpretation of results. Amplitude integrated EEG (aEEG) is derived from limited channels (usually C3-P3, C4-P4) and is filtered, rectified and time-compressed to serve as a bedside electrographic trend monitor. Its simple application and interpretation has resulted in increasing use in neonatal units across the world. Validation studies with full montage EEG have shown reliable results in interpretation of EEG background and electrographic seizures, especially when used with the simultaneously displayed raw EEG trace. Several aEEG monitors are commercially available and seizure algorithms are being developed for use on these monitors. These aEEG monitors, complement conventional EEG and offer a significant advance in the feasibility of long term electrographic monitoring of the encephalopathic neonate.

Electroencephalography epilepsy classifications using hybrid cuckoo search and neural network

NASA Astrophysics Data System (ADS)

Pratiwi, A. B.; Damayanti, A.; Miswanto

2017-07-01

Epilepsy is a condition that affects the brain and causes repeated seizures. This seizure is episodes that can vary and nearly undetectable to long periods of vigorous shaking or brain contractions. Epilepsy often can be confirmed with an electrocephalography (EEG). Neural Networks has been used in biomedic signal analysis, it has successfully classified the biomedic signal, such as EEG signal. In this paper, a hybrid cuckoo search and neural network are used to recognize EEG signal for epilepsy classifications. The weight of the multilayer perceptron is optimized by the cuckoo search algorithm based on its error. The aim of this methods is making the network faster to obtained the local or global optimal then the process of classification become more accurate. Based on the comparison results with the traditional multilayer perceptron, the hybrid cuckoo search and multilayer perceptron provides better performance in term of error convergence and accuracy. The purpose methods give MSE 0.001 and accuracy 90.0 %.

Focal Epilepsy: MR Imaging of Nonhemodynamic Field Effects by Using a Phase-cycled Stimulus-induced Rotary Saturation Approach with Spin-Lock Preparation.

PubMed

Kiefer, Claus; Abela, Eugenio; Schindler, Kaspar; Wiest, Roland

2016-07-01

Purpose To investigate whether nonhemodynamic resonant saturation effects can be detected in patients with focal epilepsy by using a phase-cycled stimulus-induced rotary saturation (PC-SIRS) approach with spin-lock (SL) preparation and whether they colocalize with the seizure onset zone and surface interictal epileptiform discharges (IED). Materials and Methods The study was approved by the local ethics committee, and all subjects gave written informed consent. Eight patients with focal epilepsy undergoing presurgical surface and intracranial electroencephalography (EEG) underwent magnetic resonance (MR) imaging at 3 T with a whole-brain PC-SIRS imaging sequence with alternating SL-on and SL-off and two-dimensional echo-planar readout. The power of the SL radiofrequency pulse was set to 120 Hz to sensitize the sequence to high gamma oscillations present in epileptogenic tissue. Phase cycling was applied to capture distributed current orientations. Voxel-wise subtraction of SL-off from SL-on images enabled the separation of T2* effects from rotary saturation effects. The topography of PC-SIRS effects was compared with the seizure onset zone at intracranial EEG and with surface IED-related potentials. Bayesian statistics were used to test whether prior PC-SIRS information could improve IED source reconstruction. Results Nonhemodynamic resonant saturation effects ipsilateral to the seizure onset zone were detected in six of eight patients (concordance rate, 0.75; 95% confidence interval: 0.40, 0.94) by means of the PC-SIRS technique. They were concordant with IED surface negativity in seven of eight patients (0.88; 95% confidence interval: 0.51, 1.00). Including PC-SIRS as prior information improved the evidence of the standard EEG source models compared with the use of uninformed reconstructions (exceedance probability, 0.77 vs 0.12; Wilcoxon test of model evidence, P < .05). Nonhemodynamic resonant saturation effects resolved in patients with favorable postsurgical outcomes, but persisted in patients with postsurgical seizure recurrence. Conclusion Nonhemodynamic resonant saturation effects are detectable during interictal periods with the PC-SIRS approach in patients with epilepsy. The method may be useful for MR imaging-based detection of neuronal currents in a clinical environment. (©) RSNA, 2016 Online supplemental material is available for this article.

Pattern detection in forensic case data using graph theory: application to heroin cutting agents.

PubMed

Terrettaz-Zufferey, Anne-Laure; Ratle, Frédéric; Ribaux, Olivier; Esseiva, Pierre; Kanevski, Mikhail

2007-04-11

Pattern recognition techniques can be very useful in forensic sciences to point out to relevant sets of events and potentially encourage an intelligence-led style of policing. In this study, these techniques have been applied to categorical data corresponding to cutting agents found in heroin seizures. An application of graph theoretic methods has been performed, in order to highlight the possible relationships between the location of seizures and co-occurrences of particular heroin cutting agents. An analysis of the co-occurrences to establish several main combinations has been done. Results illustrate the practical potential of mathematical models in forensic data analysis.

Cortical myoclonus during IV thrombolysis for ischemic stroke

PubMed Central

Bentes, Carla; Peralta, Rita; Viana, Pedro; Morgado, Carlos; Melo, Teresa P.; Ferro, José M.

2014-01-01

We describe a patient with an acute middle cerebral artery ischemic stroke developing subtle involuntary movements of the paretic upper limb with cortical origin during rt-PA perfusion. Despite the multiple potential pathophysiological mechanisms for the relationship between thrombolysis and epileptic activity, seizures during this procedure are scarcely reported. Our hypothesis is that subtle and transient clinical seizures, like those described in our patient, may not be detected or are misdiagnosed as nonepileptic involuntary movements. We aimed to draw attention to the recognition challenge of this paroxysmal motor behavior, highlighting this clinical and neurophysiological identification using video recording and back-average analysis of the EEG. PMID:25667903

[Infantile meningitis caused by respiratory syncytial virus].

PubMed

Shirota, Go; Morozumi, Miyuki; Ubukata, Kimiko; Shiro, Hiroyuki

2011-11-01

Respiratory syncytial (RS) virus commonly causes infantile respiratory tract infection causing significant morbidity and mortality, but rarely meningitis. We report a case of meningitis caused by RS virus subgroup B in a 56-day-old boy admitted for high fever who underwent blood examination and lumbar puncture. Empirical chemotherapy was started with intravenous ampicillin, gentamicin, and cefotaxime based on laboratory data on CSF cells (84/microL) and serum CRP (13.8mg/dL) data. RS virus subgroup B was only detected using real-time PCR comprehensive reverse transcription from the first CSF, but no bacterial gene was detected. No bacteria grew from his CSF, urine, or blood. Fever and serum CRP dropped in a few days. He had neither seizures nor disturbance of consciousness and was discharged on day 11 after admission. No evidence of encephalopathy was detected in brain MRI or electroencephalography. RS virus rarely causes meningitis, but a percentage of RS-virus-infected infants exhibit symptoms such as seizure and disturbance of consciousness. We should recognize that the RS virus may cause neurological complications associated with high morbidity and mortality.

Familial mesial temporal lobe epilepsy maps to chromosome 4q13.2-q21.3.

PubMed

Hedera, P; Blair, M A; Andermann, E; Andermann, F; D'Agostino, D; Taylor, K A; Chahine, L; Pandolfo, M; Bradford, Y; Haines, J L; Abou-Khalil, B

2007-06-12

To report results of linkage analysis in a large family with autosomal dominant (AD) familial mesial temporal lobe epilepsy (FMTLE). Although FMTLE is a heterogeneous syndrome, one important subgroup is characterized by a relatively benign course, absence of antecedent febrile seizures, and absence of hippocampal sclerosis. These patients have predominantly simple partial seizures (SPS) and infrequent complex partial seizures (CPS), and intense and frequent déjà vu phenomenon may be the only manifestation of this epilepsy syndrome. No linkage has been described in this form of FMTLE. We identified a four-generation kindred with several affected members meeting criteria for FMTLE and enrolled 21 individuals who gave informed consent. Every individual was personally interviewed and examined; EEG and MRI studies were performed on three affected subjects. DNA was extracted from every enrolled individual. We performed a genome-wide search using an 8 cM panel and fine mapping was performed in the regions with a multipoint lod score >1. We sequenced the highest priority candidate genes. Inheritance was consistent with AD mode with reduced penetrance. Eleven individuals were classified as affected with FMTLE and we also identified two living asymptomatic individuals who had affected offspring. Seizure semiologies included predominantly SPS with déjà vu feeling, infrequent CPS, and rare secondarily generalized tonic-clonic seizures. No structural abnormalities, including hippocampal sclerosis, were detected on MRI performed on three individuals. Genetic analysis detected a group of markers with lod score >3 on chromosome 4q13.2-q21.3 spanning a 7 cM region. No ion channel genes are predicted to be localized within this locus. We sequenced all coding exons of sodium bicarbonate cotransporter (SLC4A) gene, which plays an important role in tissue excitability, and cyclin I (CCNI), because of its role in the cell migration and possibility of subtle cortical abnormalities. No disease-causing mutations were identified in these genes. We report identification of a genetic locus for familial mesial temporal lobe epilepsy. The identification of a disease-causing gene will contribute to our understanding of the pathogenesis of temporal lobe epilepsies.

Temporal pole abnormalities detected by 3 T MRI in temporal lobe epilepsy due to hippocampal sclerosis: No influence on seizure outcome after surgery.

PubMed

Casciato, Sara; Picardi, Angelo; D'Aniello, Alfredo; De Risi, Marco; Grillea, Giovanni; Quarato, Pier Paolo; Mascia, Addolorata; Grammaldo, Liliana G; Meldolesi, Giulio Nicolo'; Morace, Roberta; Esposito, Vincenzo; Di Gennaro, Giancarlo

2017-05-01

To assess the clinical significance of temporal pole abnormalities (temporopolar blurring, TB, and temporopolar atrophy, TA) detected by using 3 Tesla MRI in the preoperative workup in patients with temporal lobe epilepsy due to hippocampal sclerosis (TLE-HS) who underwent surgery. We studied 78 consecutive patients with TLE-HS who underwent surgery and were followed up for at least 2 years. Based on findings of pre-surgical 3 Tesla MRI, patients were subdivided in subgroups according to the presence of TB or TA. Subgroups were compared on demographic, clinical, neuropsychological data and seizure outcome. TB was found in 39 (50%) patients, while TA was found in 32 (41%) patients, always ipsilateral to HS, with a considerable degree of overlap (69%) between TB and TA (p=0.01). Patients with temporopolar abnormalities did not significantly differ from those without TB or TA with regard to sex, age, age of epilepsy onset, duration of epilepsy, history of febrile convulsions or birth complications, side of surgery, seizure frequency at surgery, presence of GTCSs, and, in particular, seizure outcome. On the other hand, TB patients show a less frequent family history of epilepsy (p<.05) while age at epilepsy onset showed a trend to be lower in the TB group (p=.09). Patients with temporopolar atrophy did not significantly differ from those without TA on any variable, except for age at epilepsy onset, which was significantly lower for the TA group (p<.05). History of birth complications and longer duration of epilepsy also showed a trend to be associated with TA (p=.08). Multivariate analysis corroborated the association between temporopolar abnormalities and absence of family history of epilepsy and history of birth complications. High-field 3 T MRI in the preoperative workup for epilepsy surgery confirms that temporopolar abnormalities are frequent findings in TLE-HS patients and may be helpful to lateralize the epileptogenic zone. Their presence did not influence seizure outcome. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

[A case of focal epilepsy manifesting multiple psychiatric auras].

PubMed

Ezura, Michinori; Kakisaka, Yosuke; Jin, Kazutaka; Kato, Kazuhiro; Iwasaki, Masaki; Fujikawa, Mayu; Aoki, Masashi; Nakasato, Nobukazu

2015-01-01

We present a case of epilepsy with multiple types of focal seizures that were misdiagnosed as psychiatric disorders. A 20-year-old female patient presented with a variety of episodes, including loss of consciousness, deja vu, fear, delusion of possession, violent movements, and generalized convulsions. Each of these symptoms appeared in a stereotypic manner. She was initially diagnosed with a psychiatric disorder and treated with psychoactive medications, which had no effect. Long-term video electroencephalography revealed that her episodes of violent movement with impaired consciousness and secondarily generalized seizure were epileptic events originating in the right hemisphere. High-field brain magnetic resonance imaging for detecting subtle lesions revealed bilateral lesions from periventricular nodular heterotopia. Her final diagnosis was right hemispheric focal epilepsy. Carbamazepine administration was started, which successfully controlled all seizures. The present case demonstrates the pitfall of diagnosing focal epilepsy when it presents with multiple types of psychiatric aura. Epilepsy should thus be included in differential diagnoses, considering the stereotypic nature of symptoms, to avoid misdiagnosis.

Chronic herpes simplex type-1 encephalitis with intractable epilepsy in an immunosuppressed patient.

PubMed

Laohathai, Christopher; Weber, Daniel J; Hayat, Ghazala; Thomas, Florian P

2016-02-01

Chronic herpes simplex virus type-1 encephalitis (HSE-1) is uncommon. Past reports focused on its association with prior documented acute infection. Here, we describe a patient with increasingly intractable epilepsy from chronic HSE-1 reactivation without history of acute central nervous system infection. A 49-year-old liver transplant patient with 4-year history of epilepsy after initiation of cyclosporine developed increasingly frequent seizures over 3 months. Serial brain magnetic resonance imaging showed left temporoparietal cortical edema that gradually improved despite clinical decline. Herpes simplex virus type-1 (HSV-1) DNA was detected in cerebrospinal fluid by polymerase chain reaction. Cerebrospinal fluid HSV-1&2 IgM was negative. Seizures were controlled after acyclovir treatment, and the patient remained seizure free at 1-year follow-up. Chronic HSE is a cause of intractable epilepsy, can occur without a recognized preceding acute phase, and the clinical course of infection may not directly correlate with neuroimaging changes.

Seizures and electroencephalography findings in 61 patients with fetal alcohol spectrum disorders.

PubMed

Boronat, S; Vicente, M; Lainez, E; Sánchez-Montañez, A; Vázquez, E; Mangado, L; Martínez-Ribot, L; Del Campo, M

2017-01-01

Fetal alcohol spectrum disorders (FASD) cause neurodevelopmental abnormalities. However, publications about epilepsy and electroencephalographic features are scarce. In this study, we prospectively performed electroencephalography (EEG) and brain magnetic resonance (MR) imaging in 61 patients with diagnosis of FASD. One patient had multiple febrile seizures with normal EEGs. Fourteen children showed EEG anomalies, including slow background activity and interictal epileptiform discharges, focal and/or generalized, and 3 of them had epilepsy. In one patient, seizures were first detected during the EEG recording and one case had an encephalopathy with electrical status epilepticus during slow sleep (ESES). Focal interictal discharges in our patients did not imply the presence of underlying visible focal brain lesions in the neuroimaging studies, such as cortical dysplasia or polymicrogyria. However, they had nonspecific brain MR abnormalities, including corpus callosum hypoplasia, vermis hypoplasia or cavum septum pellucidum. The latter was significantly more frequent in the group with EEG abnormal findings (p < 0.01). Copyright © 2016 Elsevier Masson SAS. All rights reserved.

GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.

PubMed

Tae Joon Jun; Hyun Ji Park; Hyuk Yoo; Young-Hak Kim; Daeyoung Kim

2016-08-01

In this paper, we propose an GPU based Cloud system for high-performance arrhythmia detection. Pan-Tompkins algorithm is used for QRS detection and we optimized beat classification algorithm with K-Nearest Neighbor (K-NN). To support high performance beat classification on the system, we parallelized beat classification algorithm with CUDA to execute the algorithm on virtualized GPU devices on the Cloud system. MIT-BIH Arrhythmia database is used for validation of the algorithm. The system achieved about 93.5% of detection rate which is comparable to previous researches while our algorithm shows 2.5 times faster execution time compared to CPU only detection algorithm.

From unwitnessed fatality to witnessed rescue: Nonpharmacologic interventions in sudden unexpected death in epilepsy.

PubMed

Rugg-Gunn, Fergus; Duncan, John; Hjalgrim, Helle; Seyal, Masud; Bateman, Lisa

2016-01-01

Sudden unexpected death in epilepsy (SUDEP) risk reduction remains a critical aim in epilepsy care. To date, only aggressive medical and surgical efforts to control seizures have been demonstrated to be of benefit. Incomplete understanding of SUDEP mechanisms limits the development of more specific interventions. Periictal cardiorespiratory dysfunction is implicated in SUDEP; postictal electroencephalography (EEG) suppression, coma, and immobility may also play a role. Nocturnal supervision is protective against SUDEP, presumably by permitting intervention in the case of a life-threatening event. Resuscitative efforts were implemented promptly in near-SUDEP cases but delayed in SUDEP deaths in the Mortality in Epilepsy Monitoring Unit Study (MORTEMUS) study. Nursing interventions--including repositioning, oral suctioning, and oxygen administration--reduce seizure duration, respiratory dysfunction, and EEG suppression in the epilepsy monitoring unit (EMU), but have not been studied in outpatients. Cardiac pacemakers or cardioverter-defibrillator devices may be of benefit in a few select individuals. A role for implantable neurostimulators has not yet been established. Seizure detection devices, including those that monitor generalized tonic-clonic seizure-associated movements or cardiorespiratory parameters, may provide a means to permit timely periictal intervention. However, these and other devices, such as antisuffocation pillows, have not been adequately investigated with respect to SUDEP prevention. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

Calcified parenchymal central nervous system cysticercosis and clinical outcomes in epilepsy.

PubMed

Leon, Amanda; Saito, Erin K; Mehta, Bijal; McMurtray, Aaron M

2015-02-01

This study aimed to compare clinical outcomes including seizure frequency and psychiatric symptoms between patients with epilepsy with neuroimaging evidence of past brain parenchymal neurocysticercosis infection, patients with other structural brain lesions, and patients without structural neuroimaging abnormalities. The study included retrospective cross-sectional analysis of all patients treated for epilepsy in a community-based adult neurology clinic during a three-month period. A total of 160 patients were included in the analysis, including 63 with neuroimaging findings consistent with past parenchymal neurocysticercosis infection, 55 with structurally normal brain neuroimaging studies, and 42 with other structural brain lesions. No significant differences were detected between groups for either seizure freedom (46.03%, 50.91%, and 47.62%, respectively; p=0.944) or mean seizure frequency per month (mean=2.50, S.D.=8.1; mean=4.83, S.D.=17.64; mean=8.55, S.D.=27.31, respectively; p=0.267). Self-reported depressive symptoms were more prevalent in those with parenchymal neurocysticercosis than in the other groups (p=0.003). No significant differences were detected for prevalence of self-reported anxiety or psychotic symptoms. Calcified parenchymal neurocysticercosis results in refractory epilepsy about as often as other structural brain lesions. Depressive symptoms may be more common among those with epilepsy and calcified parenchymal neurocysticercosis; consequently, screening for depression may be indicated in this population. Published by Elsevier Inc.

Bayesian inference of interaction properties of noisy dynamical systems with time-varying coupling: capabilities and limitations

NASA Astrophysics Data System (ADS)

Wilting, Jens; Lehnertz, Klaus

2015-08-01

We investigate a recently published analysis framework based on Bayesian inference for the time-resolved characterization of interaction properties of noisy, coupled dynamical systems. It promises wide applicability and a better time resolution than well-established methods. At the example of representative model systems, we show that the analysis framework has the same weaknesses as previous methods, particularly when investigating interacting, structurally different non-linear oscillators. We also inspect the tracking of time-varying interaction properties and propose a further modification of the algorithm, which improves the reliability of obtained results. We exemplarily investigate the suitability of this algorithm to infer strength and direction of interactions between various regions of the human brain during an epileptic seizure. Within the limitations of the applicability of this analysis tool, we show that the modified algorithm indeed allows a better time resolution through Bayesian inference when compared to previous methods based on least square fits.

Dissociation of spontaneous seizures and brainstem seizure thresholds in mice exposed to eight flurothyl-induced generalized seizures.

PubMed

Kadiyala, Sridhar B; Ferland, Russell J

2017-03-01

C57BL/6J mice exposed to eight flurothyl-induced generalized clonic seizures exhibit a change in seizure phenotype following a 28-day incubation period and subsequent flurothyl rechallenge. Mice now develop a complex seizure semiology originating in the forebrain and propagating into the brainstem seizure network (a forebrain→brainstem seizure). In contrast, this phenotype change does not occur in seizure-sensitive DBA/2J mice. The underlying mechanism(s) was the focus of these studies. DBA2/J mice were exposed to eight flurothyl-induced seizures (1/day) followed by 24-hour video-electroencephalographic recordings for 28-days. Forebrain and brainstem seizure thresholds were determined in C57BL/6J and DBA/2J mice following one or eight flurothyl-induced seizures, or after eight flurothyl-induced seizures, a 28-day incubation period, and final flurothyl rechallenge. Similar to C57BL/6J mice, DBA2/J mice expressed spontaneous seizures. However, unlike C57BL/6J mice, DBA2/J mice continued to have spontaneous seizures without remission. Because DBA2/J mice do not express forebrain→brainstem seizures following flurothyl rechallenge after a 28-day incubation period, this indicated that spontaneous seizures were not sufficient for the evolution of forebrain→brainstem seizures. Therefore, we determined whether brainstem seizure thresholds were changing during this repeated-flurothyl model and whether this could account for the expression of forebrain→brainstem seizures. Brainstem seizure thresholds were not different between C57BL/6J and DBA/2J mice on day one or on the last induction seizure trial (day eight). However, brainstem seizure thresholds did differ significantly on flurothyl rechallenge (day 28) with DBA/2J mice showing no lowering of their brainstem seizure thresholds. These results demonstrated that DBA/2J mice exposed to the repeated-flurothyl model develop spontaneous seizures without evidence of seizure remission and provide a new model of epileptogenesis. Moreover, these findings indicated that the transition of forebrain ictal discharge into the brainstem seizure network occurs due to changes in brainstem seizure thresholds that are independent of spontaneous seizure expression.

Rhythmic EEG patterns in extremely preterm infants: Classification and association with brain injury and outcome.

PubMed

Weeke, Lauren C; van Ooijen, Inge M; Groenendaal, Floris; van Huffelen, Alexander C; van Haastert, Ingrid C; van Stam, Carolien; Benders, Manon J; Toet, Mona C; Hellström-Westas, Lena; de Vries, Linda S

2017-12-01

Classify rhythmic EEG patterns in extremely preterm infants and relate these to brain injury and outcome. Retrospective analysis of 77 infants born <28 weeks gestational age (GA) who had a 2-channel EEG during the first 72 h after birth. Patterns detected by the BrainZ seizure detection algorithm were categorized: ictal discharges, periodic epileptiform discharges (PEDs) and other waveforms. Brain injury was assessed with sequential cranial ultrasound (cUS) and MRI at term-equivalent age. Neurodevelopmental outcome was assessed with the BSITD-III (2 years) and WPPSI-III-NL (5 years). Rhythmic patterns were observed in 62.3% (ictal 1.3%, PEDs 44%, other waveforms 86.3%) with multiple patterns in 36.4%. Ictal discharges were only observed in one and excluded from further analyses. The EEG location of the other waveforms (p<0.05), but not PEDs (p=0.238), was significantly associated with head position. No relation was found between the median total duration of each pattern and injury on cUS and MRI or cognition at 2 and 5 years. Clear ictal discharges are rare in extremely preterm infants. PEDs are common but their significance is unclear. Rhythmic waveforms related to head position are likely artefacts. Rhythmic EEG patterns may have a different significance in extremely preterm infants. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Unusual 4p16.3 deletions suggest an additional chromosome region for the Wolf-Hirschhorn syndrome-associated seizures disorder.

PubMed

Zollino, Marcella; Orteschi, Daniela; Ruiter, Mariken; Pfundt, Rolph; Steindl, Katharina; Cafiero, Concetta; Ricciardi, Stefania; Contaldo, Ilaria; Chieffo, Daniela; Ranalli, Domiziana; Acquafondata, Celeste; Murdolo, Marina; Marangi, Giuseppe; Asaro, Alessia; Battaglia, Domenica

2014-06-01

Seizure disorder is one of the most relevant clinical manifestations in Wolf-Hirschhorn syndrome (WHS) and it acts as independent prognostic factor for the severity of intellectual disability (ID). LETM1, encoding a mitochondrial protein playing a role in K(+) /H(+) exchange and in Ca(2+) homeostasis, is currently considered the major candidate gene. However, whether haploinsufficiency limited to LETM1 is enough to cause epilepsy is still unclear. The main purpose of the present research is to define the 4p chromosome regions where genes for seizures reside. Comparison of our three unusual 4p16.3 deletions with 13 literature reports. Array-comparative genomic hybridization (a-CGH). Real-time polymerase chain reaction (RT-PCR) on messanger RNA (mRNA) of LETM1 and CPLX1. Direct sequencing of LETM1. Three unusual 4p16.3 deletions were detected by array-CGH in absence of a obvious clinical diagnosis of WHS. Two of these, encompassing LETM1, were found in subjects who never had seizures. The deletions were interstitial, spanning 1.1 Mb with preservation of the terminal 1.77 Mb region in one case and 0.84 Mb with preservation of the terminal 1.07 Mb region in the other. The other deletion was terminal, affecting a 0.564 Mb segment, with preservation of LETM1, and it was associated with seizures and learning difficulties. Upon evaluating our patients along with literature reports, we noted that six of eight subjects with terminal 4p deletions preserving LETM1 had seizures, whereas seven of seven with interstitial deletions including LETM1 and preserving the terminal 1 Mb region on 4p did not. An additional chromosome region for seizures is suggested, falling within the terminal 1.5 Mb on 4p, not including LETM1. We consider that haploinsufficiency not limited to LETM1 but including other genes acts as a risk factor for the WHS-associated seizure disorder, according to a comorbidity model of pathogenesis. Additional candidate genes reside in the terminal 1.5 Mb region on 4p, most likely distal to LETM1. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

Rapamycin has paradoxical effects on S6 phosphorylation in rats with and without seizures.

PubMed

Chen, Linglin; Hu, Lin; Dong, Jing-Yin; Ye, Qing; Hua, Nan; Wong, Michael; Zeng, Ling-Hui

2012-11-01

  Accumulating data have demonstrated that seizures induced by kainate (KA) or pilocarpine activate the mammalian target of rapamycin (mTOR) pathway and that mTOR inhibitor rapamycin can inhibit mTOR activation, which subsequently has potential antiepileptic effects. However, a preliminary study showed a paradoxical exacerbation of increased mTOR pathway activity reflected by S6 phosphorylation when rapamycin was administrated within a short period before KA injection. In the present study, we examined this paradoxical effect of rapamycin in more detail, both in normal rats and KA-injected animals.   Normal rats or KA-treated rats pretreated with rapamycin at different time intervals were sacrificed at various time points (1, 3, 6, 10, 15, and 24 h) after rapamycin administration or seizure onset for western blotting analysis. Phosphorylation of mTOR signaling target of Akt, mTOR, Rictor, Raptor, S6K, and S6 were analyzed. Seizure activity was monitored behaviorally and graded according to a modified Racine scale (n = 6 for each time point). Neuronal cell death was detected by Fluoro-Jade B staining.   In normal rats, we found that rapamycin showed the expected dose-dependent inhibition of S6 phosphorylation 3-24 h after injection, whereas a paradoxical elevation of S6 phosphorylation was observed 1 h after rapamycin. Similarly, pretreatment with rapamycin over 10 h before KA inhibited the KA seizure-induced mTOR activation. In contrast, rapamycin administered 1-6 h before KA caused a paradoxical increase in the KA seizure-induced mTOR activation. Rats pretreated with rapamycin 1 h prior to KA exhibited an increase in severity and duration of seizures and more neuronal cell death as compared to vehicle-treated groups. In contrast, rapamycin pretreated 10 h prior to KA had no effect on the seizures and decreased neuronal cell death. The paradoxical effect of rapamycin on S6 phosphorylation was correlated with upstream mTOR signaling and was reversed by pretreatment of perifosine, an Akt inhibitor.   These data indicate the complexity of S6 regulation and its effect on epilepsy. Paradoxical effects of rapamycin need to be considered in clinical applications, such as for potential treatment for epilepsy and other neurologic disorders. Wiley Periodicals, Inc. © 2012 International League Against Epilepsy.

Rapamycin has Paradoxical Effects on S6 Phosphorylation in Rats With and Without Seizures

PubMed Central

Chen, Linglin; Hu, Lin; Dong, Jing-Yin; Ye, Qing; Hua, Nan; Wong, Michael; Zeng, Ling-Hui

2012-01-01

Summary Purpose Accumulating data have demonstrated that seizures induced by kainate (KA) or pilocarpine activate the mammalian target of rapamycin (mTOR) pathway and mTOR inhibitor rapamycin can inhibit mTOR activation which subsequently has potential anti-epileptic effects. However, a preliminary study showed a paradoxical exacerbation of increased mTOR pathway activity reflected by S6 phosphorylation when rapamycin was administrated within a short period before KA injection. In the present study, we examined this paradoxical effect of rapamycin in more detail, both in normal rats and KA-injected animals. Methods Normal Rats or KA-treated rats pretreated with rapamycin at different time interval were sacrificed at various time points (1h, 3h, 6h, 10h, 15h and 24h) after rapamycin administration or seizure onset for Western blotting analysis. Phosphorylation of mTOR signaling target of Akt, mTOR, Rictor, Raptor, S6K and S6 were analyzed. Seizure activity was monitored behaviorally and graded according to a modified Racine scale (n=6 for each time point). Neuronal cell death was detected by Fluoro-Jade B staining. Key findings In normal rats, we found that rapamycin showed the expected dose-dependent inhibition of S6 phosphorylation 3–24 h after injection, while a paradoxical elevation of S6 phosphorylation was observed 1 hour after rapamycin. Similarly, pretreatment with rapamycin over 10 h prior to KA inhibited the KA seizure induced mTOR activation. In contrast, rapamycin administered 1 to 6 hours before KA caused a paradoxical increase in the KA seizure-induced mTOR activation. Rats pretreated with rapamycin 1 h prior to KA exhibited an increase in severity and duration of seizures and more neuronal cell death as compared to vehicle treated groups. In contrast, rapamycin pretreated 10 h prior to KA had no effect on the seizures and decreased neuronal cell death. The paradoxical effect of rapamycin on S6 phosphorylation was correlated with upstream mTOR signaling and was reversed by pre-treatment of perifosine, an Akt inhibitor. Significance These data indicate the complexity of S6 regulation and its effect on epilepsy. Paradoxical effects of rapamycin need to be considered in clinical applications, such as for potential treatment for epilepsy and other neurological disorders. PMID:23145776

High-Frequency Oscillations Recorded on the Scalp of Patients With Epilepsy Using Tripolar Concentric Ring Electrodes

PubMed Central

Martínez-Juárez, Iris E.; Makeyev, Oleksandr; Gaitanis, John N.; Blum, Andrew S.; Fisher, Robert S.; Medvedev, Andrei V.

2014-01-01

Epilepsy is the second most prevalent neurological disorder (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}\\(\\sim 1\\) \\end{document}% prevalence) affecting \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}\\(\\sim 67\\) \\end{document} million people worldwide with up to 75% from developing countries. The conventional electroencephalogram is plagued with artifacts from movements, muscles, and other sources. Tripolar concentric ring electrodes automatically attenuate muscle artifacts and provide improved signal quality. We performed basic experiments in healthy humans to show that tripolar concentric ring electrodes can indeed record the physiological alpha waves while eyes are closed. We then conducted concurrent recordings with conventional disc electrodes and tripolar concentric ring electrodes from patients with epilepsy. We found that we could detect high frequency oscillations, a marker for early seizure development and epileptogenic zone, on the scalp surface that appeared to become more narrow-band just prior to seizures. High frequency oscillations preceding seizures were present in an average of 35.5% of tripolar concentric ring electrode data channels for all the patients with epilepsy whose seizures were recorded and absent in the corresponding conventional disc electrode data. An average of 78.2% of channels that contained high frequency oscillations were within the seizure onset or irritative zones determined independently by three epileptologists based on conventional disc electrode data and videos. PMID:27170874

Interictal EEG spikes identify the region of electrographic seizure onset in some, but not all, pediatric epilepsy patients.

PubMed

Marsh, Eric D; Peltzer, Bradley; Brown, Merritt W; Wusthoff, Courtney; Storm, Phillip B; Litt, Brian; Porter, Brenda E

2010-04-01

The role of sharps and spikes, interictal epileptiform discharges (IEDs), in guiding epilepsy surgery in children remains controversial, particularly with intracranial electroencephalography (IEEG). Although ictal recording is the mainstay of localizing epileptic networks for surgical resection, current practice dictates removing regions generating frequent IEDs if they are near the ictal onset zone. Indeed, past studies suggest an inconsistent relationship between IED and seizure-onset location, although these studies were based upon relatively short EEG epochs. We employ a previously validated, computerized spike detector to measure and localize IED activity over prolonged, representative segments of IEEG recorded from 19 children with intractable, mostly extratemporal lobe epilepsy. Approximately 8 h of IEEG, randomly selected 30-min segments of continuous interictal IEEG per patient, were analyzed over all intracranial electrode contacts. When spike frequency was averaged over the 16-time segments, electrodes with the highest mean spike frequency were found to be within the seizure-onset region in 11 of 19 patients. There was significant variability between individual 30-min segments in these patients, indicating that large statistical samples of interictal activity were required for improved localization. Low-voltage fast EEG at seizure onset was the only clinical factor predicting IED localization to the seizure-onset region. Our data suggest that automated IED detection over multiple representative samples of IEEG may be of utility in planning epilepsy surgery for children with intractable epilepsy. Further research is required to better determine which patients may benefit from this technique a priori.

Interictal EEG spikes identify the region of seizure onset in some, but not all pediatric epilepsy patients

PubMed Central

Marsh, Eric D.; Peltzer, Bradley; Brown, Merritt W.; Wusthoff, Courtney; Storm, Phillip B.; Litt, Brian; Porter, Brenda E.

2010-01-01

Purpose The role of sharps and spikes, interictal epileptiform discharges (IEDs), in guiding epilepsy surgery in children remains controversial, particularly with intracranial EEG (IEEG). While ictal recording is the mainstay of localizing epileptic networks for surgical resection, current practice dictates removing regions generating frequent IEDs if they are near the ictal onset zone. Indeed, past studies suggest an inconsistent relationship between IED and seizure onset location, though these studies were based upon relatively short EEG epochs. Methods We employ a previously validated, computerized spike detector, to measure and localize IED activity over prolonged, representative segments of IEEG recorded from 19 children with intractable, mostly extra temporal lobe epilepsy. Approximately 8 hours of IEEG, randomly selected thirty-minute segments of continuous interictal IEEG per patient were analyzed over all intracranial electrode contacts. Results When spike frequency was averaged over the 16-time segments, electrodes with the highest mean spike frequency were found to be within the seizure onset region in 11 of 19 patients. There was significant variability between individual 30-minute segments in these patients, indicating that large statistical samples of interictal activity were required for improved localization. Low voltage fast EEG at seizure onset was the only clinical factor predicting IED localization to the seizure onset region. Conclusions Our data suggest that automated IED detection over multiple representative samples of IEEG may be of utility in planning epilepsy surgery for children with intractable epilepsy. Further research is required to better determine which patients may benefit from this technique a priori. PMID:19780794

Utility of an immunotherapy trial in evaluating patients with presumed autoimmune epilepsy

PubMed Central

Toledano, M.; Britton, J.W.; McKeon, A.; Shin, C.; Lennon, V.A.; Quek, A.M.L.; So, E.; Worrell, G.A.; Cascino, G.D.; Klein, C.J.; Lagerlund, T.D.; Wirrell, E.C.; Nickels, K.C.

2014-01-01

Objective: To evaluate a trial of immunotherapy as an aid to diagnosis in suspected autoimmune epilepsy. Method: We reviewed the charts of 110 patients seen at our autoimmune neurology clinic with seizures as a chief complaint. Twenty-nine patients met the following inclusion criteria: (1) autoimmune epilepsy suspected based on the presence of ≥1 neural autoantibody (n = 23), personal or family history or physical stigmata of autoimmunity, and frequent or medically intractable seizures; and (2) initiated a 6- to 12-week trial of IV methylprednisolone (IVMP), IV immune globulin (IVIg), or both. Patients were defined as responders if there was a 50% or greater reduction in seizure frequency. Results: Eighteen patients (62%) responded, of whom 10 (34%) became seizure-free; 52% improved with the first agent. Of those receiving a second agent after not responding to the first, 43% improved. A favorable response correlated with shorter interval between symptom onset and treatment initiation (median 9.5 vs 22 months; p = 0.048). Responders included 14/16 (87.5%) patients with antibodies to plasma membrane antigens, 2/6 (33%) patients seropositive for glutamic acid decarboxylase 65 antibodies, and 2/6 (33%) patients without detectable antibodies. Of 13 responders followed for more than 6 months after initiating long-term oral immunosuppression, response was sustained in 11 (85%). Conclusions: These retrospective findings justify consideration of a trial of immunotherapy in patients with suspected autoimmune epilepsy. Classification of evidence: This study provides Class IV evidence that in patients with suspected autoimmune epilepsy, IVMP, IVIg, or both improve seizure control. PMID:24706013

Seizure semiology identifies patients with bilateral temporal lobe epilepsy.

PubMed

Loesch, Anna Mira; Feddersen, Berend; Tezer, F Irsel; Hartl, Elisabeth; Rémi, Jan; Vollmar, Christian; Noachtar, Soheyl

2015-01-01

Laterality in temporal lobe epilepsy is usually defined by EEG and imaging results. We investigated whether the analysis of seizure semiology including lateralizing seizure phenomena identifies bilateral independent temporal lobe seizure onset. We investigated the seizure semiology in 17 patients in whom invasive EEG-video-monitoring documented bilateral temporal seizure onset. The results were compared to 20 left and 20 right consecutive temporal lobe epilepsy (TLE) patients who were seizure free after anterior temporal lobe resection. The seizure semiology was analyzed using the semiological seizure classification with particular emphasis on the sequence of seizure phenomena over time and lateralizing seizure phenomena. Statistical analysis included chi-square test or Fisher's exact test. Bitemporal lobe epilepsy patients had more frequently different seizure semiology (100% vs. 40%; p<0.001) and significantly more often lateralizing seizure phenomena pointing to bilateral seizure onset compared to patients with unilateral TLE (67% vs. 11%; p<0.001). The sensitivity of identical vs. different seizure semiology for the identification of bilateral TLE was high (100%) with a specificity of 60%. Lateralizing seizure phenomena had a low sensitivity (59%) but a high specificity (89%). The combination of lateralizing seizure phenomena and different seizure semiology showed a high specificity (94%) but a low sensitivity (59%). The analysis of seizure semiology including lateralizing seizure phenomena adds important clinical information to identify patients with bilateral TLE. Copyright © 2014 Elsevier B.V. All rights reserved.

Linear feature detection algorithm for astronomical surveys - I. Algorithm description

NASA Astrophysics Data System (ADS)

Bektešević, Dino; Vinković, Dejan

2017-11-01

Computer vision algorithms are powerful tools in astronomical image analyses, especially when automation of object detection and extraction is required. Modern object detection algorithms in astronomy are oriented towards detection of stars and galaxies, ignoring completely the detection of existing linear features. With the emergence of wide-field sky surveys, linear features attract scientific interest as possible trails of fast flybys of near-Earth asteroids and meteors. In this work, we describe a new linear feature detection algorithm designed specifically for implementation in big data astronomy. The algorithm combines a series of algorithmic steps that first remove other objects (stars and galaxies) from the image and then enhance the line to enable more efficient line detection with the Hough algorithm. The rate of false positives is greatly reduced thanks to a step that replaces possible line segments with rectangles and then compares lines fitted to the rectangles with the lines obtained directly from the image. The speed of the algorithm and its applicability in astronomical surveys are also discussed.

Risk of seizure recurrence after achieving initial seizure freedom on the ketogenic diet.

PubMed

Taub, Katherine S; Kessler, Sudha Kilaru; Bergqvist, A G Christina

2014-04-01

Few studies have examined the long-term sustainability of complete seizure freedom on the ketogenic diet (KD). The purpose of this study was to describe the risk of seizure recurrence in children who achieved at least 1 month of seizure freedom on the KD, and to assess clinical features associated with sustained seizure freedom. Records of patients initiated on the KD at The Children's Hospital of Philadelphia (CHOP) from 1991 to 2009 were reviewed. Subjects who attained seizure freedom for at least 1 month within 2 years were included in the study. Seizure frequency was recorded based on caregiver-reported seizure diaries as unchanged, improved, or worse compared to baseline. Those patients with seizure freedom ≥1 year were compared to those with seizure freedom <1 year in terms of demographics, age of seizure onset, number of antiepileptic drugs (AEDs) prior to KD, and epilepsy classification. Of 276 patients initiated on the KD, 65 patients (24%) attained seizure freedom for a minimum of 1 month. The majority of these patients had daily seizures. The median time to seizure freedom after KD initiation was 1.5 months. Seizures recurred in 53 patients (82%), with a median time to seizure recurrence of 3 months. However, seizure frequency after initial recurrence remained far less than baseline. No clinical features were identified as risk factors for seizure recurrence. Seizure recurrence on the KD after 1 month of seizure freedom most often occurred as occasional breakthrough seizures and not a return to baseline seizure frequency. This study provides evidence to support the continued use of the KD in patients with initial seizure freedom even after breakthrough seizures. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

Neurobehavioral Deficits in a Rat Model of Recurrent Neonatal Seizures Are Prevented by a Ketogenic Diet and Correlate with Hippocampal Zinc/Lipid Transporter Signals.

PubMed

Tian, Tian; Ni, Hong; Sun, Bao-liang

2015-10-01

The ketogenic diet (KD) has been shown to be effective as an antiepileptic therapy in adults, but it has not been extensively tested for its efficacy in neonatal seizure-induced brain damage. We have previously shown altered expression of zinc/lipid metabolism-related genes in hippocampus following penicillin-induced developmental model of epilepsy. In this study, we further investigated the effect of KD on the neurobehavioral and cognitive deficits, as well as if KD has any influence in the activity of zinc/lipid transporters such as zinc transporter 3 (ZnT-3), MT-3, ApoE, ApoJ (clusterin), and ACAT-1 activities in neonatal rats submitted to flurothyl-induced recurrent seizures. Postnatal day 9 (P9), 48 Sprague-Dawley rats were randomly assigned to two groups: flurothyl-induced recurrent seizure group (EXP) and control group (CONT). On P28, they were further randomly divided into the seizure group without ketogenic diet (EXP1), seizure plus ketogenic diet (EXP2), the control group without ketogenic diet (CONT1), and the control plus ketogenic diet (CONT2). Neurological behavioral parameters of brain damage (plane righting reflex, cliff avoidance reflex, and open field test) were observed from P35 to P49. Morris water maze test was performed during P51-P57. Then hippocampal mossy fiber sprouting and the protein levels of ZnT3, MT3, ApoE, CLU, and ACAT-1 were detected by Timm staining and Western blot analysis, respectively. Flurothyl-induced neurobehavioral toxicology and aberrant mossy fiber sprouting were blocked by KD. In parallel with these behavioral changes, rats treated with KD (EXP2) showed a significant down-regulated expression of ZnT-3, MT-3, ApoE, clusterin, and ACAT-1 in hippocampus when compared with the non-KD-treated EXP1 group. Our findings provide support for zinc/lipid transporter signals being potential targets for the treatment of neonatal seizure-induced brain damage by KD.

Morphometric MRI features are associated with surgical outcome in mesial temporal lobe epilepsy with hippocampal sclerosis.

PubMed

Garcia, Maria Teresa Fernandes Castilho; Gaça, Larissa Botelho; Sandim, Gabriel Barbosa; Assunção Leme, Idaiane Batista; Carrete, Henrique; Centeno, Ricardo Silva; Sato, João Ricardo; Yacubian, Elza Márcia Targas

2017-05-01

Corticoamygdalohippocampectomy (CAH) improves seizure control, quality of life, and decreases mortality for refractory mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS). One-third of patients continue having seizures, and it is pivotal to determine structural abnormalities that might influence the postoperative outcome. Studies indicate that nonhippocampal regions may play a role in the epileptogenic network in MTLE-HS and could generate seizures postoperatively. The aim of this study is to analyze areas of atrophy, not always detected on routine MRI, comparing patients who became seizure free (SF) with those non seizure free (NSF) after CAH, in an attempt to establish possible predictors of surgical outcome. 105 patients with refractory MTLE-HS submitted to CAH (59 left MTLE; 46 males) and 47 controls were enrolled. FreeSurfer was performed for cortical thickness and volume estimation comparing patients to controls and SF versus NSF patients. The final sample after post processing procedures resulted in 99 patients. Cortical thickness analyses showed reductions in left insula in NSF patients compared to those SF. Significant volume reductions in SF patients were present in bilateral thalami, hippocampi and pars opercularis, left parahippocampal gyrus and right temporal pole. In NSF patients reductions were present bilaterally in thalami, hippocampi, entorhinal cortices, superior frontal and supramarginal gyri; on the left: superior and middle temporal gyri, temporal pole, parahippocampal gyrus, pars opercularis and middle frontal gyrus; and on the right: precentral, superior, middle and inferior temporal gyri. Comparison between SF and NSF patients showed ipsilateral gray matter reductions in the right entorhinal cortex (p=0.003) and contralateral parahippocampal gyrus (p=0.05) in right MTLE-HS. Patients NSF had a longer duration of epilepsy than those SF (p=0.028). NSF patients exhibited more extensive areas of atrophy than SF ones. As entorhinal cortex and parahippocampal gyrus are reduced in NSF patients compared to those SF these structures might be implicated in the network responsible for the maintenance of postoperative seizures. Duration of epilepsy is a predictor of seizure outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

An efficient parallel termination detection algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, A. H.; Crivelli, S.; Jessup, E. R.

2004-05-27

Information local to any one processor is insufficient to monitor the overall progress of most distributed computations. Typically, a second distributed computation for detecting termination of the main computation is necessary. In order to be a useful computational tool, the termination detection routine must operate concurrently with the main computation, adding minimal overhead, and it must promptly and correctly detect termination when it occurs. In this paper, we present a new algorithm for detecting the termination of a parallel computation on distributed-memory MIMD computers that satisfies all of those criteria. A variety of termination detection algorithms have been devised. Ofmore » these, the algorithm presented by Sinha, Kale, and Ramkumar (henceforth, the SKR algorithm) is unique in its ability to adapt to the load conditions of the system on which it runs, thereby minimizing the impact of termination detection on performance. Because their algorithm also detects termination quickly, we consider it to be the most efficient practical algorithm presently available. The termination detection algorithm presented here was developed for use in the PMESC programming library for distributed-memory MIMD computers. Like the SKR algorithm, our algorithm adapts to system loads and imposes little overhead. Also like the SKR algorithm, ours is tree-based, and it does not depend on any assumptions about the physical interconnection topology of the processors or the specifics of the distributed computation. In addition, our algorithm is easier to implement and requires only half as many tree traverses as does the SKR algorithm. This paper is organized as follows. In section 2, we define our computational model. In section 3, we review the SKR algorithm. We introduce our new algorithm in section 4, and prove its correctness in section 5. We discuss its efficiency and present experimental results in section 6.« less

Dynamic, cell type-specific roles for GABAergic interneurons in a mouse model of optogenetically inducible seizures

PubMed Central

Khoshkhoo, Sattar; Vogt, Daniel; Sohal, Vikaas S.

2016-01-01

SUMMARY GABAergic interneurons play critical roles in seizures, but it remains unknown whether these vary across interneuron subtypes or evolve during a seizure. This uncertainty stems from the unpredictable timing of seizures in most models, which limits neuronal imaging or manipulations around the seizure onset. Here, we describe a mouse model for optogenetic seizure induction. Combining this with calcium imaging, we find that seizure onset rapidly recruits parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptitde (VIP)-expressing interneurons, whereas excitatory neurons are recruited several seconds later. Optogenetically inhibiting VIP interneurons consistently increased seizure threshold and reduced seizure duration. Inhibiting PV+ and SOM+ interneurons had mixed effects on seizure initiation, but consistently reduced seizure duration. Thus, while their roles may evolve during seizures, PV+ and SOM+ interneurons ultimately help maintain ongoing seizures. These results show how an optogenetically-induced seizure model can be leveraged to pinpoint a new target for seizure control: VIP interneurons. PMID:28041880

Relationship of number of seizures recorded on video-EEG to surgical outcome in refractory medial temporal lobe epilepsy

PubMed Central

Sainju, Rup Kamal; Wolf, Bethany Jacobs; Bonilha, Leonardo; Martz, Gabriel

2014-01-01

Introduction Surgical planning for refractory medial temporal lobe epilepsy (rMTLE) relies on seizure localization by ictal electroencephalography (EEG). Multiple factors impact the number of seizures recorded. We evaluated whether seizure freedom correlated to the number of seizures recorded, and the related factors. Methods We collected data for 32 patients with rMTLE who underwent anterior temporal lobectomy. Primary analysis evaluated number of seizures captured as a predictor of surgical outcome. Subsequent analyses explored factors that may seizure number. Results Number of seizures recorded did not predict seizure freedom. More seizures were recorded with more days of seizure occurrence (p<0.001), seizure clusters (p≤0.011) and poorly localized seizures (PLSz) (p=0.004). Regression modeling showed a trend for subjects with fewer recorded poorly localized seizures to have better surgical outcome (p=0.052). Conclusions Total number of recorded seizures does not predict surgical outcome. Patients with more PLSz may have worse outcome. PMID:22990726

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

PubMed

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

Leaving tissue associated with infrequent intracranial EEG seizure onsets is compatible with post-operative seizure freedom

PubMed Central

Huang, Cyrus; Marsh, Eric D.; Ziskind, Daniela M.; Celix, Juanita M.; Peltzer, Bradley; Brown, Merritt W.; Storm, Phillip B.; Litt, Brian; Porter, Brenda E.

2013-01-01

Identify seizure onset electrodes that need to be resected for seizure freedom in children undergoing intracranial electroencephalography recording for treatment of medically refractory epilepsy. All children undergoing intracranial electroencephalography subdural grid electrode placement at the Children’s Hospital of Philadelphia from 2002-2008 were asked to enroll. We utilized intraoperative pictures to determine the location of the electrodes and define the resection cavity. A total of 15 patients had surgical fields that allowed for complete identification of the electrodes over the area of resection. Eight of 15 patients were seizure free after a follow up of 1.7 to 8 yr. Only one seizure-free patient had complete resection of all seizure onset associated tissue. Seizure free patients had resection of 64.1% of the seizure onset electrode associated tissue, compared to 35.2% in the not seizure free patients (p=0.05). Resection of tissue associated with infrequent seizure onsets did not appear to be important for seizure freedom. Resecting ≥ 90% of the electrodes from the predominant seizure contacts predicted post-operative seizure freedom (p=0.007). The best predictor of seizure freedom was resecting ≥ 90% of tissue involved in majority of a patient’s seizures. Resection of tissue under infrequent seizure onset electrodes was not necessary for seizure freedom. PMID:24563805

The temporal relation between seizure onset and arousal-awakening in temporal lobe seizures.

PubMed

Gumusyayla, Sadiye; Erdal, Abidin; Tezer, F Irsel; Saygi, Serap

2016-07-01

Our main aim was to determine the time interval between the seizure onsets and arousal-awakening related to these seizures in patients with temporal lobe epilepsy (TLE) and to discuss the role of lateralization on arousal-awakening mechanisms. Thirty-three TLE patients who underwent video-EEG monitoring with simultaneous polysomnography (PSG) and had recorded nocturnal seizures were retrospectively examined. These TLE patients had 64 seizures during sleep. The onsets of seizures and arousal-awakening related to these seizures were marked according to clinical and electrophysiological features. The time interval between the seizure onset and arousal-awakening related to the seizure was compared in patients with right- or left-sided temporal lobe seizures. In our TLE patients nocturnal seizures mostly followed arousal-awakening (64%). The time interval between the seizure onset and arousal-awakening related to the seizure was significantly shorter in patients with left-sided temporal lobe seizures (p=0.01). Video-EEG monitoring and PSG with scalp electrodes in our TLE patients showed that nocturnal seizures mostly followed arousal-awakening, and it was more pronounced in those with left-sided seizures. Arousal-awakening might be a signal for subsequent seizures in patients with TLE. Copyright © 2016 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Radar Detection of Marine Mammals

DTIC Science & Technology

2011-09-30

BFT-BPT algorithm for use with our radar data. This track - before - detect algorithm had been effective in enhancing small but persistent signatures in...will be possible with the detect before track algorithm. 4 We next evaluated the track before detect algorithm, the BFT-BPT, on the CEDAR data

Spatiotemporal differences in the c-fos pathway between C57BL/6J and DBA/2J mice following flurothyl-induced seizures: a dissociation of hippocampal Fos from seizure activity

PubMed Central

Kadiyala, Sridhar B.; Papandrea, Dominick; Tuz, Karina; Anderson, Tara M.; Jayakumar, Sachidhanand; Herron, Bruce J.; Ferland, Russell J.

2014-01-01

Significant differences in seizure characteristics between inbred mouse strains highlight the importance of genetic predisposition to epilepsy. Here, we examined the genetic differences between the seizure-resistant C57BL/6J (B6) mouse strain and the seizure-susceptible DBA/2J (D2) strain in the phospho-Erk and Fos pathways to examine seizure-induced neuronal activity to uncover potential mechanistic correlates to these disparate seizure responsivities. Expression of neural activity markers was examined following 1, 5, or 8 seizures, or after 8 seizures, a 28 day rest period, and a final flurothyl rechallenge. Two brain regions, the hippocampus and ventromedial nucleus of the hypothalamus (VMH), had significantly different Fos expression profiles following seizures. Fos expression was highly robust in B6 hippocampus following one seizure and remained elevated following multiple seizures. Conversely, there was an absence of Fos (and phospho-Erk) expression in D2 hippocampus following one generalized seizure that increased with multiple seizures. This lack of Fos expression occurred despite intracranial electroencephalographic recordings indicating that the D2 hippocampus propagated ictal discharge during the first flurothyl seizure suggesting a dissociation of seizure discharge from Fos and phospho-Erk expression. Global transcriptional analysis confirmed a dysregulation of the c-fos pathway in D2 mice following 1 seizure. Moreover, global analysis of RNA expression differences between B6 and D2 hippocampus revealed a unique pattern of transcripts that were co-regulated with Fos in D2 hippocampus following 1 seizure. These expression differences could, in part, account for D2’s seizure susceptibility phenotype. Following 8 seizures, a 28 day rest period, and a final flurothyl rechallenge, ~85% of B6 mice develop a more complex seizure phenotype consisting of a clonic-forebrain seizure that uninterruptedly progresses into a brainstem seizure. This seizure phenotype in B6 mice is highly correlated with bilateral Fos expression in the VMH and was not observed in D2 mice, which always express clonic-forebrain seizures upon flurothyl retest. Overall, these results illustrate specific differences in protein and RNA expression in different inbred strains following seizures that precede the reorganizational events that affect seizure susceptibility and changes in seizure semiology over time. PMID:25524858

Spatiotemporal differences in the c-fos pathway between C57BL/6J and DBA/2J mice following flurothyl-induced seizures: A dissociation of hippocampal Fos from seizure activity.

PubMed

Kadiyala, Sridhar B; Papandrea, Dominick; Tuz, Karina; Anderson, Tara M; Jayakumar, Sachidhanand; Herron, Bruce J; Ferland, Russell J

2015-01-01

Significant differences in seizure characteristics between inbred mouse strains highlight the importance of genetic predisposition to epilepsy. Here, we examined the genetic differences between the seizure-resistant C57BL/6J (B6) mouse strain and the seizure-susceptible DBA/2J (D2) strain in the phospho-Erk and Fos pathways to examine seizure-induced neuronal activity to uncover potential mechanistic correlates to these disparate seizure responsivities. Expression of neural activity markers was examined following 1, 5, or 8 seizures, or after 8 seizures, a 28 day rest period, and a final flurothyl rechallenge. Two brain regions, the hippocampus and ventromedial nucleus of the hypothalamus (VMH), had significantly different Fos expression profiles following seizures. Fos expression was highly robust in B6 hippocampus following one seizure and remained elevated following multiple seizures. Conversely, there was an absence of Fos (and phospho-Erk) expression in D2 hippocampus following one generalized seizure that increased with multiple seizures. This lack of Fos expression occurred despite intracranial electroencephalographic recordings indicating that the D2 hippocampus propagated ictal discharge during the first flurothyl seizure suggesting a dissociation of seizure discharge from Fos and phospho-Erk expression. Global transcriptional analysis confirmed a dysregulation of the c-fos pathway in D2 mice following 1 seizure. Moreover, global analysis of RNA expression differences between B6 and D2 hippocampus revealed a unique pattern of transcripts that were co-regulated with Fos in D2 hippocampus following 1 seizure. These expression differences could, in part, account for D2's seizure susceptibility phenotype. Following 8 seizures, a 28 day rest period, and a final flurothyl rechallenge, ∼85% of B6 mice develop a more complex seizure phenotype consisting of a clonic-forebrain seizure that uninterruptedly progresses into a brainstem seizure. This seizure phenotype in B6 mice is highly correlated with bilateral Fos expression in the VMH and was not observed in D2 mice, which always express clonic-forebrain seizures upon flurothyl retest. Overall, these results illustrate specific differences in protein and RNA expression in different inbred strains following seizures that precede the reorganizational events that affect seizure susceptibility and changes in seizure semiology over time. Copyright © 2014 Elsevier B.V. All rights reserved.

Eight Flurothyl-Induced Generalized Seizures Lead to the Rapid Evolution of Spontaneous Seizures in Mice: A Model of Epileptogenesis with Seizure Remission

PubMed Central

Kadiyala, Sridhar B.; Yannix, Joshua Q.; Nalwalk, Julia W.; Papandrea, Dominick; Beyer, Barbara S.; Herron, Bruce J.

2016-01-01

The occurrence of recurrent, unprovoked seizures is the hallmark of human epilepsy. Currently, only two-thirds of this patient population has adequate seizure control. New epilepsy models provide the potential for not only understanding the development of spontaneous seizures, but also for testing new strategies to treat this disorder. Here, we characterize a primary generalized seizure model of epilepsy following repeated exposure to the GABAA receptor antagonist, flurothyl, in which mice develop spontaneous seizures that remit within 1 month. In this model, we expose C57BL/6J mice to flurothyl until they experience a generalized seizure. Each of these generalized seizures typically lasts <30 s. We induce one seizure per day for 8 d followed by 24 h video-electroencephalographic recordings. Within 1 d following the last of eight flurothyl-induced seizures, ∼50% of mice have spontaneous seizures. Ninety-five percent of mice tested have seizures within the first week of the recording period. Of the spontaneous seizures recorded, the majority are generalized clonic seizures, with the remaining 7–12% comprising generalized clonic seizures that transition into brainstem seizures. Over the course of an 8 week recording period, spontaneous seizure episodes remit after ∼4 weeks. Overall, the repeated flurothyl paradigm is a model of epileptogenesis with spontaneous seizures that remit. This model provides an additional tool in our armamentarium for understanding the mechanisms underlying epileptogenesis and may provide insights into why spontaneous seizures remit without anticonvulsant treatment. Elucidating these processes could lead to the development of new epilepsy therapeutics. SIGNIFICANCE STATEMENT Epilepsy is a chronic disorder characterized by the occurrence of recurrent, unprovoked seizures in which the individual seizure–ictal events are self-limiting. Remission of recurrent, unprovoked seizures can be achieved in two-thirds of cases by treatment with anticonvulsant medication, surgical resection, and/or nerve/brain electrode stimulation. However, there are examples in humans of epilepsy with recurrent, unprovoked seizures remitting without any intervention. While elucidating how recurrent, unprovoked seizures develop is critical for understanding epileptogenesis, an understanding of how and why recurrent, unprovoked seizures remit may further our understanding and treatment of epilepsy. Here, we describe a new model of recurrent, unprovoked spontaneous seizures in which the occurrence of spontaneous seizures naturally remits over time without any therapeutic intervention. PMID:27413158

Performances of the New Real Time Tsunami Detection Algorithm applied to tide gauges data

NASA Astrophysics Data System (ADS)

Chierici, F.; Embriaco, D.; Morucci, S.

2017-12-01

Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection (TDA) based on the real-time tide removal and real-time band-pass filtering of seabed pressure time series acquired by Bottom Pressure Recorders. The TDA algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability with respect to the most widely used tsunami detection algorithm, while containing the computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. In this work we present the performance of the TDA algorithm applied to tide gauge data. We have adapted the new tsunami detection algorithm and the Monte Carlo test methodology to tide gauges. Sea level data acquired by coastal tide gauges in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event generated by Tohoku earthquake on March 11th 2011, using data recorded by several tide gauges scattered all over the Pacific area.

The amygdala and temporal lobe simple partial seizures: a prospective and quantitative MRI study.

PubMed

Van Paesschen, W; King, M D; Duncan, J S; Connelly, A

2001-07-01

To determine whether specific temporal lobe simple partial seizures (SPSs) are associated with an abnormal amygdala T2 (AT2) ipsilateral to the seizure focus in patients with intractable unilateral temporal lobe epilepsy (TLE). AT2 relaxation time mapping is a sensitive method for the detection of abnormal tissue in the amygdala in patients with refractory TLE. The relation between an abnormal AT2 in the epileptic temporal lobe and amygdala seizure onset has not been established. Fifty patients with intractable unilateral TLE and concordant data during presurgical evaluation were included. Patients with a foreign-tissue lesion on standard magnetic resonance imaging (MRI) were excluded. All had AT2 mapping. Fifteen types of SPSs were ascertained prospectively, systematically, and blinded to the results of AT2 mapping. The SPSs of patients with a normal AT2 (n = 25) were compared with those of patients with an abnormal AT2 ipsilateral to the seizure focus (n = 25). The group of patients with an abnormal AT2 reported a median of six types of SPSs (range 1-11), in comparison with a median of three types of SPSs (range, 0-7) for the group with a normal AT2 (p<0.01). Déjà vu, a warm sensation, an indescribable strange sensation, a cephalic sensation, and fear were associated with an abnormal AT2. The combination of déjà vu, a cephalic sensation, a warm sensation, a gustatory hallucination, and an indescribable strange sensation discriminated best between the 25 patients with a normal and the 25 patients with an abnormal AT2. A high number and the types of different SPSs provide clinical evidence for early involvement of the amygdala during seizures in patients with refractory unilateral TLE and an abnormal AT2 in the epileptic temporal lobe

Predictive models in the diagnosis and treatment of autoimmune epilepsy.

PubMed

Dubey, Divyanshu; Singh, Jaysingh; Britton, Jeffrey W; Pittock, Sean J; Flanagan, Eoin P; Lennon, Vanda A; Tillema, Jan-Mendelt; Wirrell, Elaine; Shin, Cheolsu; So, Elson; Cascino, Gregory D; Wingerchuk, Dean M; Hoerth, Matthew T; Shih, Jerry J; Nickels, Katherine C; McKeon, Andrew

2017-07-01

To validate predictive models for neural antibody positivity and immunotherapy response in epilepsy. We conducted a retrospective study of epilepsy cases at Mayo Clinic (Rochester-MN; Scottsdale-AZ, and Jacksonville-FL) in whom autoimmune encephalopathy/epilepsy/dementia autoantibody testing profiles were requested (06/30/2014-06/30/2016). An Antibody Prevalence in Epilepsy (APE) score, based on clinical characteristics, was assigned to each patient. Among patients who received immunotherapy, a Response to Immunotherapy in Epilepsy (RITE) score was assigned. Favorable seizure outcome was defined as >50% reduction of seizure frequency at the first follow-up. Serum and cerebrospinal fluid (CSF) from 1,736 patients were sent to the Mayo Clinic Neuroimmunology Laboratory for neural autoantibody evaluation. Three hundred eighty-seven of these patients met the diagnostic criteria for epilepsy. Central nervous system (CNS)-specific antibodies were detected in 44 patients. Certain clinical features such as new-onset epilepsy, autonomic dysfunction, viral prodrome, faciobrachial dystonic seizures/oral dyskinesia, inflammatory CSF profile, and mesial temporal magnetic resonance imaging (MRI) abnormalities had a significant association with positive antibody results. A significantly higher proportion of antibody-positive patients had an APE score ≥4 (97.7% vs. 21.6%, p < 0.01). Sensitivity and specificity of an APE score ≥4 to predict presence of specific neural auto-antibody were 97.7% and 77.9%, respectively. In the subset of patients who received immunotherapy (77), autonomic dysfunction, faciobrachial dystonic seizures/oral dyskinesia, early initiation of immunotherapy, and presence of antibodies targeting plasma membrane proteins (cell-surface antigens) were associated with favorable seizure outcome. Sensitivity and specificity of a RITE score ≥7 to predict favorable seizure outcome were 87.5% and 83.8%, respectively. APE and RITE scores can aid diagnosis, treatment, and prognostication of autoimmune epilepsy. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

Epileptogenic zone localization using magnetoencephalography predicts seizure freedom in epilepsy surgery

PubMed Central

Englot, Dario J.; Nagarajan, Srikantan S.; Imber, Brandon S.; Raygor, Kunal P.; Honma, Susanne M.; Mizuiri, Danielle; Mantle, Mary; Knowlton, Robert C.; Kirsch, Heidi E.; Chang, Edward F.

2015-01-01

Objective The efficacy of epilepsy surgery depends critically upon successful localization of the epileptogenic zone. Magnetoencephalography (MEG) enables non-invasive detection of interictal spike activity in epilepsy, which can then be localized in three dimensions using magnetic source imaging (MSI) techniques. However, the clinical value of MEG in the pre-surgical epilepsy evaluation is not fully understood, as studies to date are limited by either a lack of long-term seizure outcomes or small sample size. Methods We performed a retrospective cohort study of focal epilepsy patients who received MEG for interictal spike mapping followed by surgical resection at our institution. Results We studied 132 surgical patients, with mean post-operative follow-up of 3.6 years (minimum 1 year). Dipole source modelling was successful in 103 (78%) patients, while no interictal spikes were seen in others. Among patients with successful dipole modelling, MEG findings were concordant with and specific to: i) the region of resection in 66% of patients, ii) invasive electrocorticography (ECoG) findings in 67% of individuals, and iii) the MRI abnormality in 74% of cases. MEG showed discordant lateralization in ~5% of cases. After surgery, 70% of all patients achieved seizure-freedom (Engel class I outcome). Whereas 85% of patients with concordant and specific MEG findings became seizure-free, this outcome was achieved by only 37% of individuals with MEG findings that were non-specific or discordant with the region of resection (χ2 = 26.4, p < 0.001). MEG reliability was comparable in patients with or without localized scalp EEG, and overall, localizing MEG findings predicted seizure freedom with an odds ratio of 5.11 (2.23–11.8, 95% CI). Significance MEG is a valuable tool for non-invasive interictal spike mapping in epilepsy surgery, including patients with non-localized findings on long-term EEG monitoring, and localization of the epileptogenic zone using MEG is associated with improved seizure outcomes. PMID:25921215

Data mining neocortical high-frequency oscillations in epilepsy and controls

PubMed Central

Stead, Matt; Krieger, Abba; Stacey, William; Maus, Douglas; Marsh, Eric; Viventi, Jonathan; Lee, Kendall H.; Marsh, Richard; Litt, Brian; Worrell, Gregory A.

2011-01-01

Transient high-frequency (100–500 Hz) oscillations of the local field potential have been studied extensively in human mesial temporal lobe. Previous studies report that both ripple (100–250 Hz) and fast ripple (250–500 Hz) oscillations are increased in the seizure-onset zone of patients with mesial temporal lobe epilepsy. Comparatively little is known, however, about their spatial distribution with respect to seizure-onset zone in neocortical epilepsy, or their prevalence in normal brain. We present a quantitative analysis of high-frequency oscillations and their rates of occurrence in a group of nine patients with neocortical epilepsy and two control patients with no history of seizures. Oscillations were automatically detected and classified using an unsupervised approach in a data set of unprecedented volume in epilepsy research, over 12 terabytes of continuous long-term micro- and macro-electrode intracranial recordings, without human preprocessing, enabling selection-bias-free estimates of oscillation rates. There are three main results: (i) a cluster of ripple frequency oscillations with median spectral centroid = 137 Hz is increased in the seizure-onset zone more frequently than a cluster of fast ripple frequency oscillations (median spectral centroid = 305 Hz); (ii) we found no difference in the rates of high frequency oscillations in control neocortex and the non-seizure-onset zone neocortex of patients with epilepsy, despite the possibility of different underlying mechanisms of generation; and (iii) while previous studies have demonstrated that oscillations recorded by parenchyma-penetrating micro-electrodes have higher peak 100–500 Hz frequencies than penetrating macro-electrodes, this was not found for the epipial electrodes used here to record from the neocortical surface. We conclude that the relative rate of ripple frequency oscillations is a potential biomarker for epileptic neocortex, but that larger prospective studies correlating high-frequency oscillations rates with seizure-onset zone, resected tissue and surgical outcome are required to determine the true predictive value. PMID:21903727

Data mining neocortical high-frequency oscillations in epilepsy and controls.

PubMed

Blanco, Justin A; Stead, Matt; Krieger, Abba; Stacey, William; Maus, Douglas; Marsh, Eric; Viventi, Jonathan; Lee, Kendall H; Marsh, Richard; Litt, Brian; Worrell, Gregory A

2011-10-01

Transient high-frequency (100-500 Hz) oscillations of the local field potential have been studied extensively in human mesial temporal lobe. Previous studies report that both ripple (100-250 Hz) and fast ripple (250-500 Hz) oscillations are increased in the seizure-onset zone of patients with mesial temporal lobe epilepsy. Comparatively little is known, however, about their spatial distribution with respect to seizure-onset zone in neocortical epilepsy, or their prevalence in normal brain. We present a quantitative analysis of high-frequency oscillations and their rates of occurrence in a group of nine patients with neocortical epilepsy and two control patients with no history of seizures. Oscillations were automatically detected and classified using an unsupervised approach in a data set of unprecedented volume in epilepsy research, over 12 terabytes of continuous long-term micro- and macro-electrode intracranial recordings, without human preprocessing, enabling selection-bias-free estimates of oscillation rates. There are three main results: (i) a cluster of ripple frequency oscillations with median spectral centroid = 137 Hz is increased in the seizure-onset zone more frequently than a cluster of fast ripple frequency oscillations (median spectral centroid = 305 Hz); (ii) we found no difference in the rates of high frequency oscillations in control neocortex and the non-seizure-onset zone neocortex of patients with epilepsy, despite the possibility of different underlying mechanisms of generation; and (iii) while previous studies have demonstrated that oscillations recorded by parenchyma-penetrating micro-electrodes have higher peak 100-500 Hz frequencies than penetrating macro-electrodes, this was not found for the epipial electrodes used here to record from the neocortical surface. We conclude that the relative rate of ripple frequency oscillations is a potential biomarker for epileptic neocortex, but that larger prospective studies correlating high-frequency oscillations rates with seizure-onset zone, resected tissue and surgical outcome are required to determine the true predictive value.

Eight Flurothyl-Induced Generalized Seizures Lead to the Rapid Evolution of Spontaneous Seizures in Mice: A Model of Epileptogenesis with Seizure Remission.

PubMed

Kadiyala, Sridhar B; Yannix, Joshua Q; Nalwalk, Julia W; Papandrea, Dominick; Beyer, Barbara S; Herron, Bruce J; Ferland, Russell J

2016-07-13

The occurrence of recurrent, unprovoked seizures is the hallmark of human epilepsy. Currently, only two-thirds of this patient population has adequate seizure control. New epilepsy models provide the potential for not only understanding the development of spontaneous seizures, but also for testing new strategies to treat this disorder. Here, we characterize a primary generalized seizure model of epilepsy following repeated exposure to the GABAA receptor antagonist, flurothyl, in which mice develop spontaneous seizures that remit within 1 month. In this model, we expose C57BL/6J mice to flurothyl until they experience a generalized seizure. Each of these generalized seizures typically lasts <30 s. We induce one seizure per day for 8 d followed by 24 h video-electroencephalographic recordings. Within 1 d following the last of eight flurothyl-induced seizures, ∼50% of mice have spontaneous seizures. Ninety-five percent of mice tested have seizures within the first week of the recording period. Of the spontaneous seizures recorded, the majority are generalized clonic seizures, with the remaining 7-12% comprising generalized clonic seizures that transition into brainstem seizures. Over the course of an 8 week recording period, spontaneous seizure episodes remit after ∼4 weeks. Overall, the repeated flurothyl paradigm is a model of epileptogenesis with spontaneous seizures that remit. This model provides an additional tool in our armamentarium for understanding the mechanisms underlying epileptogenesis and may provide insights into why spontaneous seizures remit without anticonvulsant treatment. Elucidating these processes could lead to the development of new epilepsy therapeutics. Epilepsy is a chronic disorder characterized by the occurrence of recurrent, unprovoked seizures in which the individual seizure-ictal events are self-limiting. Remission of recurrent, unprovoked seizures can be achieved in two-thirds of cases by treatment with anticonvulsant medication, surgical resection, and/or nerve/brain electrode stimulation. However, there are examples in humans of epilepsy with recurrent, unprovoked seizures remitting without any intervention. While elucidating how recurrent, unprovoked seizures develop is critical for understanding epileptogenesis, an understanding of how and why recurrent, unprovoked seizures remit may further our understanding and treatment of epilepsy. Here, we describe a new model of recurrent, unprovoked spontaneous seizures in which the occurrence of spontaneous seizures naturally remits over time without any therapeutic intervention. Copyright © 2016 the authors 0270-6474/16/367486-12$15.00/0.

A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors

PubMed Central

Tian, Xiaochun; Chen, Jiabin; Han, Yongqiang; Shang, Jianyu; Li, Nan

2016-01-01

Zero velocity update (ZUPT) plays an important role in pedestrian navigation algorithms with the premise that the zero velocity interval (ZVI) should be detected accurately and effectively. A novel adaptive ZVI detection algorithm based on a smoothed pseudo Wigner–Ville distribution to remove multiple frequencies intelligently (SPWVD-RMFI) is proposed in this paper. The novel algorithm adopts the SPWVD-RMFI method to extract the pedestrian gait frequency and to calculate the optimal ZVI detection threshold in real time by establishing the function relationships between the thresholds and the gait frequency; then, the adaptive adjustment of thresholds with gait frequency is realized and improves the ZVI detection precision. To put it into practice, a ZVI detection experiment is carried out; the result shows that compared with the traditional fixed threshold ZVI detection method, the adaptive ZVI detection algorithm can effectively reduce the false and missed detection rate of ZVI; this indicates that the novel algorithm has high detection precision and good robustness. Furthermore, pedestrian trajectory positioning experiments at different walking speeds are carried out to evaluate the influence of the novel algorithm on positioning precision. The results show that the ZVI detected by the adaptive ZVI detection algorithm for pedestrian trajectory calculation can achieve better performance. PMID:27669266

Is the first seizure epilepsy--and when?

PubMed

Lawn, Nicholas; Chan, Josephine; Lee, Judy; Dunne, John

2015-09-01

Epilepsy has recently been redefined to include a single unprovoked seizure if the probability of recurrence is ≥60% over the following 10 years. This definition is based on the estimated risk of a third seizure after two unprovoked seizures, using the lower-limit 95% confidence interval (CI) at 4 years, and does not account for the initially high recurrence rate after first-ever seizure that rapidly falls with increasing duration of seizure freedom. We analyzed long-term outcomes after the first-ever seizure, and the influence of duration of seizure freedom on the likelihood of seizure recurrence, and their relevance to the new definition of epilepsy. Prospective analysis of 798 adults with a first-ever unprovoked seizure seen at a hospital-based first seizure clinic between 2000 and 2011. The likelihood of seizure recurrence was analyzed according to the duration of seizure freedom, etiology, electroencephalography (EEG), and neuroimaging findings. The likelihood of seizure recurrence at 10 years was ≥60% in patients with epileptiform abnormalities on EEG or neuroimaging abnormalities, therefore, meeting the new definition of epilepsy. However, the risk of recurrence was highly time dependent; after a brief period (≤12 weeks) of seizure freedom, no patient group continued to fulfill the new definition of epilepsy. Of 407 patients who had a second seizure, the likelihood of a third seizure at 4 years was 68% (95% CI 63-73%) and at 10 years was 85% (95% CI 79-91%). The duration of seizure freedom following first-ever seizure substantially influences the risk of recurrence, with none of our patients fulfilling the new definition of epilepsy after a short period of seizure freedom. When a threshold was applied based on the 10-year risk of a third seizure from our data, no first-seizure patient group ever had epilepsy. These data may be utilized in a definition of epilepsy after a first-ever seizure. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Predictors of seizure freedom after resection of supratentorial low-grade gliomas. A review.

PubMed

Englot, Dario J; Berger, Mitchel S; Barbaro, Nicholas M; Chang, Edward F

2011-08-01

Seizures are the most frequent presenting symptom in patients with low-grade gliomas (LGGs), and significantly influence quality of life if they are uncontrolled. Achieving freedom from seizures is of utmost importance in surgical planning, but the factors associated with seizure control remain incompletely understood. The authors performed a systematic literature review of seizure outcomes after resection of LGGs causing seizures, examining 773 patients across 20 published series. Rates of seizure freedom were stratified across 7 variables: patient age, tumor location, preoperative seizure control with medication, seizure semiology, epilepsy duration, extent of resection, and the use of intraoperative electrocorticography (ECoG). Gross-total resection was most predictive of complete seizure freedom, when compared with subtotal resection (OR 3.41, 95% CI 2.36-4.93). Other predictors of seizure freedom included preoperative seizure control on antiepileptic medication (OR 2.12, 95% CI 1.33-3.38) and duration of seizures of ≤ 1 year (OR 1.85, 95% CI 1.22-2.79). Patients with simple partial seizure semiology achieved seizure freedom less often than those with complex partial, generalized, or mixed seizure types (OR 0.46, 95% CI 0.26-0.80). No significant differences in seizure outcome were observed between adults versus children, patients with temporal lobe versus extratemporal tumors, or with the use of intraoperative ECoG. Seizure control is one of the most important considerations in planning surgery for low-grade brain tumors. Gross-total resection is a critical factor in achieving seizure freedom.

Neuroimaging in epilepsy.

PubMed

Sidhu, Meneka Kaur; Duncan, John S; Sander, Josemir W

2018-05-17

Epilepsy neuroimaging is important for detecting the seizure onset zone, predicting and preventing deficits from surgery and illuminating mechanisms of epileptogenesis. An aspiration is to integrate imaging and genetic biomarkers to enable personalized epilepsy treatments. The ability to detect lesions, particularly focal cortical dysplasia and hippocampal sclerosis, is increased using ultra high-field imaging and postprocessing techniques such as automated volumetry, T2 relaxometry, voxel-based morphometry and surface-based techniques. Statistical analysis of PET and single photon emission computer tomography (STATISCOM) are superior to qualitative analysis alone in identifying focal abnormalities in MRI-negative patients. These methods have also been used to study mechanisms of epileptogenesis and pharmacoresistance.Recent language fMRI studies aim to localize, and also lateralize language functions. Memory fMRI has been recommended to lateralize mnemonic function and predict outcome after surgery in temporal lobe epilepsy. Combinations of structural, functional and post-processing methods have been used in multimodal and machine learning models to improve the identification of the seizure onset zone and increase understanding of mechanisms underlying structural and functional aberrations in epilepsy.

Clinical utility of BOLD fMRI in preoperative work-up of epilepsy

PubMed Central

Ganesan, Karthik; Ursekar, Meher

2014-01-01

Surgical techniques have emerged as a viable therapeutic option in patients with drug refractory epilepsy. Pre-surgical evaluation of epilepsy requires a comprehensive, multiparametric, and multimodal approach for precise localization of the epileptogenic focus. Various non-invasive techniques are available at the disposal of the treating physician to detect the epileptogenic focus, which include electroencephalography (EEG), video-EEG, magnetic resonance imaging (MRI), functional MRI including blood oxygen level dependent (BOLD) techniques, single photon emission tomography (SPECT), and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET). Currently, non-invasive high-resolution MR imaging techniques play pivotal roles in the preoperative detection of the seizure focus, and represent the foundation for successful epilepsy surgery. BOLD functional magnetic resonance imaging (fMRI) maps allow for precise localization of the eloquent cortex in relation to the seizure focus. This review article focuses on the clinical utility of BOLD (fMRI) in the pre-surgical work-up of epilepsy patients. PMID:24851002

An unusual complication of invasive video-EEG monitoring: subelectrode hematoma without subdural component: case report.

PubMed

Bozkurt, Gokhan; Ayhan, Selim; Dericioglu, Nese; Saygi, Serap; Akalan, Nejat

2010-08-01

The potential complications of the subdural electrode implantation providing identification of the seizure focus and direct stimulation of the cerebral cortex for defining the eloquent cortical areas are epidural and subdural hematoma, cortical contusions, infection, brain edema, raised intracranial pressure, CSF leakage, and venous infarction have been previously reported in the literature. To present the first case of subelectrode hematoma without subdural component that was detected during invasive EEG monitoring after subdural electrode implantation. A 19-year-old female with drug resistant seizures was decided to undergo invasive monitoring with subdural electrodes. While good quality recordings had been initially obtained from all electrodes placed on the right parietal convexity, no cerebral cortical activity could be obtained from one electrode 2 days after the first operation. Explorative surgery revealed a circumscribed subelectrode hematoma without a subdural component. Awareness of the potential complications of subdural electrode implantation and close follow-up of the clinical findings of the patient are of highest value for early detection and successful management.

Holistic approach for automated background EEG assessment in asphyxiated full-term infants

NASA Astrophysics Data System (ADS)

Matic, Vladimir; Cherian, Perumpillichira J.; Koolen, Ninah; Naulaers, Gunnar; Swarte, Renate M.; Govaert, Paul; Van Huffel, Sabine; De Vos, Maarten

2014-12-01

Objective. To develop an automated algorithm to quantify background EEG abnormalities in full-term neonates with hypoxic ischemic encephalopathy. Approach. The algorithm classifies 1 h of continuous neonatal EEG (cEEG) into a mild, moderate or severe background abnormality grade. These classes are well established in the literature and a clinical neurophysiologist labeled 272 1 h cEEG epochs selected from 34 neonates. The algorithm is based on adaptive EEG segmentation and mapping of the segments into the so-called segments’ feature space. Three features are suggested and further processing is obtained using a discretized three-dimensional distribution of the segments’ features represented as a 3-way data tensor. Further classification has been achieved using recently developed tensor decomposition/classification methods that reduce the size of the model and extract a significant and discriminative set of features. Main results. Effective parameterization of cEEG data has been achieved resulting in high classification accuracy (89%) to grade background EEG abnormalities. Significance. For the first time, the algorithm for the background EEG assessment has been validated on an extensive dataset which contained major artifacts and epileptic seizures. The demonstrated high robustness, while processing real-case EEGs, suggests that the algorithm can be used as an assistive tool to monitor the severity of hypoxic insults in newborns.

A snapshot on NPS in Italy: Distribution of drugs in seized materials analysed in an Italian forensic laboratory in the period 2013-2015.

PubMed

Odoardi, Sara; Romolo, Francesco Saverio; Strano-Rossi, Sabina

2016-08-01

The diffusion of New Psychoactive Substances (NPS) in the illicit drug market is a worldwide problem. The aim of the study is to describe the qualitative distribution of drugs of abuse in seized materials confiscated in the Italian territory over the last two years. Between 2013 and 2015 162 seizures of substances purchased through the Internet and confiscated by police authorities were analyzed: 35 seizures (22%) were crystals of 3-methylmethcathinone (3-MMC). Although 3-MMC is subject to the relevant legislation in Italy, it is not controlled in other countries such as the Netherlands, from which the shipments originated. 33 seizures (20%) were crystals of 4-methylethcathinone (4-MEC), 19 seizures (12%) were powders containing methylenedioxypyrovalerone (MDPV). N,N-diallyl-5-methoxytryptamine (5-MeO-DALT) was identified in 5 powders, whereas ethylphenidate in six and pyrrolidinophenones in fourteen seized powders: 6 α-PVP (alpha-pyrrolidinovalerophenone), 6 α-PHP (alpha-pyrrolidinohexiophenone) and 1 α-PVT (alpha-pyrrolidinopentiothiophenone). Other substances identified were cathinones such as pentedrone, methylone, buthylone, ethylone, methedrone, 3-CMC (3-chloromethcathinone), 3,4-dimethylmethcathinone (3,4-DMMC), flephedrone (4-fluoromethcathinone or 4-FMC), 2-FMC and 3-FMC (2- and 3-fluoromethcathinone), MPPP (4-methyl-alpha-pyrrolidinopropiophenone), bk-2C-B (2-amino-1-(4-bromo-2,5-dimethoxyphenyl)ethan-1-one). Other compounds were NM2AI (N-methyl-2-aminoindane), MPA (1-(thiophen-2-yl)-2-methylaminopropane), MTTA (mephtetramine), 4-APB and 6-APB (4- and 6- (2-aminopropyl)benzofuran), 2-fluoromethamphetamine, 1mCPP (1-meta-chlorophenylpiperazine) and diphenidine, detected for the first time in Europe. Only three seizures contained synthetic cannabinoids, consisting of herbal blends soaked in N-(1-adamantyl)-1-pentyl-1H-indazole-3-carboxamide (AKB48), or a mixture of 5-F-AKB48 and BB-22 (1-(cyclohexylmethyl)-8-quinolinyl ester-1H-indole-3-carboxylic acid). In some mixtures of drugs - such as granules - 4-MEC and pentedrone were detected, also with traces of diphenidine on one occasion. In other cases 5-MeO-DALT, ethylphenidate and caffeine were mixed together. In one batch, the mixture was flephedrone and methoxethamine, whereas in another one the sample contained methylone, ethylone, methedrone, 4-fluoroamphetamine, 5-MeO-DALT and 5MeO-MiPT (N-methyl-N-isopropyl-5-methoxytryptamine). In 9 seizures, tablets shipped together with NPS were also found to contain sildenafil. The analyses performed on these seizures showed the presence of a wide number of NPS within the Italian boundaries coming from abroad, therefore this study confirms the threat for the public health, especially when the content of NPS being sold is not reported on the label or misleading. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Congenital Toxoplasmosis in Tunisia: Prenatal and Neonatal Diagnosis and Postnatal Follow-up of 35 Cases.

PubMed

Boudaouara, Yosr; Aoun, Karim; Maatoug, Rania; Souissi, Olfa; Bouratbine, Aïda; Abdallah, Rym Ben

2018-06-01

Congenital toxoplasmosis (CT) results from transplacental passage of Toxoplasma gondii to the fetus during acute maternal infection. Our study aims to report clinical and biological patterns of 35 cases of CT diagnosed at the department of the Parasitology of the Pasteur Institute of Tunis and to access the performance of prenatal and early postnatal diagnosis techniques. Serological screening of maternal infection was performed by Immunoglobulin (Ig) M and IgG detection and IgG avidity determination. Prenatal diagnosis was based on both Toxoplasma DNA detection in the amniotic fluid and monthly ultrasound examinations. polymerase chain reaction analysis on amniotic fluid, performed only in 15 cases, detected Toxoplasma 's DNA in five cases (33.3%). Ultrasound examination did not reveal any morphological abnormalities. Thirty newborns had serological criteria of Toxoplasma infection. Congenital toxoplasmosis diagnosis was confirmed in 23 cases (76.6%) by immunoblot. Among the 35 born-infants, five (14.3%) were symptomatic: three had chorioretinitis at the first clinical ocular examination, one had neurological symptoms (seizures) with positive parasite DNA in cerebral spinal fluid, and one had both ophthalmological and neurological damages- chorioretinitis and intracranial calcifications in the computed tomography scan. Thirty-four of 35 infected children were treated with pyrimethamine-sulfadiazine combination. Four (11.7%) of the treated infants showed abnormal hematological values because of the treatment side effect. Serological rebound was observed in seven infants. A screening program and a diagnostic algorithm in pregnant women should be implemented in Tunisia to improve the follow-up of seronegative ones and to prevent CT cases.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elmagarmid, A.K.

The availability of distributed data bases is directly affected by the timely detection and resolution of deadlocks. Consequently, mechanisms are needed to make deadlock detection algorithms resilient to failures. Presented first is a centralized algorithm that allows transactions to have multiple requests outstanding. Next, a new distributed deadlock detection algorithm (DDDA) is presented, using a global detector (GD) to detect global deadlocks and local detectors (LDs) to detect local deadlocks. This algorithm essentially identifies transaction-resource interactions that m cause global (multisite) deadlocks. Third, a deadlock detection algorithm utilizing a transaction-wait-for (TWF) graph is presented. It is a fully disjoint algorithmmore » that allows multiple outstanding requests. The proposed algorithm can achieve improved overall performance by using multiple disjoint controllers coupled with the two-phase property while maintaining the simplicity of centralized schemes. Fourth, an algorithm that combines deadlock detection and avoidance is given. This algorithm uses concurrent transaction controllers and resource coordinators to achieve maximum distribution. The language of CSP is used to describe this algorithm. Finally, two efficient deadlock resolution protocols are given along with some guidelines to be used in choosing a transaction for abortion.« less

Yield of MRI, high-density electric source imaging (HD-ESI), SPECT and PET in epilepsy surgery candidates.

PubMed

Lascano, Agustina M; Perneger, Thomas; Vulliemoz, Serge; Spinelli, Laurent; Garibotto, Valentina; Korff, Christian M; Vargas, Maria I; Michel, Christoph M; Seeck, Margitta

2016-01-01

Preoperative workup aims at localizing the epileptogenic focus to achieve postoperative seizure-freedom. We studied the predictive value of non-invasive techniques, i.e. structural magnetic resonance imaging [MRI], high-density electric source imaging [HD-ESI] and metabolic imaging (positron emission tomography [PET]; single-photon emission computed tomography [SPECT]), in surgically treated patients. A prospective study of 190 epileptic operated patients, with >12 months follow-up and analyzed with state-of-the-art algorithms. 58 patients underwent all techniques. We computed sensitivity, specificity, predictive value and diagnostic odds ratio (OR) in relation to postoperative outcome. Of 190 patients, 148 (77.9%) were seizure-free at follow-up. Resection of the epileptogenic focus was associated with favorable postsurgical outcome (p<0.05). Among 58 patients who underwent all tests, only MRI and HD-ESI were favorable outcome predictors (MRI: OR 10.9, p=0.004; HD-ESI: OR 13.1, p=0.004). Patients with concordant structural MRI and HD-ESI results had 92.3% (24/26) probability of favorable outcome. When both results were negative, probability was 0% (0/5); and when they disagreed, it was 63.0% (17/27). Combination of MRI and HD-ESI offered the highest predictive value for postoperative seizure-freedom. This finding highlights the added value of HD-ESI in the presurgical workup, in particular in combination with an informative MRI. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Seizure prognosis of patients with low-grade tumors.

PubMed

Kahlenberg, Cynthia A; Fadul, Camilo E; Roberts, David W; Thadani, Vijay M; Bujarski, Krzysztof A; Scott, Rod C; Jobst, Barbara C

2012-09-01

Seizures frequently impact the quality of life of patients with low grade tumors. Management is often based on best clinical judgment. We examined factors that correlate with seizure outcome to optimize seizure management. Patients with supratentorial low-grade tumors evaluated at a single institution were retrospectively reviewed. Using multiple regression analysis the patient characteristics and treatments were correlated with seizure outcome using Engel's classification. Of the 73 patients with low grade tumors and median follow up of 3.8 years (range 1-20 years), 54 (74%) patients had a seizure ever and 46 (63%) had at least one seizure before tumor surgery. The only factor significantly associated with pre-surgical seizures was tumor histology. Of the 54 patients with seizures ever, 25 (46.3%) had a class I outcome at last follow up. There was no difference in seizure outcome between grade II gliomas (astrocytoma grade II, oligodendroglioma grade II, mixed oligo-astrocytoma grade II) and other pathologies (pilocytic astrocytoma, ependymomas, DNET, gangliocytoma and ganglioglioma). Once seizures were established seizure prognosis was similar between different pathologies. Chemotherapy (p=0.03) and radiation therapy (p=0.02) had a positive effect on seizure outcome. No other parameter including significant tumor growth during the follow up period predicted seizure outcome. Only three patients developed new-onset seizures after tumor surgery that were non-perioperative. Anticonvulsant medication was tapered in 14 patients with seizures and 10 had no further seizures. Five patients underwent additional epilepsy surgery with a class I outcome in four. Two patients received a vagal nerve stimulator with >50% seizure reduction. Seizures at presentation are the most important factor associated with continued seizures after tumor surgery. Pathology does not influence seizure outcome. Use of long term prophylactic anticonvulsants is unwarranted. Chemotherapy and radiation therapy have a favorable impact on seizure outcome. Additional epilepsy surgery is effective. Copyright © 2012 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

How do doctors in training react to seizures?

PubMed

Seneviratne, Udaya; Ma, Henry; Phan, Thanh G

2016-01-01

There are scant data on how doctors approach seizures in the acute setting. We sought to study (a) exposure to seizure disorders as well as relevant training and (b) reactions to seizures in the acute setting, among medical residents undergoing physician training. The exposure to and training on seizure disorders were assessed using a structured questionnaire first. Then, they were tested with 20 videos consisting of 10 epileptic seizures (ESs) and 10 psychogenic nonepileptic seizures (PNESs). After each video, we asked three questions to test (a) the diagnosis and the practice of administration of benzodiazepines to terminate the seizure, (b) the estimation of seizure duration, and (c) the practice of intubation. The accuracy of diagnosis was measured by the area under the summary receiver operating characteristics curve (AUC). The difference between true seizure duration and estimated duration was evaluated using paired-sample t-test. A total of 48 trainees participated in the study. The majority witnessed seizures in movies (37, 77.1%) and television (35, 72.9%). Only 12 (25%) received bedside teaching on seizure disorders. Their diagnostic accuracy of seizures was very poor (AUC=0.54). Participants significantly underestimated the duration of seizures. Thirty-five doctors made an illogical decision to intubate but not to terminate the seizure with intravenous benzodiazepine. The diagnostic accuracy of seizures is poor among trainees, and their estimates of seizure duration are unreliable. Our study highlights potential pitfalls in the acute management of seizures and the need for more training on seizure disorders. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Incidence of seizures following initial ischemic stroke in a community-based cohort: The Framingham Heart Study.

PubMed

Stefanidou, Maria; Das, Rohit R; Beiser, Alexa S; Sundar, Banu; Kelly-Hayes, Margaret; Kase, Carlos S; Devinsky, Orrin; Seshadri, Sudha; Friedman, Daniel

2017-04-01

We examined the incidence of seizures following ischemic stroke in a community-based sample. All subjects with incident ischemic strokes in the Framingham Original and Offspring cohorts between 1982 and 2003 were identified and followed for up to 20 years to determine incidence of seizures. Seizure-type was based on the 2010 International League Against Epilepsy (ILAE) classification. Disability was stratified into mild/none, moderate and severe, based on post-stroke neurological deficit documentation according to the Framingham Heart Study (FHS) protocol and functional status was determined using the Barthel Index. An initial ischemic stroke occurred in 469 subjects in the cohort and seizures occurred in 25 (5.3%) of these subjects. Seizure incidence was similar in both large artery atherosclerosis (LAA) (6.8%) and cardio-embolic (CE) (6.2%) strokes. No seizures occurred following lacunar strokes. The predominant seizure type was focal seizure with or without evolution to bilateral convulsive seizure. One third of participants had seizures within the first 24h from stroke onset and half of all seizures occurred within the first 30days. On multivariate analysis, moderate and severe disability following stroke was associated with increased risk of incident seizure. Seizures occurred in approximately 5% of subjects after an ischemic stroke. One third of these seizures occurred in the first 24h after stroke and none followed lacunar strokes. Focal seizures with or without evolution in bilateral convulsive seizures were the most common seizure type. Moderate and severe disability was predictive of incident seizures. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Seizures and the natural history of World Health Organization Grade II gliomas: a review.

PubMed

Smits, Anja; Duffau, Hugues

2011-05-01

The majority of adults with low-grade gliomas have seizures. Despite the frequency of seizures as initial symptoms and symptoms of later disease, seizures in relation to the natural course of low-grade gliomas have received little attention. In this review, we provide an update of the literature on the prognostic impact of preoperative seizures and discuss the tumor- and treatment-related factors affecting seizure control at later stages of the disease. Seizures occur most frequently at disease presentation and predict a more favorable outcome. Initial seizures are correlated with tumor location and possibly indirectly to the molecular profile of the tumor. About 50% of all patients with seizures at presentation continue to have seizures before surgery. Maximal tumor resection, including resection of epileptic foci, is a valuable strategy for improving seizure control. In addition, radiotherapy and chemotherapy, as single therapies or in combination with surgery, have shown beneficial effects in terms of seizure reduction. Recurrent seizures after macroscopically complete tumor resection may be a marker for accelerated tumor growth. Recurrent seizures after an initial transient stabilization after radiotherapy and/or chemotherapy may be a marker for anaplastic tumor transformation. Preoperative seizures likely reflect, apart from tumor location, intrinsic tumor properties as well. Change in seizure control in individual patients is frequently associated with altered tumor behavior. Including seizures and seizure control as clinical parameters is recommended in future trials of low-grade gliomas to further establish the prognostic value of these symptoms and to identify the factors affecting seizure control.

Experiences in the field of radioactive materials seizures in the Czech Republic

DOE Office of Scientific and Technical Information (OSTI.GOV)

Svoboda, Karel; Podlaha, Josef; Sir, David

2007-07-01

In recent years, the amount of radioactive materials seizures (captured radioactive materials) has been rising. It was above all due to newly installed detection facilities that were able to check metallic scrap during its collection in scrap yards or on the entrance to iron-mills, checking municipal waste upon entrance to municipal disposal sites, even incineration plants, or through checking vehicles going through the borders of the Czech Republic. Most cases bore a relationship to secondary raw materials or they were connected to the application of machines and installations made from contaminated metallic materials. However, in accordance to our experience, themore » number of cases of seizures of materials and devices containing radioactive sources used in the public domain was lower, but not negligible, in the municipal storage yards or incineration plants. Atomic Act No. 18/1997 Coll. will apply to everybody who provides activities leading to exposure, mandatory assurance as high radiation safety as risk of the endangering of life, personal health and environment is as low as reasonably achievable in according to social and economic aspects. Hence, attention on the examination of all cases of the radioactive material seizure based on detection facilities alarm or reasonably grounds suspicion arising from the other information is important. Therefore, a service carried out by group of workers who ensure assessment of captured radioactive materials and eventual retrieval of radioactive sources from the municipal waste has come into existence in the Nuclear Research Institute Rez plc. This service has covered also transport, storage, processing and disposal of found radioactive sources. This service has arisen especially for municipal disposal sites, but later on even other companies took advantage of this service like incineration plants, the State Office for Nuclear Safety, etc. Our experience in the field of ensuring assessment of captured radioactive materials and eventual retrieval of radioactive sources will be presented in the paper. (authors)« less

Genetic (idiopathic) epilepsy with photosensitive seizures includes features of both focal and generalized seizures.

PubMed

Xue, Jiao; Gong, Pan; Yang, Haipo; Liu, Xiaoyan; Jiang, Yuwu; Zhang, Yuehua; Yang, Zhixian

2018-04-19

Clinically, some patients having genetic (idiopathic) epilepsy with photosensitive seizures were difficult to be diagnosed. We aimed to discuss whether the genetic (idiopathic) epilepsy with photosensitive seizures is a focal entity, a generalized entity or a continuum. Twenty-two patients with idiopathic epilepsies and photoconvulsive response (PCR) were retrospectively recruited. In the medical records, the seizure types included "generalized tonic-clonic seizures (GTCS)" in 15, "partial secondarily GTCS (PGTCS)" in 3, partial seizures (PS) in 3, myoclonic seizures in 2, eyelid myoclonus in one, and only febrile seizures in one. Seizure types of PCR included GTCS (1/22), PGTCS (6/22), PS (9/22), electrical seizures (ES) (3/22) and GTCS/PGTCS (3/22). Combined the medical history with PCR results, they were diagnosed as: idiopathic (photosensitive) occipital lobe epilepsy (I(P)OE) in 12, genetic (idiopathic) generalized epilepsy (GGE) in one, GGE/I(P)OE in 5, pure photosensitive seizure in one, and epilepsy with undetermined generalized or focal seizure in 3. So, the dichotomy between generalized and focal seizures might have been out of date regarding to pathophysiological advances in epileptology. To some extent, it would be better to recognize the idiopathic epilepsy with photosensitive seizures as a continuum between focal and generalized seizures.

Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis.

PubMed

Yang, Chao; He, Zengyou; Yu, Weichuan

2009-01-06

In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis. Recently, there has been significant progress in the development of various peak detection algorithms. However, neither a comprehensive survey nor an experimental comparison of these algorithms is yet available. The main objective of this paper is to provide such a survey and to compare the performance of single spectrum based peak detection methods. In general, we can decompose a peak detection procedure into three consequent parts: smoothing, baseline correction and peak finding. We first categorize existing peak detection algorithms according to the techniques used in different phases. Such a categorization reveals the differences and similarities among existing peak detection algorithms. Then, we choose five typical peak detection algorithms to conduct a comprehensive experimental study using both simulation data and real MALDI MS data. The results of comparison show that the continuous wavelet-based algorithm provides the best average performance.

Seizure clustering.

PubMed

Haut, Sheryl R

2006-02-01

Seizure clusters, also known as repetitive or serial seizures, occur commonly in epilepsy. Clustering implies that the occurrence of one seizure may influence the probability of a subsequent seizure; thus, the investigation of the clustering phenomenon yields insights into both specific mechanisms of seizure clustering and more general concepts of seizure occurrence. Seizure clustering has been defined clinically as a number of seizures per unit time and, statistically, as a deviation from a random distribution, or interseizure interval dependence. This review explores the pathophysiology, epidemiology, and clinical implications of clustering, as well as other periodic patterns of seizure occurrence. Risk factors for experiencing clusters and potential precipitants of clustering are also addressed.

Perceived stress and its predictors in people with epilepsy.

PubMed

Moon, Hye-Jin; Seo, Jong-Geun; Park, Sung-Pa

2016-09-01

Perceived stress in people with epilepsy (PWE) is one of the major precipitants for seizures. We investigated the degree of perceived stress in PWE and its predictors. We also aimed to reveal the interrelationships among the predictors. This was a case-control study. Consecutive patients visiting a tertiary care epilepsy clinic completed self-reported questionnaires including the Perceived Stress Scale (PSS), Revised Stigma Scale (RSS), Korean version of the Neurological Disorders Depression Inventory for Epilepsy (K-NDDI-E), Generalized Anxiety Disorder - 7 (GAD-7), and short forms of the Patient-Reported Outcomes Measurement Information System - Sleep Disturbance (PROMIS-SD) and Patient-Reported Outcomes Measurement Information System - Sleep-Related Impairment (PROMIS-SRI) scales. The mean score of the PSS was significantly lower in patients with well-controlled epilepsy (WCE) and higher in those with uncontrolled epilepsy compared with controls. Although several factors including demographic, socioeconomic, psychosomatic, and epilepsy-related factors were associated with the PSS score, the strongest predictor for the PSS score was the K-NDDI-E score, followed by the PROMIS-SRI score, the GAD-7 score, and seizure control. Psychosomatic factors exerted both a direct effect on the PSS score and an indirect effect on the PSS score through seizure control. Rapid detection and appropriate management of psychiatric and sleep-related problems in PWE may lessen stress and aid in preventing further seizures. Copyright © 2016 Elsevier Inc. All rights reserved.

International Veterinary Epilepsy Task Force recommendations for a veterinary epilepsy-specific MRI protocol.

PubMed

Rusbridge, Clare; Long, Sam; Jovanovik, Jelena; Milne, Marjorie; Berendt, Mette; Bhatti, Sofie F M; De Risio, Luisa; Farqhuar, Robyn G; Fischer, Andrea; Matiasek, Kaspar; Muñana, Karen; Patterson, Edward E; Pakozdy, Akos; Penderis, Jacques; Platt, Simon; Podell, Michael; Potschka, Heidrun; Stein, Veronika M; Tipold, Andrea; Volk, Holger A

2015-08-28

Epilepsy is one of the most common chronic neurological diseases in veterinary practice. Magnetic resonance imaging (MRI) is regarded as an important diagnostic test to reach the diagnosis of idiopathic epilepsy. However, given that the diagnosis requires the exclusion of other differentials for seizures, the parameters for MRI examination should allow the detection of subtle lesions which may not be obvious with existing techniques. In addition, there are several differentials for idiopathic epilepsy in humans, for example some focal cortical dysplasias, which may only apparent with special sequences, imaging planes and/or particular techniques used in performing the MRI scan. As a result, there is a need to standardize MRI examination in veterinary patients with techniques that reliably diagnose subtle lesions, identify post-seizure changes, and which will allow for future identification of underlying causes of seizures not yet apparent in the veterinary literature.There is a need for a standardized veterinary epilepsy-specific MRI protocol which will facilitate more detailed examination of areas susceptible to generating and perpetuating seizures, is cost efficient, simple to perform and can be adapted for both low and high field scanners. Standardisation of imaging will improve clinical communication and uniformity of case definition between research studies. A 6-7 sequence epilepsy-specific MRI protocol for veterinary patients is proposed and further advanced MR and functional imaging is reviewed.

[A case of amyloid-β-related cerebral angiitis with ApoE ε4/ε2 genotype].

PubMed

Ogura, Aya; Moriyoshi, Hideyuki; Nakai, Noriyoshi; Nishida, Suguru; Kitagawa, Satoshi; Yoshida, Mari; Yasuda, Takeshi; Ito, Yasuhiro

2015-01-01

A 53-year-old male with a past medical history of hypertension and bipolar disorder gradually developed gait disturbance and cognitive dysfunction over half a year. His cranial MRI showed an area of hyperintensity in the right occipital lobe on T2 weighted images and the surface of the lesion was enhanced along the sulci. We diagnosed his condition as amyloid-β-related angiitis (ABRA) based on brain biopsy. Repeated, frequent seizures resistant to several antiepileptic drugs (AEDs) occurred after the operation. Steroid therapy was effective and the symptoms, including the intractable seizures and MRI abnormalities dramatically improved. In contrast to the common wild type ε3/ε3 ApoE genotype, a majority of ABRA patients have ε4/ε4. However, in this case the rare ε4/ε2 type was detected. The ε4 allele is considered to promote Aβ deposition on the vessel wall, and ε2 is speculated to trigger vessel ruptures or vascular inflammation. Although seizure is not a common complication of brain biopsy, it occurred repeatedly and responded poorly to AEDs in this case. Surgical stress in this patient with ε2 probably induced the uncontrolled seizures. ApoE genotype may be an effective and low-invasive marker in case of suspected ABRA and in predicting the risks of the complication from brain biopsy.

Transition dynamics of generalized multiple epileptic seizures associated with thalamic reticular nucleus excitability: A computational study

NASA Astrophysics Data System (ADS)

Liu, Suyu; Wang, Qingyun

2017-11-01

Presently, we improve a computational framework of thalamocortical circuits related to the Taylor's model to investigate the relationship between thalamic reticular nucleus (RE) excitability and epilepsy. By using bifurcation analysis, we explore the RE's excitability dynamics mechanism in the processes of seizure generation, development and transition. Results show that the seizure-free state, absence seizures, clonic seizures and tonic seizures can be formed as the RE excitability is changed in this established model. Importantly, it is verified that physiological changing GABAA inhibition in RE can elicit absence seizures and clonic seizures and the pathological transitions between these two seizures. Furthermore, when the level of AMPA connection is decreased or increased, this proposed model embraces absence seizures and clonic seizures, and tonic seizures, respectively. Except that, bifurcation mechanisms of dynamical transition of different seizures are analyzed in detail. In addition, hybrid regulations of the reticular nucleus excitability for epileptic seizures are proven to be valid within the suitable levels of AMPA and GABAA connection. Hopefully, the obtained results could be helpful for effective control of epileptic activities with additional pharmacological interference.

Precipitating factors and therapeutic outcome in epilepsy with generalized tonic-clonic seizures.

PubMed

Bauer, J; Saher, M S; Burr, W; Elger, C E

2000-10-01

The aim of the study was to evaluate the influence of precipitating factors and therapy on the outcome of epilepsy with generalized tonic-clonic seizures. Retrospective analysis of data from 34 patients (mean age at seizure onset 19 years; mean duration of follow-up 9.2 years) suffering from epilepsy of either cryptogenic or remote symptomatic (n = 19), or idiopathic (n = 15) etiology. The total number of seizures in all patients was 146. Without treatment 97 seizures manifested during 90.5 years without treatment (1.07 seizures/year), during treatment with carbamazepine or valproate 49 seizures occurred within 224 years (0.2 seizures/year). The frequency of seizures was significantly lower during treatment. Precipitating factors were found in relation to 31% of seizures in patients with remote symptomatic or cryptogenic epilepsy, and for 51% of seizures in patients with idiopathic epilepsy. There was a low frequency of seizures in patients with generalized tonic-clonic seizures. Precipitating factors are common. Antiepileptic drug treatment is effective.

Long-term outcome and risk factors for uncontrolled seizures after a first seizure in children with hematological malignancies.

PubMed

Khan, Raja B; Morris, E Brannon; Pui, Ching-Hon; Hudson, Melissa M; Zhou, Yinmei; Cheng, Cheng; Ledet, Davonna S; Howard, Scott C

2014-06-01

Long-term outcomes of seizures that develop during treatment of childhood hematological malignancies have not been described. We analyzed seizure outcome in 62 children with leukemia or lymphoma treated at our institution. There was a median follow-up of 6.5 years since first seizure. Seizure etiology included intrathecal or systemic methotrexate in 24, leucoencephalopathy in 11, brain hemorrhage or thrombosis in 11, meningitis in 4, and no identifiable cause in 12. Seizures remained uncontrolled in 18, and risk factors for poor control included female sex (P = .02), no seizure control with first antiseizure drug (P = .08), and longer interval between cancer diagnosis and seizure onset (P = .09). Poor seizure control after initial antiseizure drug also predicted recurrent seizure after drug withdrawal (P = .04). In conclusion, seizures are controlled with medications in a majority of patients with hematological cancer. After a period without seizures, antiseizure drug withdrawal in appropriately selected patient has a high success rate. © The Author(s) 2013.

The Feasibility of Detecting Neuropsychologic and Neuroanatomic Effects of Type 1 Diabetes in Young Children

PubMed Central

Aye, Tandy; Reiss, Allan L.; Kesler, Shelli; Hoang, Sherry; Drobny, Jessica; Park, Yaena; Schleifer, Kristin; Baumgartner, Heidi; Wilson, Darrell M.; Buckingham, Bruce A.

2011-01-01

OBJECTIVE To determine if frequent exposures to hypoglycemia and hyperglycemia during early childhood lead to neurocognitive deficits and changes in brain anatomy. RESEARCH DESIGN AND METHODS In this feasibility, cross-sectional study, young children, aged 3 to 10 years, with type 1 diabetes and age- and sex-matched healthy control (HC) subjects completed neuropsychologic (NP) testing and magnetic resonance imaging (MRI) scans of the brain. RESULTS NP testing and MRI scanning was successfully completed in 98% of the type 1 diabetic and 93% of the HC children. A significant negative relationship between HbA1c and Wechsler Intelligence Scale for Children (WISC) verbal comprehension was observed. WISC index scores were significantly reduced in type 1 diabetic subjects who had experienced seizures. White matter volume did not show the expected increase with age in children with type 1 diabetes compared with HC children (diagnosis by age interaction, P = 0.005). A similar trend was detected for hippocampal volume. Children with type 1 diabetes who had experienced seizures showed significantly reduced gray matter and white matter volumes relative to children with type 1 diabetes who had not experienced seizures. CONCLUSIONS It is feasible to perform MRI and NP testing in young children with type 1 diabetes. Further, early signs of neuroanatomic variation may be present in this population. Larger cross-sectional and longitudinal studies of neurocognitive function and neuroanatomy are needed to define the effect of type 1 diabetes on the developing brain. PMID:21562318

Localizing seizure-onset zones in presurgical evaluation of drug-resistant epilepsy by electroencephalography/fMRI: effectiveness of alternative thresholding strategies.

PubMed

Hauf, M; Jann, K; Schindler, K; Scheidegger, O; Meyer, K; Rummel, C; Mariani, L; Koenig, T; Wiest, R

2012-10-01

Simultaneous EEG/fMRI is an effective noninvasive tool for identifying and localizing the SOZ in patients with focal epilepsy. In this study, we evaluated different thresholding strategies in EEG/fMRI for the assessment of hemodynamic responses to IEDs in the SOZ of drug-resistant epilepsy. Sixteen patients with focal epilepsy were examined by using simultaneous 92-channel EEG and BOLD fMRI. The temporal fluctuation of epileptiform signals on the EEG was extracted by independent component analysis to predict the hemodynamic responses to the IEDs. We applied 3 different threshold criteria to detect hemodynamic responses within the SOZ: 1) PA, 2) a fixed threshold at P < .05 corrected for multiple comparison (FWE), and 3) FAV (4000 ± 200 activated voxels within the brain). PA identified the SOZ in 9 of 16 patients; FWE resulted in concordant BOLD signal correlates in 11 of 16, and FAV in 13 of 16 patients. Hemodynamic responses were detected within the resected areas in 5 (PA), 6 (FWE), and 8 (FAV) of 10 patients who remained seizure-free after surgery. EEG/fMRI is a noninvasive tool for the presurgical work-up of patients with epilepsy, which can be performed during seizure-free periods and is complementary to the ictal electroclinical assessment. Our findings suggest that the effectiveness of EEG/fMRI in delineating the SOZ may be further improved by the additional use of alternative analysis strategies such as FAV.

Evaluation of Kilifi epilepsy education programme: a randomized controlled trial.

PubMed

Ibinda, Fredrick; Mbuba, Caroline K; Kariuki, Symon M; Chengo, Eddie; Ngugi, Anthony K; Odhiambo, Rachael; Lowe, Brett; Fegan, Greg; Carter, Julie A; Newton, Charles R

2014-02-01

The epilepsy treatment gap is largest in resource-poor countries. We evaluated the efficacy of a 1-day health education program in a rural area of Kenya. The primary outcome was adherence to antiepileptic drugs (AEDs) as measured by drug levels in the blood, and the secondary outcomes were seizure frequency and Kilifi Epilepsy Beliefs and Attitudes Scores (KEBAS). Seven hundred thirty-eight people with epilepsy (PWE) and their designated supporter were randomized to either the intervention (education) or nonintervention group. Data were collected at baseline and 1 year after the education intervention was administered to the intervention group. There were 581 PWE assessed at both time points. At the end of the study, 105 PWE from the intervention group and 86 from the nonintervention group gave blood samples, which were assayed for the most commonly used AEDs (phenobarbital, phenytoin, and carbamazepine). The proportions of PWE with detectable AED levels were determined using a standard blood assay method. The laboratory technicians conducting the assays were blinded to the randomization. Secondary outcomes were evaluated using questionnaires administered by trained field staff. Modified Poisson regression was used to investigate the factors associated with improved adherence (transition from nonoptimal AED level in blood at baseline to optimal levels at follow-up), reduced seizures, and improved KEBAS, which was done as a post hoc analysis. This trial is registered in ISRCTN register under ISRCTN35680481. There was no significant difference in adherence to AEDs based on detectable drug levels (odds ratio [OR] 1.46, 95% confidence interval [95% CI] 0.74-2.90, p = 0.28) or by self-reports (OR 1.00, 95% CI 0.71-1.40, p = 1.00) between the intervention and nonintervention group. The intervention group had significantly fewer beliefs about traditional causes of epilepsy, cultural treatment, and negative stereotypes than the nonintervention group. There was no difference in seizure frequency. A comparison of the baseline and follow-up data showed a significant increase in adherence-intervention group (36-81% [p < 0.001]) and nonintervention group (38-74% [p < 0.001])-using detectable blood levels. The number of patients with less frequent seizures (≤3 seizures in the last 3 months) increased in the intervention group (62-80% [p = 0.002]) and in the nonintervention group (67-75% [p = 0.04]). Improved therapeutic adherence (observed in both groups combined) was positively associated with positive change in beliefs about risks of epilepsy (relative risk [RR] 2.00, 95% CI 1.03-3.95) and having nontraditional religious beliefs (RR 2.01, 95% CI 1.01-3.99). Reduced seizure frequency was associated with improved adherence (RR 1.72, 95% CI 1.19-2.47). Positive changes in KEBAS were associated with having tertiary education as compared to none (RR 1.09, 95% CI 1.05-1.14). Health education improves knowledge about epilepsy, but once only contact does not improve adherence. However, sustained education may improve adherence in future studies. © 2013 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

Cerebellar output controls generalized spike‐and‐wave discharge occurrence

PubMed Central

Kros, Lieke; Eelkman Rooda, Oscar H. J.; Spanke, Jochen K.; Alva, Parimala; van Dongen, Marijn N.; Karapatis, Athanasios; Tolner, Else A.; Strydis, Christos; Davey, Neil; Winkelman, Beerend H. J.; Negrello, Mario; Serdijn, Wouter A.; Steuber, Volker; van den Maagdenberg, Arn M. J. M.; De Zeeuw, Chris I.

2015-01-01

Objective Disrupting thalamocortical activity patterns has proven to be a promising approach to stop generalized spike‐and‐wave discharges (GSWDs) characteristic of absence seizures. Here, we investigated to what extent modulation of neuronal firing in cerebellar nuclei (CN), which are anatomically in an advantageous position to disrupt cortical oscillations through their innervation of a wide variety of thalamic nuclei, is effective in controlling absence seizures. Methods Two unrelated mouse models of generalized absence seizures were used: the natural mutant tottering, which is characterized by a missense mutation in Cacna1a, and inbred C3H/HeOuJ. While simultaneously recording single CN neuron activity and electrocorticogram in awake animals, we investigated to what extent pharmacologically increased or decreased CN neuron activity could modulate GSWD occurrence as well as short‐lasting, on‐demand CN stimulation could disrupt epileptic seizures. Results We found that a subset of CN neurons show phase‐locked oscillatory firing during GSWDs and that manipulating this activity modulates GSWD occurrence. Inhibiting CN neuron action potential firing by local application of the γ‐aminobutyric acid type A (GABA‐A) agonist muscimol increased GSWD occurrence up to 37‐fold, whereas increasing the frequency and regularity of CN neuron firing with the use of GABA‐A antagonist gabazine decimated its occurrence. A single short‐lasting (30–300 milliseconds) optogenetic stimulation of CN neuron activity abruptly stopped GSWDs, even when applied unilaterally. Using a closed‐loop system, GSWDs were detected and stopped within 500 milliseconds. Interpretation CN neurons are potent modulators of pathological oscillations in thalamocortical network activity during absence seizures, and their potential therapeutic benefit for controlling other types of generalized epilepsies should be evaluated. Ann Neurol 2015;77:1027–1049 PMID:25762286

Cyclooxygenase system contributes to the maintenance of post convulsive period of epileptic phenomena in the genetically epileptic El mice.

PubMed

Okada, Kazumasa; Yamashita, Uki; Tsuji, Sadatoshi

2006-09-01

Recent studies have shown that cytokines and cyclooxygenase (COX)-2 are up-regulated in the brain of human epilepsy patients and animal models of epilepsy. We investigated the effect of inflammatory responses induced by intramuscular injection of turpentine on the epileptic phenomenon in genetically epileptic El mice. As parameters of epileptic seizure, seizure threshold (number of toss-ups to induce convulsion), duration of actual convulsion and duration of post actual convulsive period (period from the offset of convulsion to full recovery) were evaluated. The post actual convulsive period was prolonged without any change of seizure threshold or duration of actual convulsion 24 h after turpentine injection. Although pretreatment with indomethacin for one week did not change the seizure parameters, indomethacin suppressed the prolongation of the post actual convulsive period induced by turpentine. The mRNA expression of IL-1beta, IL-6 and COX-2 in the cerebral cortex was detected by RT-PCR. There was no difference in the mRNA expression in the cerebral cortex before and 24 h after seizure. The mRNA levels of IL-1beta, IL-6 and COX-2 in the cerebral cortex were up-regulated 24 h after turpentine injection. On the other hand, the up-regulated mRNA levels of IL-1beta, IL-6 and COX-2 in the cerebral cortex after turpentine treatment were not suppressed by indomethacin. These results suggest that prostaglandins induced with COX-2 in the cerebral cortex seem to play an important role in the maintenance of the post convulsive period, but not in induction and maintenance of the actual convulsive state.

A retrospective comparison of the effects of propofol and etomidate on stimulus variables and efficacy of electroconvulsive therapy in depressed inpatients.

PubMed

Graveland, Pieternella E; Wierdsma, André I; van den Broek, Walter W; Birkenhäger, Tom K

2013-08-01

To compare the effects of propofol and etomidate on the stimulus variables and efficacy of electroconvulsive therapy (ECT) in depressed inpatients. This retrospective study included 54 inpatients (aged 18-75 years) who met the DSM-IV criteria for major depression and were treated with bilateral ECT. For the first part of the study, the primary outcome was the mean stimulus charge per ECT session. For the second part, the main outcome measure was the proportion of patients achieving full remission. Propofol-treated patients showed a higher mean stimulus charge (etomidate = 227.58 ± 130.44, propofol = 544.91 ± 237.56, p<0.001) despite the lack of a significant difference in starting threshold doses. The propofol group had shorter mean electroencephalogram (etomidate = 69.41 ± 22.50, propofol = 42.95 ± 22.26, p<0.001) seizure duration and motor (etomidate = 46.11 ± 14.38, propofol = 22.89 ± 7.13, p<0.001) seizure duration and a higher mean number of inadequate seizures (etomidate = 0.12 ± 0.15, propofol = 0.47 ± 0.26, p<0.001). No significant differences were found between the groups for the effects of the anesthetics on the efficacy of ECT. Our study is limited by a retrospective design and the small number of patients treated with propofol restricted the sample size. Anesthesia with propofol has a significant reducing effect on seizure duration during the course of ECT which results in more inadequate seizures, despite the use of a higher mean stimulus charge. Regarding the possible effect of the anesthetics on ECT, randomized clinical trials with sufficient power to detect differences are warranted. Copyright © 2013 Elsevier Inc. All rights reserved.

Targeted Interneuron Ablation in the Mouse Hippocampus Can Cause Spontaneous Recurrent Seizures

PubMed Central

2017-01-01

Abstract The death of GABAergic interneurons has long been hypothesized to contribute to acquired epilepsy. These experiments tested the hypothesis that focal interneuron lesions cause acute seizures [i.e., status epilepticus (SE)] and/or chronic epilepsy [i.e., persistent spontaneous recurrent seizures (SRSs)]. To selectively ablate interneurons, Gad2-ires-Cre mice were injected unilaterally in the CA1 area of the dorsal hippocampus with an adeno-associated virus containing the diphtheria toxin receptor (DTR). Simultaneously, an electrode, connected to a miniature telemetry device, was positioned at the injection site for chronic recordings of local field potentials (LFPs). Two weeks after virus transfection, intraperitoneal injection of DT consistently caused focal, specific, and extensive ablation of interneurons. Long-term, continuous monitoring revealed that all mice with DT-induced interneuron lesions had SRSs. Seizures lasted tens of seconds and interseizure intervals were several hours (or days); therefore, these interneuron lesions did not induce SE. The SRSs occurred 3-5 d after DT treatment, which is the estimated time required for DT-induced cell death; therefore, induction of SRSs occurred without the latent period typical of acquired epilepsy. In five of six DT-treated mice, SRSs stopped within days, suggesting that the DT-induced interneuron lesions did not usually cause epilepsy. In one mouse, however, SRSs occurred for ≥34 d after interneuron ablation, similar to epilepsy after experimental SE. Sham control mice had no detectable seizures, confirming that the SRSs were due to ablation of interneurons. These data show that selective interneuron ablation consistently caused SRSs but not SE; and, at least under the conditions used here, interneuron lesions rarely led to persistent SRSs (i.e., epilepsy). PMID:28785726

From the Cover: Magnetic Resonance Imaging Reveals Progressive Brain Injury in Rats Acutely Intoxicated With Diisopropylfluorophosphate

PubMed Central

Hobson, Brad A.; Sisó, Sílvia; Rowland, Douglas J.; Harvey, Danielle J.; Bruun, Donald A.; Garbow, Joel R.

2017-01-01

Abstract Acute intoxication with organophosphates (OPs) can trigger seizures that progress to status epilepticus, and survivors often exhibit chronic neuropathology, cognitive impairment, affective disorders, and/or electroencephalographic abnormalities. Understanding how acute injury transitions to persistent neurological sequelae is critical to developing medical countermeasures for mitigating damage following OP-induced seizures. Here, we used in vivo magnetic resonance imaging (MRI) to monitor the spatiotemporal patterns of neuropathology for 1 month after acute intoxication with diisopropylfluorophosphate (DFP). Adult male Sprague Dawley rats administered pyridostigmine bromide (0.1 mg/kg, im) 30 min prior to successive administration of DFP (4 mg/kg, sc), atropine sulfate (2 mg/kg, im), and 2-pralidoxime (25 mg/kg, im) exhibited moderate-to-severe seizure behavior. T2-weighted and diffusion-weighted MR imaging prior to DFP exposure and at 3, 7, 14, 21, or 28 days postexposure revealed prominent lesions, tissue atrophy, and ventricular enlargement in discrete brain regions. Lesions varied in intensity and/or extent over time, with the overall magnitude of injury strongly influenced by seizure severity. Importantly, lesions detected by MRI correlated spatially and temporally with histological evidence of brain pathology. Analysis of histogram parameters extracted from frequency distributions of regional apparent diffusion coefficient (ADC) values identified the standard deviation and 90th percentile of the ADC as robust metrics for quantifying persistent and progressive neuropathological changes. The interanimal and interregional variations observed in lesion severity and progression, coupled with potential reinjury following spontaneous recurrent seizures, underscore the advantages of using in vivo imaging to longitudinally monitor neuropathology and, ultimately, therapeutic response, following acute OP intoxication. PMID:28329842

Everolimus is better than rapamycin in attenuating neuroinflammation in kainic acid-induced seizures.

PubMed

Yang, Ming-Tao; Lin, Yi-Chin; Ho, Whae-Hong; Liu, Chao-Lin; Lee, Wang-Tso

2017-01-21

Microglia is responsible for neuroinflammation, which may aggravate brain injury in diseases like epilepsy. Mammalian target of rapamycin (mTOR) kinase is related to microglial activation with subsequent neuroinflammation. In the present study, rapamycin and everolimus, both as mTOR inhibitors, were investigated in models of kainic acid (KA)-induced seizure and lipopolysaccharide (LPS)-induced neuroinflammation. In vitro, we treated BV2 cells with KA and LPS. In vivo, KA was used to induce seizures on postnatal day 25 in B6.129P-Cx3cr1 tm1Litt /J mice. Rapamycin and everolimus were evaluated in their modulation of neuroinflammation detected by real-time PCR, Western blotting, and immunostaining. Everolimus was significantly more effective than rapamycin in inhibiting iNOS and mTOR signaling pathways in both models of neuroinflammation (LPS) and seizure (KA). Everolimus significantly attenuated the mRNA expression of iNOS by LPS and nitrite production by KA and LPS than that by rapamycin. Only everolimus attenuated the mRNA expression of mTOR by LPS and KA treatment. In the present study, we also found that the modulation of mTOR under LPS and KA treatment was not mediated by Akt pathway but was primarily mediated by ERK phosphorylation, which was more significantly attenuated by everolimus. This inhibition of ERK phosphorylation and microglial activation in the hippocampus by everolimus was also confirmed in KA-treated mice. Rapamycin and everolimus can block the activation of inflammation-related molecules and attenuated the microglial activation. Everolimus had better efficacy than rapamycin, possibly mediated by the inhibition of ERK phosphorylation. Taken together, mTOR inhibitor can be a potential pharmacological target of anti-inflammation and seizure treatment.

Postictal in situ MRS brain lactate in the rat kindling model.

PubMed

Maton, B M; Najm, I M; Wang, Y; Lüders, H O; Ng, T C

1999-12-10

To determine the temporal and spatial extent of the lactate (Lact) changes as correlated with seizure characteristics and EEG changes in the rat kindling model. Prior studies using MRS have detected cerebral Lact postictally in animal models of seizures and in patients with intractable focal epilepsy. We performed MRS in sham control rats (n = 4) and in rats stimulated in the right hippocampus at two different stages of the kindling and at three time points after the seizures: <2 hours (n = 8 and 5, stage 0 and stage 5), 2 to 3 hours (n = 5 and 6), and >3 hours (n = 4 and 2). Lact/creatine (Cr) and N-acetylaspartate (NAA)/Cr ratios were measured in six contiguous voxels (three left, three right) covering the hippocampi, anterior and posterior regions, and compared with EEG and ictal behavior. Lact/Cr ratios were measured at a very low level in the sham control rats and in the >3-hour group. In the <2-hour group, Lact/Cr increase was higher in stage-5 rats as compared with stage-0 rats (p = 0.001, unpaired t-test) and sham control rats when all the voxels were considered. Lact/Cr ratios were higher in the stimulated area as compared with all other brain areas in stage-0 rats (p = 0.05, paired t-test) but not in the stage-5 rats. Similar results with more inter-animal variability were measured in the 2- to 3-hour group. NAA/Cr ratios increased significantly after stage-0 kindling in the stimulated hippocampus but not after stage-5 kindling. Postictal Lact increase as assayed by MRS correlates with EEG and behavioral seizures and suggests that it would be an additional noninvasive technique for seizure localization during the presurgical evaluation of patients with intractable focal epilepsy.

GC-MS-Based metabolomics discovers a shared serum metabolic characteristic among three types of epileptic seizures.

PubMed

Wang, Dian; Wang, Xingxing; Kong, Jing; Wu, Jiayan; Lai, Minchao

2016-10-01

Understanding the overall and common metabolic changes of seizures can provide novel clues for their control and prevention. Here, we aim to investigate the global metabolic feature of serum for three types of seizures. We recruited 27 patients who had experienced a seizure within 48h (including 11 who had a generalized seizure, nine who had a generalized seizure secondary to partial seizure and seven who had a partial seizure) and 23 healthy controls. We analyzed the global metabolic changes of serum after seizures using gas chromatography-mass spectrometry-based metabolomics. Based on differential metabolites, the metabolic pathways and their potential to diagnose seizures were analyzed, and metabolic differences among three types of seizures were compared. The metabolic profiles of serum were distinctive between the seizure group and the controls but were not different among the three types of seizures. Compared to the controls, patients with seizures had higher levels of lactate, butanoic acid, proline and glutamate and lower levels of palmitic acid, linoleic acid, elaidic acid, trans-13-octadecenoic acid, stearic acid, citrate, cysteine, glutamine, asparagine, and glyceraldehyde in the serum. Furthermore, these differential metabolites had common change trends among the three types of seizures. Related pathophysiological processes reflected by these metabolites are energy deficit, inflammation, nervous excitation and neurotoxicity. Importantly, transamination inhibition is suspected to occur in seizures. Lactate, glyceraldehyde and trans-13-octadecenoic acid in serum jointly enabled a precision of 92.9% for diagnosing seizures. There is a common metabolic feature in three types of seizures. Lactate, glyceraldehyde and trans-13-octadecenoic acid levels jointly enable high-precision seizure diagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.

A new class of anticonvulsants possessing 6 Hz psychomotor seizure test activity: 2-(1H-benzotriazol-1-yl)-N'-[substituted] acetohydrazides.

PubMed

Kumar, Praveen; Tripathi, Laxmi

2012-05-01

A series of 2-(1H-Benzotriazol-1-yl)-N'-[substituted]acetohydrazides were designed & synthesized keeping in view the structural requirement of pharmacophore and evaluated for anticonvulsant activity and neurotoxicity. The new compounds were characterized using FT-IR, 1H NMR, mass spectral data and elemental analysis. The anticonvulsant activity of the titled compounds was assessed using the 6 Hz psychomotor seizure test. The neurotoxicity was assessed using the rotorod method. The most active compound of the series was N'-[4-(1,3-Benzodioxol-5-yloxy)benzylidene]-2-(1H-benzotriazol-1-yl)acetohydrazide (BTA 9), which showed good activity with 75 % protection (3/4, 0.5 h) at a dose of 100 mg/kg in mice. All the compounds exhibited no neurotoxicity. A computational study was carried out for calculation of pharmacophore pattern and prediction of pharmacokinetic properties. Titled compounds have also exhibited good binding properties with epilepsy molecular targets such as glutamate, GABA (A) delta, GABA (A) alpha-1 receptors and Na/H exchanger, in Lamarckian genetic algorithm based flexible docking studies.

Nuclear, chloroplast, and mitochondrial data of a US cannabis DNA database.

PubMed

Houston, Rachel; Birck, Matthew; LaRue, Bobby; Hughes-Stamm, Sheree; Gangitano, David

2018-05-01

As Cannabis sativa (marijuana) is a controlled substance in many parts of the world, the ability to track biogeographical origin of cannabis could provide law enforcement with investigative leads regarding its trade and distribution. Population substructure and inbreeding may cause cannabis plants to become more genetically related. This genetic relatedness can be helpful for intelligence purposes. Analysis of autosomal, chloroplast, and mitochondrial DNA allows for not only prediction of biogeographical origin of a plant but also discrimination between individual plants. A previously validated, 13-autosomal STR multiplex was used to genotype 510 samples. Samples were analyzed from four different sites: 21 seizures at the US-Mexico border, Northeastern Brazil, hemp seeds purchased in the US, and the Araucania area of Chile. In addition, a previously reported multi-loci system was modified and optimized to genotype five chloroplast and two mitochondrial markers. For this purpose, two methods were designed: a homopolymeric STR pentaplex and a SNP triplex with one chloroplast (Cscp001) marker shared by both methods for quality control. For successful mitochondrial and chloroplast typing, a novel real-time PCR quantitation method was developed and validated to accurately estimate the quantity of the chloroplast DNA (cpDNA) using a synthetic DNA standard. Moreover, a sequenced allelic ladder was also designed for accurate genotyping of the homopolymeric STR pentaplex. For autosomal typing, 356 unique profiles were generated from the 425 samples that yielded full STR profiles and 25 identical genotypes within seizures were observed. Phylogenetic analysis and case-to-case pairwise comparisons of 21 seizures at the US-Mexico border, using the Fixation Index (F ST ) as genetic distance, revealed the genetic association of nine seizures that formed a reference population. For mitochondrial and chloroplast typing, subsampling was performed, and 134 samples were genotyped. Complete haplotypes (STRs and SNPs) were observed for 127 samples. As expected, extensive haplotype sharing was observed; five distinguishable haplotypes were detected. In the reference population, the same haplotype was observed 39 times and two unique haplotypes were also detected. Haplotype sharing was observed between the US border seizures, Brazil, and Chile, while the hemp samples generated a distinct haplotype. Phylogenetic analysis of the four populations was performed, and results revealed that both autosomal and lineage markers could discern population substructure.

Improved target detection algorithm using Fukunaga-Koontz transform and distance classifier correlation filter

NASA Astrophysics Data System (ADS)

Bal, A.; Alam, M. S.; Aslan, M. S.

2006-05-01

Often sensor ego-motion or fast target movement causes the target to temporarily go out of the field-of-view leading to reappearing target detection problem in target tracking applications. Since the target goes out of the current frame and reenters at a later frame, the reentering location and variations in rotation, scale, and other 3D orientations of the target are not known thus complicating the detection algorithm has been developed using Fukunaga-Koontz Transform (FKT) and distance classifier correlation filter (DCCF). The detection algorithm uses target and background information, extracted from training samples, to detect possible candidate target images. The detected candidate target images are then introduced into the second algorithm, DCCF, called clutter rejection module, to determine the target coordinates are detected and tracking algorithm is initiated. The performance of the proposed FKT-DCCF based target detection algorithm has been tested using real-world forward looking infrared (FLIR) video sequences.

Adaboost multi-view face detection based on YCgCr skin color model

NASA Astrophysics Data System (ADS)

Lan, Qi; Xu, Zhiyong

2016-09-01

Traditional Adaboost face detection algorithm uses Haar-like features training face classifiers, whose detection error rate is low in the face region. While under the complex background, the classifiers will make wrong detection easily to the background regions with the similar faces gray level distribution, which leads to the error detection rate of traditional Adaboost algorithm is high. As one of the most important features of a face, skin in YCgCr color space has good clustering. We can fast exclude the non-face areas through the skin color model. Therefore, combining with the advantages of the Adaboost algorithm and skin color detection algorithm, this paper proposes Adaboost face detection algorithm method that bases on YCgCr skin color model. Experiments show that, compared with traditional algorithm, the method we proposed has improved significantly in the detection accuracy and errors.

Sensitivity and specificity of memory and naming tests for identifying left temporal-lobe epilepsy.

PubMed

Umfleet, Laura Glass; Janecek, Julie K; Quasney, Erin; Sabsevitz, David S; Ryan, Joseph J; Binder, Jeffrey R; Swanson, Sara J

2015-01-01

The sensitivity and specificity of the Selective Reminding Test (SRT) Delayed Recall, Wechsler Memory Scale (WMS) Logical Memory, the Boston Naming Test (BNT), and two nonverbal memory measures for detecting lateralized dysfunction in association with side of seizure focus was examined in a sample of 143 patients with left or right temporal-lobe epilepsy (TLE). Scores on the SRT and BNT were statistically significantly lower in the left TLE group compared with the right TLE group, whereas no group differences emerged on the Logical Memory subtest. No significant group differences were found with nonverbal memory measures. When the SRT and BNT were both entered as predictors in a logistic regression, the BNT, although significant, added minimal value to the model beyond the variance accounted for by the SRT Delayed Recall. Both variables emerged as significant predictors of side of seizure focus when entered into separate regressions. Sensitivity and specificity of the SRT and BNT ranged from 56% to 65%. The WMS Logical Memory and nonverbal memory measures were not significant predictors of the side of seizure focus.

Elevated expression of pleiotrophin in pilocarpine-induced seizures of immature rats and in pentylenetetrazole-induced hippocampal astrocytes in vitro.

PubMed

Zhang, Shuqin; Liang, Feng; Wang, Bing; Le, Yuan; Wang, Hua

2014-03-01

Pleiotrophin (PTN) is a secreted extracellular matrix (ECM)-associated cytokine that has emerged as an important neuromodulator with multiple neuronal functions. In the present study, we detected and compared the dynamic expression of PTN in the hippocampus and adjacent cortex of immature rats with pilocarpine-induced epilepsy. Moreover, we also confirmed the results by examining PTN expression in hippocampal astrocytes cultured in the presence of pentylenetetrazole (PTZ). Immunohistochemistry showed faint immunostaining of PTN in the control hippocampus and adjacent cortex. Notably, PTN immunoreactivity began to increase in relatively small cells in the hippocampus and adjacent cortex at 2h and 3 weeks after seizures, and the labeling intensity reached the maximum level in the hippocampus and adjacent cortex at 8 weeks after seizures. Furthermore, we also found that PTZ treatment significantly reduced astrocytic viability in a dose-dependent manner and time-dependently increased expression levels of PTN in hippocampal astrocytes. In conclusion, our data suggest that increased expression of PTN in the brain tissues may be involved in epileptogenesis. Copyright © 2013 Elsevier GmbH. All rights reserved.

Brain State Is a Major Factor in Preseizure Hippocampal Network Activity and Influences Success of Seizure Intervention

PubMed Central

Ewell, Laura A.; Liang, Liang; Armstrong, Caren; Soltész, Ivan; Leutgeb, Stefan

2015-01-01

Neural dynamics preceding seizures are of interest because they may shed light on mechanisms of seizure generation and could be predictive. In healthy animals, hippocampal network activity is shaped by behavioral brain state and, in epilepsy, seizures selectively emerge during specific brain states. To determine the degree to which changes in network dynamics before seizure are pathological or reflect ongoing fluctuations in brain state, dorsal hippocampal neurons were recorded during spontaneous seizures in a rat model of temporal lobe epilepsy. Seizures emerged from all brain states, but with a greater likelihood after REM sleep, potentially due to an observed increase in baseline excitability during periods of REM compared with other brains states also characterized by sustained theta oscillations. When comparing the firing patterns of the same neurons across brain states associated with and without seizures, activity dynamics before seizures followed patterns typical of the ongoing brain state, or brain state transitions, and did not differ until the onset of the electrographic seizure. Next, we tested whether disparate activity patterns during distinct brain states would influence the effectiveness of optogenetic curtailment of hippocampal seizures in a mouse model of temporal lobe epilepsy. Optogenetic curtailment was significantly more effective for seizures preceded by non-theta states compared with seizures that emerged from theta states. Our results indicate that consideration of behavioral brain state preceding a seizure is important for the appropriate interpretation of network dynamics leading up to a seizure and for designing effective seizure intervention. SIGNIFICANCE STATEMENT Hippocampal single-unit activity is strongly shaped by behavioral brain state, yet this relationship has been largely ignored when studying activity dynamics before spontaneous seizures in medial temporal lobe epilepsy. In light of the increased attention on using single-unit activity for the prediction of seizure onset and closed-loop seizure intervention, we show a need for monitoring brain state to interpret correctly whether changes in neural activity before seizure onset is pathological or normal. Moreover, we also find that the brain state preceding a seizure determines the success of therapeutic interventions to curtail seizure duration. Together, these findings suggest that seizure prediction and intervention will be more successful if tailored for the specific brain states from which seizures emerge. PMID:26609157

Effect of Immunotherapy on Seizure Outcome in Patients with Autoimmune Encephalitis: A Prospective Observational Registry Study

PubMed Central

Jung, Keun-Hwa; Sunwoo, Jun-Sang; Moon, Jangsup; Lim, Jung-Ah; Lee, Doo Young; Shin, Yong-Won; Kim, Tae-Joon; Lee, Keon-Joo; Lee, Woo-Jin; Lee, Han-Sang; Jun, Jinsun; Kim, Dong-Yub; Kim, Man-Young; Kim, Hyunjin; Kim, Hyeon Jin; Suh, Hong Il; Lee, Yoojin; Kim, Dong Wook; Jeong, Jin Ho; Choi, Woo Chan; Bae, Dae Woong; Shin, Jung-Won; Jeon, Daejong; Park, Kyung-Il; Jung, Ki-Young; Chu, Kon; Lee, Sang Kun

2016-01-01

Objective To evaluate the seizure characteristics and outcome after immunotherapy in adult patients with autoimmune encephalitis (AE) and new-onset seizure. Methods Adult (age ≥18 years) patients with AE and new-onset seizure who underwent immunotherapy and were followed-up for at least 6 months were included. Seizure frequency was evaluated at 2–4 weeks and 6 months after the onset of the initial immunotherapy and was categorized as “seizure remission”, “> 50% seizure reduction”, or “no change” based on the degree of its decrease. Results Forty-one AE patients who presented with new-onset seizure were analysed. At 2–4 weeks after the initial immunotherapy, 51.2% of the patients were seizure free, and 24.4% had significant seizure reduction. At 6 months, seizure remission was observed in 73.2% of the patients, although four patients died during hospitalization. Rituximab was used as a second-line immunotherapy in 12 patients who continued to have seizures despite the initial immunotherapy, and additional seizure remission was achieved in 66.6% of them. In particular, those who exhibited partial response to the initial immunotherapy had a better seizure outcome after rituximab, with low adverse events. Conclusion AE frequently presented as seizure, but only 18.9% of the living patients suffered from seizure at 6 months after immunotherapy. Aggressive immunotherapy can improve seizure outcome in patients with AE. PMID:26771547

Application and Evaluation of Independent Component Analysis Methods to Generalized Seizure Disorder Activities Exhibited in the Brain.

PubMed

George, S Thomas; Balakrishnan, R; Johnson, J Stanly; Jayakumar, J

2017-07-01

EEG records the spontaneous electrical activity of the brain using multiple electrodes placed on the scalp, and it provides a wealth of information related to the functions of brain. Nevertheless, the signals from the electrodes cannot be directly applied to a diagnostic tool like brain mapping as they undergo a "mixing" process because of the volume conduction effect in the scalp. A pervasive problem in neuroscience is determining which regions of the brain are active, given voltage measurements at the scalp. Because of which, there has been a surge of interest among the biosignal processing community to investigate the process of mixing and unmixing to identify the underlying active sources. According to the assumptions of independent component analysis (ICA) algorithms, the resultant mixture obtained from the scalp can be closely approximated by a linear combination of the "actual" EEG signals emanating from the underlying sources of electrical activity in the brain. As a consequence, using these well-known ICA techniques in preprocessing of the EEG signals prior to clinical applications could result in development of diagnostic tool like quantitative EEG which in turn can assist the neurologists to gain noninvasive access to patient-specific cortical activity, which helps in treating neuropathologies like seizure disorders. The popular and proven ICA schemes mentioned in various literature and applications were selected (which includes Infomax, JADE, and SOBI) and applied on generalized seizure disorder samples using EEGLAB toolbox in MATLAB environment to see their usefulness in source separations; and they were validated by the expert neurologist for clinical relevance in terms of pathologies on brain functionalities. The performance of Infomax method was found to be superior when compared with other ICA schemes applied on EEG and it has been established based on the validations carried by expert neurologist for generalized seizure and its clinical correlation. The results are encouraging for furthering the studies in the direction of developing useful brain mapping tools using ICA methods.

Automated Identification of Abnormal Adult EEGs

PubMed Central

López, S.; Suarez, G.; Jungreis, D.; Obeid, I.; Picone, J.

2016-01-01

The interpretation of electroencephalograms (EEGs) is a process that is still dependent on the subjective analysis of the examiners. Though interrater agreement on critical events such as seizures is high, it is much lower on subtler events (e.g., when there are benign variants). The process used by an expert to interpret an EEG is quite subjective and hard to replicate by machine. The performance of machine learning technology is far from human performance. We have been developing an interpretation system, AutoEEG, with a goal of exceeding human performance on this task. In this work, we are focusing on one of the early decisions made in this process – whether an EEG is normal or abnormal. We explore two baseline classification algorithms: k-Nearest Neighbor (kNN) and Random Forest Ensemble Learning (RF). A subset of the TUH EEG Corpus was used to evaluate performance. Principal Components Analysis (PCA) was used to reduce the dimensionality of the data. kNN achieved a 41.8% detection error rate while RF achieved an error rate of 31.7%. These error rates are significantly lower than those obtained by random guessing based on priors (49.5%). The majority of the errors were related to misclassification of normal EEGs. PMID:27195311

A false-alarm aware methodology to develop robust and efficient multi-scale infrared small target detection algorithm

NASA Astrophysics Data System (ADS)

Moradi, Saed; Moallem, Payman; Sabahi, Mohamad Farzan

2018-03-01

False alarm rate and detection rate are still two contradictory metrics for infrared small target detection in an infrared search and track system (IRST), despite the development of new detection algorithms. In certain circumstances, not detecting true targets is more tolerable than detecting false items as true targets. Hence, considering background clutter and detector noise as the sources of the false alarm in an IRST system, in this paper, a false alarm aware methodology is presented to reduce false alarm rate while the detection rate remains undegraded. To this end, advantages and disadvantages of each detection algorithm are investigated and the sources of the false alarms are determined. Two target detection algorithms having independent false alarm sources are chosen in a way that the disadvantages of the one algorithm can be compensated by the advantages of the other one. In this work, multi-scale average absolute gray difference (AAGD) and Laplacian of point spread function (LoPSF) are utilized as the cornerstones of the desired algorithm of the proposed methodology. After presenting a conceptual model for the desired algorithm, it is implemented through the most straightforward mechanism. The desired algorithm effectively suppresses background clutter and eliminates detector noise. Also, since the input images are processed through just four different scales, the desired algorithm has good capability for real-time implementation. Simulation results in term of signal to clutter ratio and background suppression factor on real and simulated images prove the effectiveness and the performance of the proposed methodology. Since the desired algorithm was developed based on independent false alarm sources, our proposed methodology is expandable to any pair of detection algorithms which have different false alarm sources.

Morphine potentiates seizures induced by GABA antagonists and attenuates seizures induced by electroshock in the rat.

PubMed

Foote, F; Gale, K

1983-11-25

In a naloxone-reversible, dose-dependent manner, morphine (10-50 mg/kg i.p.) protected against seizures induced by maximal electroshock and increased the incidence and severity of seizures induced by bicuculline, in rats. Morphine also potentiated seizures induced by isoniazid and by picrotoxin. Thus, opiate activity influences the expression of seizures in contrasting ways depending upon the mode of seizure induction. Since morphine consistently potentiated seizures induced by interference with GABA transmission, it appears that GABAergic systems may be of particular significance for the elucidation of the varied effects of morphine on seizure susceptibility.

Prediction of secondary generalization from a focal onset seizure in intracerebral EEG.

PubMed

Karthick, P A; Tanaka, Hideaki; Khoo, Hui Ming; Gotman, Jean

2018-05-01

We propose a system based on the first five seconds of intracerebrally recorded focal seizures to predict their evolution from focal to bilateral tonic-clonic seizure (FTC), to spread outside the onset zone but without tonic-clonic component (FS), or to a seizure remaining focal (F). Nineteen time and frequency domain features were extracted from 39 seizures of 32 patients and were subjected to support vector machine based classification. Three prediction approaches regarding seizure evolution were (1) FTC vs FS vs F, (2) FTC vs FS or F and (3) FTC or FS vs F. We used 21 seizures for training. Most features had significantly different distributions in the three seizure types (p < 0.05). Eighteen seizures were used for testing. We predicted the evolution of 12 seizures in FTC vs FS vs F, 13 seizures in FTC vs FS or F and 14 seizures in FTC or FS vs F. The first five seconds of a focal seizure contains information regarding the eventual evolution of the seizure, which could be predicted in most seizures. The proposed system could alert the health care team when a patient is hospitalized for intracerebral EEG and improve safety and eventually result in an implantable device. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Seizure characteristics of epilepsy in childhood after acute encephalopathy with biphasic seizures and late reduced diffusion.

PubMed

Ito, Yuji; Natsume, Jun; Kidokoro, Hiroyuki; Ishihara, Naoko; Azuma, Yoshiteru; Tsuji, Takeshi; Okumura, Akihisa; Kubota, Tetsuo; Ando, Naoki; Saitoh, Shinji; Miura, Kiyokuni; Negoro, Tamiko; Watanabe, Kazuyoshi; Kojima, Seiji

2015-08-01

The aim of this study was to clarify characteristics of post-encephalopathic epilepsy (PEE) in children after acute encephalopathy with biphasic seizures and late reduced diffusion (AESD), paying particular attention to precise diagnosis of seizure types. Among 262 children with acute encephalopathy/encephalitis registered in a database of the Tokai Pediatric Neurology Society between 2005 and 2012, 44 were diagnosed with AESD according to the clinical course and magnetic resonance imaging (MRI) findings and were included in this study. Medical records were reviewed to investigate clinical data, MRI findings, neurologic outcomes, and presence or absence of PEE. Seizure types of PEE were determined by both clinical observation by pediatric neurologists and ictal video-electroencephalography (EEG) recordings. Of the 44 patients after AESD, 10 (23%) had PEE. The period between the onset of encephalopathy and PEE ranged from 2 to 39 months (median 8.5 months). Cognitive impairment was more severe in patients with PEE than in those without. Biphasic seizures and status epilepticus during the acute phase of encephalopathy did not influence the risk of PEE. The most common seizure type of PEE on clinical observation was focal seizures (n = 5), followed by epileptic spasms (n = 4), myoclonic seizures (n = 3), and tonic seizures (n = 2). In six patients with PEE, seizures were induced by sudden unexpected sounds. Seizure types confirmed by ictal video-EEG recordings were epileptic spasms and focal seizures with frontal onset, and all focal seizures were startle seizures induced by sudden acoustic stimulation. Intractable daily seizures remain in six patients with PEE. We demonstrate seizure characteristics of PEE in children after AESD. Epileptic spasms and startle focal seizures are common seizure types. The specific seizure types may be determined by the pattern of diffuse subcortical white matter injury in AESD and age-dependent reorganization of the brain network. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Breakthrough seizures—Further analysis of the Standard versus New Antiepileptic Drugs (SANAD) study

PubMed Central

Powell, Graham A.; Tudur Smith, Catrin; Marson, Anthony G.

2017-01-01

Objectives To develop prognostic models for risk of a breakthrough seizure, risk of seizure recurrence after a breakthrough seizure, and likelihood of achieving 12-month remission following a breakthrough seizure. A breakthrough seizure is one that occurs following at least 12 months remission whilst on treatment. Methods We analysed data from the SANAD study. This long-term randomised trial compared treatments for participants with newly diagnosed epilepsy. Multivariable Cox models investigated how clinical factors affect the probability of each outcome. Best fitting multivariable models were produced with variable reduction by Akaike’s Information Criterion. Risks associated with combinations of risk factors were calculated from each multivariable model. Results Significant factors in the multivariable model for risk of a breakthrough seizure following 12-month remission were number of tonic-clonic seizures by achievement of 12-month remission, time taken to achieve 12-month remission, and neurological insult. Significant factors in the model for risk of seizure recurrence following a breakthrough seizure were total number of drugs attempted to achieve 12-month remission, time to achieve 12-month remission prior to breakthrough seizure, and breakthrough seizure treatment decision. Significant factors in the model for likelihood of achieving 12-month remission after a breakthrough seizure were gender, age at breakthrough seizure, time to achieve 12-month remission prior to breakthrough, and breakthrough seizure treatment decision. Conclusions This is the first analysis to consider risk of a breakthrough seizure and subsequent outcomes. The described models can be used to identify people most likely to have a breakthrough seizure, a seizure recurrence following a breakthrough seizure, and to achieve 12-month remission following a breakthrough seizure. The results suggest that focussing on achieving 12-month remission swiftly represents the best therapeutic aim to reduce the risk of a breakthrough seizure and subsequent negative outcomes. This will aid individual patient risk stratification and the design of future epilepsy trials. PMID:29267375

NBQX, a highly selective competitive antagonist of AMPA and KA ionotropic glutamate receptors, increases seizures and mortality following picornavirus infection

PubMed Central

Libbey, Jane E.; Hanak, Tyler J.; Doty, Daniel J.; Wilcox, Karen S.; Fujinami, Robert S.

2016-01-01

Seizures occur due to an imbalance between excitation and inhibition, with the balance tipping towards excitation, and glutamate is the predominant excitatory neurotransmitter in the central nervous system of mammals. Since upregulation of expression and/or function of glutamate receptors can contribute to seizures we determined the effects of three antagonists, NBQX, GYKI-52466 and MK 801, of the various ionotropic glutamate receptors, AMPA, NMDA and KA, on acute seizure development in the Theiler’s murine encephalomyelitis virus (TMEV)-induced seizure model. We found that only NBQX had an effect on acute seizure development, resulting in a significantly higher number of mice experiencing seizures, an increase in the number of seizures per mouse, a greater cumulative seizure score per mouse and a significantly higher mortality rate among the mice. Although NBQX has previously been shown to be a potent anticonvulsant in animal seizure models, seizures induced by electrical stimulation, drug administration or as a result of genetic predisposition may differ greatly in terms of mechanism of seizure development from our virus-induced seizure model, which could explain the opposite, proconvulsant effect of NBQX observed in the TMEV-induced seizure model. PMID:27072529

Termination Patterns of Complex Partial Seizures: An Intracranial EEG Study

PubMed Central

Afra, Pegah; Jouny, Christopher C.; Bergey, Gregory K.

2015-01-01

Purpose While seizure onset patterns have been the subject of many reports, there have been few studies of seizure termination. In this study we report the incidence of synchronous and asynchronous termination patterns of partial seizures recorded with intracranial arrays. Methods Data were collected from patients with intractable complex partial seizures undergoing presurgical evaluations with intracranial electrodes. Patients with seizures originating from mesial temporal and neocortical regions were grouped into three groups based on patterns of seizure termination: synchronous only (So), asynchronous only (Ao), or mixed (S/A, with both synchronous and asynchronous termination patterns). Results 88% of the patients in the MT group had seizures with a synchronous pattern of termination exclusively (38%) or mixed (50%). 82% of the NC group had seizures with synchronous pattern of termination exclusively (52%) or mixed (30%). In the NC group, there was a significant difference of the range of seizure durations between So and Ao groups, with Ao exhibiting higher variability. Seizures with synchronous termination had low variability in both groups. Conclusions Synchronous seizure termination is a common pattern for complex partial seizures of both mesial temporal or neocortical onset. This may reflect stereotyped network behavior or dynamics at the seizure focus. PMID:26552555

Predictors of seizure occurrence in children undergoing pre-surgical monitoring.

PubMed

Harini, Chellamani; Singh, Kanwaljit; Takeoka, Masanori; Parulkar, Isha; Bergin, Ann Marie; Loddenkemper, Tobias; Kothare, Sanjeev V

2013-10-01

Long-Term-Monitoring (LTM) is a valuable tool for seizure localization/lateralization among children with refractory-epilepsy undergoing pre-surgical-monitoring. The aim of this study was to examine the factors predicting occurrence of single/multiple seizures in children undergoing pre-surgical monitoring in the LTM unit. Chart review was done on 95 consecutive admissions on 92 children (40 females) admitted to the LTM-unit for pre-surgical workup. Relationship between occurrence of multiple (≥ 3) seizures and factors such as home seizure-frequency, demographics, MRI-lesions/seizure-type and localization/AED usage/neurological-exam/epilepsy-duration was evaluated by logistic-regression and survival-analysis. Home seizure-frequency was further categorized into low (up-to 1/month), medium (up-to 1/week) and high (>1/week) and relationship of these categories to the occurrence of multiple seizures was evaluated. Mean length of stay was 5.24 days in all 3 groups. Home seizure frequency was the only factor predicting the occurrence of single/multiple seizures in children undergoing presurgical workup. Other factors (age/sex/MRI-lesions/seizure-type and localization/AED-usage/neurological-exam/epilepsy-duration) did not affect occurrence of single/multiple seizures or time-to-occurrence of first/second seizure. Analysis of the home-seizure frequency categories revealed that 98% admissions in high-frequency, 94% in the medium, and 77% in low-frequency group had at-least 1 seizure recorded during the monitoring. Odds of first-seizure increased in high vs. low-frequency group (p=0.01). Eighty-nine percent admissions in high-frequency, 78% in medium frequency, versus 50% in low-frequency group had ≥ 3 seizures. The odds of having ≥ 3 seizures increased in high-frequency (p=0.0005) and in medium-frequency (p=0.007), compared to low-frequency group. Mean time-to-first-seizure was 2.7 days in low-frequency, 2.1 days in medium, and 2 days in high-frequency group. Time-to-first-seizure in high and medium-frequency was less than in low-frequency group (p<0.0014 and p=0.038). Majority of the admissions (92%) admitted to the LTM-unit for pre-surgical workup had at-least one seizure during a mean length of stay of 5.24 days. Home seizure-frequency was the only predictor influencing occurrence of single/multiple seizures in the LTM unit. Patients with low seizure-frequency are at risk for completing the monitoring with less than the optimum number (<3) of seizures captured. Copyright © 2013 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Screening EEG in Aircrew Selection: Clinical Aerospace Neurology Perspective

NASA Technical Reports Server (NTRS)

Clark, Jonathan B.; Riley, Terrence

2001-01-01

As clinical aerospace neurologists we do not favor using screening EEG in pilot selection on unselected and otherwise asymptomatic individuals. The role of EEG in aviation screening should be as an adjunct to diagnosis, and the decision to disqualify a pilot should never be based solely on the EEG. Although a policy of using a screening EEG in an unselected population might detect an individual with a potentially increased relative risk, it would needlessly exclude many applicants who would probably never have a seizure. A diagnostic test performed on an asymptomatic individual without clinical indications, in a population with a low prevalence of disease (seizure) may be of limited or possibly detrimental value. We feel that rather than do EEGs on all candidates, a better approach would be to perform an EEG for a specific indication, such as family history of seizure, single convulsion (seizure) , history of unexplained loss of consciousness or head injury. Routine screening EEGs in unselected aviation applications are not done without clinical indication in the U.S. Air Force, Navy, or NASA. The USAF discontinued routine screening EEGs for selection in 1978, the U.S. Navy discontinued it in 1981 , and NASA discontinued it in 1995. EEG as an aeromedical screening tool in the US Navy dates back to 1939. The US Navy routinely used EEGs to screen all aeromedical personnel from 1961 to 1981. The incidence of epileptiform activity on EEG in asymptomatic flight candidates ranges from 0.11 to 2.5%. In 3 studies of asymptomatic flight candidates with epileptiform activity on EEG followed for 2 to 15 years, 1 of 31 (3.2%), 1 of 30 (3.3%), and 0 of 14 (0%) developed a seizure, for a cumulative risk of an individual with an epileptiform EEG developing a seizure of 2.67% (2 in 75). Of 28,658 student naval aviation personnel screened 31 had spikes and/or slow waves on EEG, and only 1 later developed a seizure. Of the 28,627 who had a normal EEG, 4 later developed seizures, or .0139% (4/28627). After review of the value of the EEG as a screening tool, the US Navy now uses EEG only for certain clinical indications (head injury, unexplained loss of consciousness, family history of epilepsy, and abnormal neurological exam). Currently the US Navy does not use EEG for screening for any flight applicant without a neurologic indication. In the US Navy, an electroencephalographic pattern is determined to be epileptiform by a neurologist.

Seizure Termination by Acidosis Depends on ASIC1a

PubMed Central

Ziemann, Adam E.; Schnizler, Mikael K.; Albert, Gregory W.; Severson, Meryl A.; Howard, Matthew A.; Welsh, Michael J.; Wemmie, John A.

2008-01-01

SUMMARY Most seizures stop spontaneously. However, the molecular mechanisms remain unknown. Earlier observations that seizures reduce brain pH and that acidosis inhibits seizures indicated that acidosis halts epileptic activity. Because acid–sensing ion channel–1a (ASIC1a) shows exquisite sensitivity to extracellular pH and regulates neuron excitability, we hypothesized that acidosis might activate ASIC1a to terminate seizures. Disrupting mouse ASIC1a increased the severity of chemoconvulsant–induced seizures, whereas overexpressing ASIC1a had the opposite effect. ASIC1a did not affect seizure threshold or onset, but shortened seizure duration and prevented progression. CO2 inhalation, long known to lower brain pH and inhibit seizures, also required ASIC1a to interrupt tonic–clonic seizures. Acidosis activated inhibitory interneurons through ASIC1a, suggesting that ASIC1a might limit seizures by increasing inhibitory tone. These findings identify ASIC1a as a key element in seizure termination when brain pH falls. The results suggest a molecular mechanism for how the brain stops seizures and suggest new therapeutic strategies. PMID:18536711

Seizure Freedom in Children With Pathology-Confirmed Focal Cortical Dysplasia.

PubMed

Mrelashvili, Anna; Witte, Robert J; Wirrell, Elaine C; Nickels, Katherine C; Wong-Kisiel, Lily C

2015-12-01

We evaluated the temporal course of seizure outcome in children with pathology-confirmed focal cortical dysplasia and explored predictors of sustained seizure freedom. We performed a single-center retrospective study of children ≤ 18 years who underwent resective surgery from January 1, 2000 through December 31, 2012 and had pathology-proven focal cortical dysplasia. Surgical outcome was classified as seizure freedom (Engel class I) or seizure recurrence (Engel classes II-IV). Fisher exact and nonparametric Wilcoxon ranksum tests were used, as appropriate. Survival analysis was based on seizure-free outcome. Patients were censored at the time of seizure recurrence or seizure freedom at last follow-up. Thirty-eight patients were identified (median age at surgery, 6.5 years; median duration of epilepsy, 3.3 years). Median time to last follow-up was 13.5 months (interquartile range, 7-41 months). Twenty patients (53%) were seizure free and 26 patients (68%) attained seizure freedom for a minimum of 3 months. Median time to seizure recurrence was 38 months (95% confidence interval, 6-109 months), and the cumulative seizure-free rate was 60% at 12 months (95% confidence interval, 43%-77%). Clinical features associated with seizure freedom at last follow-up included older age at seizure onset (P = .02), older age at surgery (P = .04), absent to mild intellectual disability before surgery (P = .05), and seizure freedom for a minimum of 3 months (P < .001). Favorable clinical features associated with sustained seizure freedom included older age at seizure onset, older age at surgery, absent or mild intellectual disability at baseline, and seizure freedom for a minimum of 3 months. Copyright © 2015 Elsevier Inc. All rights reserved.

Parent and caregiver knowledge, beliefs, and responses to convulsive seizures in children in Kingston, Jamaica - A hospital-based survey.

PubMed

Hall-Parkinson, Debra; Tapper, Judy; Melbourne-Chambers, Roxanne

2015-10-01

The objective of this study was to determine the knowledge and beliefs about seizures and actions during seizures of parents/caregivers of Jamaican children hospitalized for convulsive seizures. This was a cross-sectional study of parents and caregivers of children with acute convulsive seizures hospitalized at the Bustamante Hospital, Kingston, Jamaica between May 1 and October 31, 2013. Subjects were identified by admission records. Parents/caregivers were invited to participate. A questionnaire on the demographics, knowledge, beliefs, and response of parents/caregivers during the child's current seizure episode was administered face to face. Data were analyzed for frequencies: groups were compared using chi-square analysis for categorical variables, Student's t-test for normally distributed data, and the Mann-Whitney U-test for data not normally distributed. There were fifty participants: 39 (78%) mothers, mean (SD) age - 33.8 (10.1) years. All sought medical care first. Twenty-two (44%) had plausible beliefs about the cause of seizures. Twenty-seven (54%) knew of appropriate actions during a seizure, 10 (20%) knew of appropriate precautions, and 11 (22%) responded appropriately during the seizure. Eleven (22%) reported receiving seizure education. Witnessing a previous seizure, education level, and seizure education were associated with knowledge of seizures (p<0.05). Socioeconomic status was higher in those with plausible beliefs about seizures and lower in those who took appropriate action during a seizure (p<0.05). Parents/caregivers of children with convulsive seizures have appropriate health-care seeking behavior, but most do not have appropriate knowledge about seizures. Few take appropriate action during the episode. A public education program is needed to improve parental knowledge of and response to convulsive seizures. Copyright © 2015 Elsevier Inc. All rights reserved.

Clustering of spontaneous recurrent seizures separated by long seizure-free periods: An extended video-EEG monitoring study of a pilocarpine mouse model.

PubMed

Lim, Jung-Ah; Moon, Jangsup; Kim, Tae-Joon; Jun, Jin-Sun; Park, Byeongsu; Byun, Jung-Ick; Sunwoo, Jun-Sang; Park, Kyung-Il; Lee, Soon-Tae; Jung, Keun-Hwa; Jung, Ki-Young; Kim, Manho; Jeon, Daejong; Chu, Kon; Lee, Sang Kun

2018-01-01

Seizure clustering is a common and significant phenomenon in patients with epilepsy. The clustering of spontaneous recurrent seizures (SRSs) in animal models of epilepsy, including mouse pilocarpine models, has been reported. However, most studies have analyzed seizures for a short duration after the induction of status epilepticus (SE). In this study, we investigated the detailed characteristics of seizure clustering in the chronic stage of a mouse pilocarpine-induced epilepsy model for an extended duration by continuous 24/7 video-EEG monitoring. A seizure cluster was defined as the occurrence of one or more seizures per day for at least three consecutive days and at least five seizures during the cluster period. We analyzed the cluster duration, seizure-free period, cluster interval, and numbers of seizures within and outside the seizure clusters. The video-EEG monitoring began 84.5±33.7 days after the induction of SE and continued for 53.7±20.4 days. Every mouse displayed seizure clusters, and 97.0% of the seizures occurred within a cluster period. The seizure clusters were followed by long seizure-free periods of 16.3±6.8 days, showing a cyclic pattern. The SRSs also occurred in a grouped pattern within a day. We demonstrate that almost all seizures occur in clusters with a cyclic pattern in the chronic stage of a mouse pilocarpine-induced epilepsy model. The seizure-free periods between clusters were long. These findings should be considered when performing in vivo studies using this animal model. Furthermore, this model might be appropriate for studying the unrevealed mechanism of ictogenesis.

Are YouTube seizure videos misleading? Neurologists do not always agree.

PubMed

Brna, P M; Dooley, J M; Esser, M J; Perry, M S; Gordon, K E

2013-11-01

The internet has become the first stop for the public and patients to seek health-related information. Video-sharing websites are particularly important sources of information for those seeking answers about seizures and epilepsy. Because of the widespread popularity of YouTube, we sought to explore whether a seizure diagnosis and classification could reliably be applied. All videos related to "seizures" were reviewed, and irrelevant videos were excluded. The remaining 162 nonduplicate videos were analyzed by 4 independent pediatric neurologists who classified the events as epilepsy seizures, nonepileptic seizures, or indeterminate. Videos designated as epilepsy seizures were then classified into focal, generalized, or unclassified. At least 3 of the 4 reviewers agreed that 35% of the videos showed that the events were "epilepsy seizures", at least 3 of the 4 reviewers agreed that 28% of the videos demonstrated that the events were "nonepileptic seizures", and there was good agreement that 7% of the videos showed that the event was "indeterminate". Overall, interrater agreement was moderate at k=0.57 for epilepsy seizures and k=0.43 for nonepileptic seizures. For seizure classification, reviewer agreement was greatest for "generalized seizures" (k=0.45) and intermediate for "focal seizures" (k=0.27), and there was no agreement for unclassified events (k=0.026, p=0.2). Overall, neurology reviewer agreement suggests that only approximately one-third of the videos designated as "seizures" on the most popular video-sharing website, YouTube, definitely depict a seizure. Caution should be exercised in the use of such online video media for accessing educational or self-diagnosis aids for seizures. © 2013.

Neonatal seizures triple the risk of a remote seizure after perinatal ischemic stroke.

PubMed

Fox, Christine K; Glass, Hannah C; Sidney, Stephen; Smith, Sabrina E; Fullerton, Heather J

2016-06-07

To determine incidence rates and risk factors of remote seizure after perinatal arterial ischemic stroke. We retrospectively identified a population-based cohort of children with perinatal arterial ischemic stroke (presenting acutely or in a delayed fashion) from a large Northern Californian integrated health care system. We determined incidence and predictors of a remote seizure (unprovoked seizure after neonatal period, defined as 28 days of life) by survival analyses, and measured epilepsy severity in those with active epilepsy (≥1 remote seizure and maintenance anticonvulsant treatment) at last follow-up. Among 87 children with perinatal stroke, 40 (46%) had a seizure in the neonatal period. During a median follow-up of 7.1 years (interquartile range 3.2-10.5), 37 children had ≥1 remote seizure. Remote seizure risk was highest during the first year of life, with a 20% (95% confidence interval [CI] 13%-30%) cumulative incidence by 1 year of age, 46% (CI 35%-58%) by 5 years, and 54% (CI 41%-67%) by 10 years. Neonatal seizures increased the risk of a remote seizure (hazard ratio 2.8, CI 1.3-5.8). Children with neonatal seizures had a 69% (CI 48%-87%) cumulative incidence of remote seizure by age 10 years. Among the 24 children with active epilepsy at last follow-up, 8 (33%) were having monthly seizures despite an anticonvulsant and 7 (29%) were on more than one anticonvulsant. Remote seizures and epilepsy, including medically refractory epilepsy, are common after perinatal stroke. Neonatal seizures are associated with nearly 3-fold increased remote seizure risk. © 2016 American Academy of Neurology.

Counting seizures: The primary outcome measure in epileptology from the patients' perspective.

PubMed

Blachut, Barbara; Hoppe, Christian; Surges, Rainer; Stahl, Jutta; Elger, Christian E; Helmstaedter, Christoph

2015-07-01

Patient-reported seizure counts represent a key outcome measure for individual treatments and clinical studies in epileptology. Video-EEG based research, however, demonstrated lack of validity due to underreporting. Here we examined the practice of keeping seizure diaries and the patients' attitudes toward seizure counting. Anticipating a low return rate, a comprehensive survey was mailed to 1100 adult outpatients. Besides methods and reasons to document or not to document seizures, the questionnaire addressed clinical, personality and sociodemographic characteristics as well as the subjective experience of seizures. Questionnaires from 170 patients (15%) could be included in our analysis. Patients estimated to be aware of 5.3 out of 10 daytime seizures (nocturnal seizures: 2.6) while they supposed that relatives/colleagues noticed 7.1 (nocturnal: 4.6). Almost two-thirds of the patients reported to keep a seizure diary with a self-estimated documentation rate of 8.7 out of 10 noticed daytime seizures (nocturnal: 7.7). Documenters and non-documenters showed only marginal group differences with regard to clinical, personality and sociodemographic characteristics. Importantly, patients were more committed to keep a seizure diary when they judged it to be relevant for clinical treatment decisions. Patients appear to know that they underreport seizures. According to their view, seizure unawareness as induced by seizures themselves seems to be a more important factor than omitting documentation of noticed seizures. Thus, the potential to improve the validity of seizure diaries of electronic devices which facilitate documenting noticed seizures appears limited. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Predictability of uncontrollable multifocal seizures - towards new treatment options

NASA Astrophysics Data System (ADS)

Lehnertz, Klaus; Dickten, Henning; Porz, Stephan; Helmstaedter, Christoph; Elger, Christian E.

2016-04-01

Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage. An approach to control previously uncontrollable seizures in epilepsy patients would consist of identifying seizure precursors in critical brain areas combined with delivering a counteracting influence to prevent seizure generation. Predictability of seizures with acceptable levels of sensitivity and specificity, even in an ambulatory setting, has been repeatedly shown, however, in patients with a single seizure focus only. We did a study to assess feasibility of state-of-the-art, electroencephalogram-based seizure-prediction techniques in patients with uncontrollable multifocal seizures. We obtained significant predictive information about upcoming seizures in more than two thirds of patients. Unexpectedly, the emergence of seizure precursors was confined to non-affected brain areas. Our findings clearly indicate that epileptic networks, spanning lobes and hemispheres, underlie generation of seizures. Our proof-of-concept study is an important milestone towards new therapeutic strategies based on seizure-prediction techniques for clinical practice.

Absence seizure

MedlinePlus

Seizure - petit mal; Seizure - absence; Petit mal seizure; Epilepsy - absence seizure ... Abou-Khalil BW, Gallagher MJ, Macdonald RL. Epilepsies. In: Daroff ... Practice . 7th ed. Philadelphia, PA: Elsevier; 2016:chap 101. ...

Traditional and non-traditional treatments for autism spectrum disorder with seizures: an on-line survey

PubMed Central

2011-01-01

Background Despite the high prevalence of seizure, epilepsy and abnormal electroencephalograms in individuals with autism spectrum disorder (ASD), there is little information regarding the relative effectiveness of treatments for seizures in the ASD population. In order to determine the effectiveness of traditional and non-traditional treatments for improving seizures and influencing other clinical factor relevant to ASD, we developed a comprehensive on-line seizure survey. Methods Announcements (by email and websites) by ASD support groups asked parents of children with ASD to complete the on-line surveys. Survey responders choose one of two surveys to complete: a survey about treatments for individuals with ASD and clinical or subclinical seizures or abnormal electroencephalograms, or a control survey for individuals with ASD without clinical or subclinical seizures or abnormal electroencephalograms. Survey responders rated the perceived effect of traditional antiepileptic drug (AED), non-AED seizure treatments and non-traditional ASD treatments on seizures and other clinical factors (sleep, communication, behavior, attention and mood), and listed up to three treatment side effects. Results Responses were obtained concerning 733 children with seizures and 290 controls. In general, AEDs were perceived to improve seizures but worsened other clinical factors for children with clinical seizure. Valproic acid, lamotrigine, levetiracetam and ethosuximide were perceived to improve seizures the most and worsen other clinical factors the least out of all AEDs in children with clinical seizures. Traditional non-AED seizure and non-traditional treatments, as a group, were perceived to improve other clinical factors and seizures but the perceived improvement in seizures was significantly less than that reported for AEDs. Certain traditional non-AED treatments, particularly the ketogenic diet, were perceived to improve both seizures and other clinical factors. For ASD individuals with reported subclinical seizures, other clinical factors were reported to be worsened by AEDs and improved by non-AED traditional seizure and non-traditional treatments. The rate of side effects was reportedly higher for AEDs compared to traditional non-AED treatments. Conclusion Although this survey-based method only provides information regarding parental perceptions of effectiveness, this information may be helpful for selecting seizure treatments in individuals with ASD. PMID:21592359

Seizure outcome in 175 patients with juvenile myoclonic epilepsy--a long-term observational study.

PubMed

Höfler, Julia; Unterberger, Iris; Dobesberger, Judith; Kuchukhidze, Giorgi; Walser, Gerald; Trinka, Eugen

2014-12-01

Juvenile myoclonic epilepsy (JME) is a genetic generalized epilepsy syndrome. Under appropriate antiepileptic drugs (AED) up to 85% of patients become seizure-free, but many may have a relapse after AED withdrawal. We retrospectively studied 242 patients with JME at the Department of Neurology, Medical University Innsbruck, Austria (1975-2006). We analyzed age at seizure onset, age at last follow up, seizure types, photosensitivity, seizure outcome and neuroimaging findings; inclusion criterion was a medical treatment period of >2 years; exclusion criteria were traumatic or infectious brain injury before the onset of JME and/or gross structural pathology on neuroimaging. We identified 175 patients (111 women) with a median age at seizure onset of 15 years, (range 3-46) and a median age at follow-up (FU) of 38 years (range 14-87; median FU 8 years, range 2-38). Fourteen percent showed (24/175) photosensitivity on routine EEG. Seizure outcome: 62% (109/175) were seizure-free of myoclonic seizures (MS), generalized tonic clonic seizures (GTCS) and absence seizures (AS) for >1 year, and 53% (94/175) for >2 years, including 16 patients (9%) without AEDs. Thirty-one percent (54/175) were seizure-free between 2 and 5 years, 15% (26/175) between 6 and 10, and 8% (14/175) >10 years; 38% (66/175) were not seizure-free. Not seizure-free patients had more often MS, AS and GTCS within the first year of epilepsy than those who were seizure-free at last FU (11% vs. 3%, Chi(2)=4.679, df=1, p=0.043). Seizure-free patients had more often MS and GTCS as last seizure types in the year before becoming seizure-free (37% vs. 15%, p=0.003), whereas in not seizure-free group MS only and GTCS only persisted. JME does not always need lifelong treatment, as a substantial minority of patients remain seizure-free without AEDs. AS, MS and GTCS at onset of the disease are indicators of poor long-term seizure control. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Traditional and non-traditional treatments for autism spectrum disorder with seizures: an on-line survey.

PubMed

Frye, Richard E; Sreenivasula, Swapna; Adams, James B

2011-05-18

Despite the high prevalence of seizure, epilepsy and abnormal electroencephalograms in individuals with autism spectrum disorder (ASD), there is little information regarding the relative effectiveness of treatments for seizures in the ASD population. In order to determine the effectiveness of traditional and non-traditional treatments for improving seizures and influencing other clinical factor relevant to ASD, we developed a comprehensive on-line seizure survey. Announcements (by email and websites) by ASD support groups asked parents of children with ASD to complete the on-line surveys. Survey responders choose one of two surveys to complete: a survey about treatments for individuals with ASD and clinical or subclinical seizures or abnormal electroencephalograms, or a control survey for individuals with ASD without clinical or subclinical seizures or abnormal electroencephalograms. Survey responders rated the perceived effect of traditional antiepileptic drug (AED), non-AED seizure treatments and non-traditional ASD treatments on seizures and other clinical factors (sleep, communication, behavior, attention and mood), and listed up to three treatment side effects. Responses were obtained concerning 733 children with seizures and 290 controls. In general, AEDs were perceived to improve seizures but worsened other clinical factors for children with clinical seizure. Valproic acid, lamotrigine, levetiracetam and ethosuximide were perceived to improve seizures the most and worsen other clinical factors the least out of all AEDs in children with clinical seizures. Traditional non-AED seizure and non-traditional treatments, as a group, were perceived to improve other clinical factors and seizures but the perceived improvement in seizures was significantly less than that reported for AEDs. Certain traditional non-AED treatments, particularly the ketogenic diet, were perceived to improve both seizures and other clinical factors.For ASD individuals with reported subclinical seizures, other clinical factors were reported to be worsened by AEDs and improved by non-AED traditional seizure and non-traditional treatments. The rate of side effects was reportedly higher for AEDs compared to traditional non-AED treatments. Although this survey-based method only provides information regarding parental perceptions of effectiveness, this information may be helpful for selecting seizure treatments in individuals with ASD.

Partial (focal) seizure

MedlinePlus

... Jacksonian seizure; Seizure - partial (focal); Temporal lobe seizure; Epilepsy - partial seizures ... Abou-Khalil BW, Gallagher MJ, Macdonald RL. Epilepsies. In: Daroff ... Practice . 7th ed. Philadelphia, PA: Elsevier; 2016:chap 101. ...

Low-complexity R-peak detection in ECG signals: a preliminary step towards ambulatory fetal monitoring.

PubMed

Rooijakkers, Michiel; Rabotti, Chiara; Bennebroek, Martijn; van Meerbergen, Jef; Mischi, Massimo

2011-01-01

Non-invasive fetal health monitoring during pregnancy has become increasingly important. Recent advances in signal processing technology have enabled fetal monitoring during pregnancy, using abdominal ECG recordings. Ubiquitous ambulatory monitoring for continuous fetal health measurement is however still unfeasible due to the computational complexity of noise robust solutions. In this paper an ECG R-peak detection algorithm for ambulatory R-peak detection is proposed, as part of a fetal ECG detection algorithm. The proposed algorithm is optimized to reduce computational complexity, while increasing the R-peak detection quality compared to existing R-peak detection schemes. Validation of the algorithm is performed on two manually annotated datasets, the MIT/BIH Arrhythmia database and an in-house abdominal database. Both R-peak detection quality and computational complexity are compared to state-of-the-art algorithms as described in the literature. With a detection error rate of 0.22% and 0.12% on the MIT/BIH Arrhythmia and in-house databases, respectively, the quality of the proposed algorithm is comparable to the best state-of-the-art algorithms, at a reduced computational complexity.

The New Classification of Seizures by the International League Against Epilepsy 2017.

PubMed

Fisher, Robert S

2017-06-01

This review presents the newly developed International League Against Epilepsy (ILAE) 2017 classification of seizure types. The fundamental distinction is between seizures that begin focally in one hemisphere of the brain, generalized onset seizures that apparently originate in both hemispheres, and seizures of unknown onset. Focal seizures optionally can be subclassified according to whether awareness (a surrogate marker for consciousness) is intact or impaired. The next level of classification for focal seizures is motor (with subgroups automatisms, atonic, clonic, epileptic spasms, hyperkinetic, myoclonic, tonic), non-motor (with subgroups autonomic, behavior arrest, cognitive, emotional, sensory), and focal to bilateral tonic-clonic. Generalized seizures are categorized as motor (tonic-clonic, clonic, tonic, myoclonic, myoclonic-tonic-clonic, myoclonic-atonic, atonic, epileptic spasms) and non-motor/absence (typical, atypical, myoclonic, eyelid myoclonia). The classification allows new types of focal seizures and a few new generalized seizures, and clarifies terms used to name seizures.

Increasing Epilepsy Awareness in Schools: A Seizure Smart Schools Project.

PubMed

Brook, Heather A; Hiltz, Cynthia M; Kopplin, Vicki L; Lindeke, Linda L

2015-08-01

A high prevalence of epilepsy diagnoses and seizure events among students was identified at a large Midwestern school district. In partnership with the Epilepsy Foundation of Minnesota (EFMN), a quality improvement project was conducted to provide education and resources to staff caring for school children with seizures. School nurses (N = 26) were trained as seizure management educators and instructed staff in 21 schools on seizure awareness and response. School nurses utilized new seizure management resources, a procedural guideline, and care plan updates. The majority of school nurses rated the resources and training interventions as "very helpful." School nurse confidence in managing students with seizures increased, seizure action plan use increased, and 88% of children's records with new seizure diagnoses had completed documentation. School nurses played vital roles in increasing seizure awareness as educators and care managers. EFMN is using this project as an exemplar for expanding its Seizure Smart Schools program. © The Author(s) 2015.

Seizures and Epilepsy in Alzheimer’s Disease

PubMed Central

Friedman, Daniel; Honig, Lawrence S.; Scarmeas, Nikolaos

2013-01-01

Introduction Many studies have shown that patients with Alzheimer’s disease (AD) are at increased risk for developing seizures and epilepsy. However, reported prevalence and incidence of seizures and relationship of seizures to disease measures such as severity, outcome and progression vary widely between studies. Methods Literature review of the available clinical and epidemiological data on the topic of seizures in patients with AD. We review seizure rates and types, risk factors for seizures, electroencephalogram (EEG)studies, and treatment responses. Finally, we consider limitations and methodological issues. Results There is considerable variability in the reported prevalence and incidence of seizures in patients with AD - with reported lifetime prevalence rates of 1.5 - 64%. More recent, prospective, and larger studies in general report lower rates. Some, but not all, studies have noted increased seizure risk with increasing dementia severity or with younger age of AD onset. Generalized convulsive seizures are the most commonly reported type, but often historical information is the only basis used to determine seizure type and the manifestation of seizures may be difficult to distinguish from other behaviors common in demented patients. EEG has infrequently been performed and reported. Data on treatment of seizures in AD are extremely limited. Similarly, the relationship between seizures and cognitive impairment in AD is unclear. Conclusions The literature on seizures and epilepsy in AD, including diagnosis, risk factors, and response to treatment suffers from methodological limitations and gaps. PMID:22070283

Neurodevelopmental comorbidities and seizure control 24 months after a first unprovoked seizure in children.

PubMed

Jason, Eva Åndell; Tomson, Torbjörn; Carlsson, Sofia; Tedroff, Kristina; Åmark, Per

2018-07-01

To follow children with newly diagnosed unprovoked seizures to determine (1) whether the prevalence of neurodevelopmental comorbidities and cerebral palsy (CP) changed after the initial seizure, and (2) the association between studied comorbidities and seizures 13-24 months after seizure onset or initiation of treatment. Analyses were based on 750 children (28 days-18 years) with a first unprovoked seizure (index) included in a population-based Incidence Registry in Stockholm between 2001 and 2006. The children were followed for two years and their medical records were examined for a priori defined neurodevelopmental/psychiatric comorbidities and CP and seizure frequency. Baseline information was collected from medical records from before, and up to six months after, the index seizure. Odds ratios (OR) of repeated seizures 13-24 months after the first seizure or after initiation of anti-epileptic drug treatment was calculated by logistic regression and adjusted for age and sex. At baseline, 32% of the children had neurodevelopmental/psychiatric comorbidities or CP compared to 35%, 24 months later. Children with such comorbidities more often experienced seizures 13-24 months after the index seizure (OR 2.87, CI 2.07-3.99) with the highest OR in those with CP or attention deficit hyperactivity disorder (ADHD). Children diagnosed at age <1 year exhibited the highest prevalence of comorbidities as well as OR for repeated seizures. A combination of young age and comorbidity was associated with an OR for repeated seizures of 5.12 (CI 3.03-8.65). Among the children without comorbidities 76% were seizure free 13-24 months after the index seizure or after initiation of AED treatment compared to 53% of children with comorbidities. This study indicates that neurodevelopmental comorbidities and CP in children with epilepsy tend to be present already at seizure onset and that such comorbidities are strong indicators of poor outcome regarding seizure control with or without treatment. Copyright © 2018 Elsevier B.V. All rights reserved.

Effect of epileptic seizures on the cerebrospinal fluid--A systematic retrospective analysis.

PubMed

Tumani, Hayrettin; Jobs, Catherine; Brettschneider, Johannes; Hoppner, Anselm C; Kerling, Frank; Fauser, Susanne

2015-08-01

Analyses of the cerebrospinal fluid (CSF) are obligatory when epileptic seizures manifest for the first time in order to exclude life-threatening causes or treatable diseases such as acute infections or autoimmune encephalitis. However, there are only few systematic investigations on the effect of seizures themselves on CSF parameters and the significance of these parameters in differential diagnosis. CSF samples of 309 patients with epileptic and 10 with psychogenic seizures were retrospectively analyzed. CSF samples were collected between 1999 and 2008. Cell counts, the albumin quotient, lactate and Tau-protein levels were determined. Findings were correlated with seizure types, seizure etiology (symptomatic, cryptogenic, occasional seizure), and seizure duration. Pathological findings were only observed in patients with epileptic but not with psychogenic seizures. The lactate concentration was elevated in 14%, the albumin quotient in 34%, and the Tau protein level in 36% of CSF samples. Cell counts were only slightly elevated in 6% of patients. Different seizure types influenced all parameters except for the cell count: In status epilepticus highest, in simple partial seizures lowest values were seen. Symptomatic partial and generalized epileptic seizures had significantly higher Tau-protein levels than cryptogenic partial seizures. In patients with repetitive and occasional epileptic seizures, higher Tau-protein levels were seen than in those with psychogenic seizures. Duration of epileptic seizures was positively correlated with the albumin quotient, lactate and Tau-protein levels. High variability of investigated CSF parameters within each subgroup rendered a clear separation between epileptic and psychogenic seizures impossible. Elevated cell counts are infrequently observed in patients with epileptic seizures and should therefore not uncritically be interpreted as a postictal phenomenon. However, blood-CSF barrier disruption, increased glucose metabolism and elevation of neuronal damage markers are observed in considerable percentages of patients and depend on many factors such as etiology, seizure type and duration. Copyright © 2015 Elsevier B.V. All rights reserved.

NASA airborne radar wind shear detection algorithm and the detection of wet microbursts in the vicinity of Orlando, Florida

NASA Technical Reports Server (NTRS)

Britt, Charles L.; Bracalente, Emedio M.

1992-01-01

The algorithms used in the NASA experimental wind shear radar system for detection, characterization, and determination of windshear hazard are discussed. The performance of the algorithms in the detection of wet microbursts near Orlando is presented. Various suggested algorithms that are currently being evaluated using the flight test results from Denver and Orlando are reviewed.

Seizure Recognition and Observation: A Guide for Allied Health Professionals.

ERIC Educational Resources Information Center

Epilepsy Foundation of America, Landover, MD.

Intended for allied health professionals, this guide provides information on seizure recognition and classification to help them assist the patient, the family, and the treating physician in obtaining control of epileptic seizures. A section on seizure recognition describes epilepsy and seizures, covering seizure classification and the causes of…

19 CFR 162.22 - Seizure of conveyances.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 19 Customs Duties 2 2010-04-01 2010-04-01 false Seizure of conveyances. 162.22 Section 162.22... TREASURY (CONTINUED) INSPECTION, SEARCH, AND SEIZURE Seizures § 162.22 Seizure of conveyances. (a) General applicability. If it shall appear to any officer authorized to board conveyances and make seizures that there...

19 CFR 162.21 - Responsibility and authority for seizures.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 19 Customs Duties 2 2010-04-01 2010-04-01 false Responsibility and authority for seizures. 162.21...; DEPARTMENT OF THE TREASURY (CONTINUED) INSPECTION, SEARCH, AND SEIZURE Seizures § 162.21 Responsibility and authority for seizures. (a) Seizures by Customs officers. Property may be seized, if available, by any...