A periodic spatio-spectral filter for event-related potentials.

PubMed

Ghaderi, Foad; Kim, Su Kyoung; Kirchner, Elsa Andrea

2016-12-01

With respect to single trial detection of event-related potentials (ERPs), spatial and spectral filters are two of the most commonly used pre-processing techniques for signal enhancement. Spatial filters reduce the dimensionality of the data while suppressing the noise contribution and spectral filters attenuate frequency components that most likely belong to noise subspace. However, the frequency spectrum of ERPs overlap with that of the ongoing electroencephalogram (EEG) and different types of artifacts. Therefore, proper selection of the spectral filter cutoffs is not a trivial task. In this research work, we developed a supervised method to estimate the spatial and finite impulse response (FIR) spectral filters, simultaneously. We evaluated the performance of the method on offline single trial classification of ERPs in datasets recorded during an oddball paradigm. The proposed spatio-spectral filter improved the overall single-trial classification performance by almost 9% on average compared with the case that no spatial filters were used. We also analyzed the effects of different spectral filter lengths and the number of retained channels after spatial filtering. Copyright © 2016. Published by Elsevier Ltd.

Classification of Hand Grasp Kinetics and Types Using Movement-Related Cortical Potentials and EEG Rhythms.

PubMed

Jochumsen, Mads; Rovsing, Cecilie; Rovsing, Helene; Niazi, Imran Khan; Dremstrup, Kim; Kamavuako, Ernest Nlandu

2017-01-01

Detection of single-trial movement intentions from EEG is paramount for brain-computer interfacing in neurorehabilitation. These movement intentions contain task-related information and if this is decoded, the neurorehabilitation could potentially be optimized. The aim of this study was to classify single-trial movement intentions associated with two levels of force and speed and three different grasp types using EEG rhythms and components of the movement-related cortical potential (MRCP) as features. The feature importance was used to estimate encoding of discriminative information. Two data sets were used. 29 healthy subjects executed and imagined different hand movements, while EEG was recorded over the contralateral sensorimotor cortex. The following features were extracted: delta, theta, mu/alpha, beta, and gamma rhythms, readiness potential, negative slope, and motor potential of the MRCP. Sequential forward selection was performed, and classification was performed using linear discriminant analysis and support vector machines. Limited classification accuracies were obtained from the EEG rhythms and MRCP-components: 0.48 ± 0.05 (grasp types), 0.41 ± 0.07 (kinetic profiles, motor execution), and 0.39 ± 0.08 (kinetic profiles, motor imagination). Delta activity contributed the most but all features provided discriminative information. These findings suggest that information from the entire EEG spectrum is needed to discriminate between task-related parameters from single-trial movement intentions.

Cognitive workload modulation through degraded visual stimuli: a single-trial EEG study

NASA Astrophysics Data System (ADS)

Yu, K.; Prasad, I.; Mir, H.; Thakor, N.; Al-Nashash, H.

2015-08-01

Objective. Our experiments explored the effect of visual stimuli degradation on cognitive workload. Approach. We investigated the subjective assessment, event-related potentials (ERPs) as well as electroencephalogram (EEG) as measures of cognitive workload. Main results. These experiments confirm that degradation of visual stimuli increases cognitive workload as assessed by subjective NASA task load index and confirmed by the observed P300 amplitude attenuation. Furthermore, the single-trial multi-level classification using features extracted from ERPs and EEG is found to be promising. Specifically, the adopted single-trial oscillatory EEG/ERP detection method achieved an average accuracy of 85% for discriminating 4 workload levels. Additionally, we found from the spatial patterns obtained from EEG signals that the frontal parts carry information that can be used for differentiating workload levels. Significance. Our results show that visual stimuli can modulate cognitive workload, and the modulation can be measured by the single trial EEG/ERP detection method.

Classification and definition of misuse, abuse, and related events in clinical trials: ACTTION systematic review and recommendations.

PubMed

Smith, Shannon M; Dart, Richard C; Katz, Nathaniel P; Paillard, Florence; Adams, Edgar H; Comer, Sandra D; Degroot, Aldemar; Edwards, Robert R; Haddox, J David; Jaffe, Jerome H; Jones, Christopher M; Kleber, Herbert D; Kopecky, Ernest A; Markman, John D; Montoya, Ivan D; O'Brien, Charles; Roland, Carl L; Stanton, Marsha; Strain, Eric C; Vorsanger, Gary; Wasan, Ajay D; Weiss, Roger D; Turk, Dennis C; Dworkin, Robert H

2013-11-01

As the nontherapeutic use of prescription medications escalates, serious associated consequences have also increased. This makes it essential to estimate misuse, abuse, and related events (MAREs) in the development and postmarketing adverse event surveillance and monitoring of prescription drugs accurately. However, classifications and definitions to describe prescription drug MAREs differ depending on the purpose of the classification system, may apply to single events or ongoing patterns of inappropriate use, and are not standardized or systematically employed, thereby complicating the ability to assess MARE occurrence adequately. In a systematic review of existing prescription drug MARE terminology and definitions from consensus efforts, review articles, and major institutions and agencies, MARE terms were often defined inconsistently or idiosyncratically, or had definitions that overlapped with other MARE terms. The Analgesic, Anesthetic, and Addiction Clinical Trials, Translations, Innovations, Opportunities, and Networks (ACTTION) public-private partnership convened an expert panel to develop mutually exclusive and exhaustive consensus classifications and definitions of MAREs occurring in clinical trials of analgesic medications to increase accuracy and consistency in characterizing their occurrence and prevalence in clinical trials. The proposed ACTTION classifications and definitions are designed as a first step in a system to adjudicate MAREs that occur in analgesic clinical trials and postmarketing adverse event surveillance and monitoring, which can be used in conjunction with other methods of assessing a treatment's abuse potential. Copyright © 2013 International Association for the Study of Pain. All rights reserved.

Classification of mouth movements using 7 T fMRI.

PubMed

Bleichner, M G; Jansma, J M; Salari, E; Freudenburg, Z V; Raemaekers, M; Ramsey, N F

2015-12-01

A brain-computer interface (BCI) is an interface that uses signals from the brain to control a computer. BCIs will likely become important tools for severely paralyzed patients to restore interaction with the environment. The sensorimotor cortex is a promising target brain region for a BCI due to the detailed topography and minimal functional interference with other important brain processes. Previous studies have shown that attempted movements in paralyzed people generate neural activity that strongly resembles actual movements. Hence decodability for BCI applications can be studied in able-bodied volunteers with actual movements. In this study we tested whether mouth movements provide adequate signals in the sensorimotor cortex for a BCI. The study was executed using fMRI at 7 T to ensure relevance for BCI with cortical electrodes, as 7 T measurements have been shown to correlate well with electrocortical measurements. Twelve healthy volunteers executed four mouth movements (lip protrusion, tongue movement, teeth clenching, and the production of a larynx activating sound) while in the scanner. Subjects performed a training and a test run. Single trials were classified based on the Pearson correlation values between the activation patterns per trial type in the training run and single trials in the test run in a 'winner-takes-all' design. Single trial mouth movements could be classified with 90% accuracy. The classification was based on an area with a volume of about 0.5 cc, located on the sensorimotor cortex. If voxels were limited to the surface, which is accessible for electrode grids, classification accuracy was still very high (82%). Voxels located on the precentral cortex performed better (87%) than the postcentral cortex (72%). The high reliability of decoding mouth movements suggests that attempted mouth movements are a promising candidate for BCI in paralyzed people.

Effects of eye artifact removal methods on single trial P300 detection, a comparative study.

PubMed

Ghaderi, Foad; Kim, Su Kyoung; Kirchner, Elsa Andrea

2014-01-15

Electroencephalographic signals are commonly contaminated by eye artifacts, even if recorded under controlled conditions. The objective of this work was to quantitatively compare standard artifact removal methods (regression, filtered regression, Infomax, and second order blind identification (SOBI)) and two artifact identification approaches for independent component analysis (ICA) methods, i.e. ADJUST and correlation. To this end, eye artifacts were removed and the cleaned datasets were used for single trial classification of P300 (a type of event related potentials elicited using the oddball paradigm). Statistical analysis of the results confirms that the combination of Infomax and ADJUST provides a relatively better performance (0.6% improvement on average of all subject) while the combination of SOBI and correlation performs the worst. Low-pass filtering the data at lower cutoffs (here 4 Hz) can also improve the classification accuracy. Without requiring any artifact reference channel, the combination of Infomax and ADJUST improves the classification performance more than the other methods for both examined filtering cutoffs, i.e., 4 Hz and 25 Hz. Copyright © 2013 Elsevier B.V. All rights reserved.

A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface.

PubMed

Zhou, Bangyan; Wu, Xiaopei; Lv, Zhao; Zhang, Lei; Guo, Xiaojin

2016-01-01

Independent component analysis (ICA) as a promising spatial filtering method can separate motor-related independent components (MRICs) from the multichannel electroencephalogram (EEG) signals. However, the unpredictable burst interferences may significantly degrade the performance of ICA-based brain-computer interface (BCI) system. In this study, we proposed a new algorithm frame to address this issue by combining the single-trial-based ICA filter with zero-training classifier. We developed a two-round data selection method to identify automatically the badly corrupted EEG trials in the training set. The "high quality" training trials were utilized to optimize the ICA filter. In addition, we proposed an accuracy-matrix method to locate the artifact data segments within a single trial and investigated which types of artifacts can influence the performance of the ICA-based MIBCIs. Twenty-six EEG datasets of three-class motor imagery were used to validate the proposed methods, and the classification accuracies were compared with that obtained by frequently used common spatial pattern (CSP) spatial filtering algorithm. The experimental results demonstrated that the proposed optimizing strategy could effectively improve the stability, practicality and classification performance of ICA-based MIBCI. The study revealed that rational use of ICA method may be crucial in building a practical ICA-based MIBCI system.

Optimization of Support Vector Machine (SVM) for Object Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

The synergy between complex channel-specific FIR filter and spatial filter for single-trial EEG classification.

PubMed

Yu, Ke; Wang, Yue; Shen, Kaiquan; Li, Xiaoping

2013-01-01

The common spatial pattern analysis (CSP), a frequently utilized feature extraction method in brain-computer-interface applications, is believed to be time-invariant and sensitive to noises, mainly due to an inherent shortcoming of purely relying on spatial filtering. Therefore, temporal/spectral filtering which can be very effective to counteract the unfavorable influence of noises is usually used as a supplement. This work integrates the CSP spatial filters with complex channel-specific finite impulse response (FIR) filters in a natural and intuitive manner. Each hybrid spatial-FIR filter is of high-order, data-driven and is unique to its corresponding channel. They are derived by introducing multiple time delays and regularization into conventional CSP. The general framework of the method follows that of CSP but performs better, as proven in single-trial classification tasks like event-related potential detection and motor imagery.

Testing of the Support Vector Machine for Binary-Class Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew

2011-01-01

The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Support Vector Machines implemented in this research were used as classifiers for the final stage in a Multistage Autonomous Target Recognition system. A single kernel SVM known as SVMlight, and a modified version known as a Support Vector Machine with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SMV as a method for classification. From trial to trial, SVM produces consistent results

Comparing Features for Classification of MEG Responses to Motor Imagery.

PubMed

Halme, Hanna-Leena; Parkkonen, Lauri

2016-01-01

Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio-spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI-related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system.

A Fast, Open EEG Classification Framework Based on Feature Compression and Channel Ranking

PubMed Central

Han, Jiuqi; Zhao, Yuwei; Sun, Hongji; Chen, Jiayun; Ke, Ang; Xu, Gesen; Zhang, Hualiang; Zhou, Jin; Wang, Changyong

2018-01-01

Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA) model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI) competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods. PMID:29713262

Emotion Recognition from Single-Trial EEG Based on Kernel Fisher's Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine

PubMed Central

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-01-01

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods. PMID:25061837

Emotion recognition from single-trial EEG based on kernel Fisher's emotion pattern and imbalanced quasiconformal kernel support vector machine.

PubMed

Liu, Yi-Hung; Wu, Chien-Te; Cheng, Wei-Teng; Hsiao, Yu-Tsung; Chen, Po-Ming; Teng, Jyh-Tong

2014-07-24

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher's discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher's emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

Detailed classification of swimming paths in the Morris Water Maze: multiple strategies within one trial

PubMed Central

Gehring, Tiago V.; Luksys, Gediminas; Sandi, Carmen; Vasilaki, Eleni

2015-01-01

The Morris Water Maze is a widely used task in studies of spatial learning with rodents. Classical performance measures of animals in the Morris Water Maze include the escape latency, and the cumulative distance to the platform. Other methods focus on classifying trajectory patterns to stereotypical classes representing different animal strategies. However, these approaches typically consider trajectories as a whole, and as a consequence they assign one full trajectory to one class, whereas animals often switch between these strategies, and their corresponding classes, within a single trial. To this end, we take a different approach: we look for segments of diverse animal behaviour within one trial and employ a semi-automated classification method for identifying the various strategies exhibited by the animals within a trial. Our method allows us to reveal significant and systematic differences in the exploration strategies of two animal groups (stressed, non-stressed), that would be unobserved by earlier methods. PMID:26423140

Sequence-sensitive exemplar and decision-bound accounts of speeded-classification performance in a modified Garner-tasks paradigm.

PubMed

Little, Daniel R; Wang, Tony; Nosofsky, Robert M

2016-09-01

Among the most fundamental results in the area of perceptual classification are the "correlated facilitation" and "filtering interference" effects observed in Garner's (1974) speeded categorization tasks: In the case of integral-dimension stimuli, relative to a control task, single-dimension classification is faster when there is correlated variation along a second dimension, but slower when there is orthogonal variation that cannot be filtered out (e.g., by attention). These fundamental effects may result from participants' use of a trial-by-trial bypass strategy in the control and correlated tasks: The observer changes the previous category response whenever the stimulus changes, and maintains responses if the stimulus repeats. Here we conduct modified versions of the Garner tasks that eliminate the availability of a pure bypass strategy. The fundamental facilitation and interference effects remain, but are still largely explainable in terms of pronounced sequential effects in all tasks. We develop sequence-sensitive versions of exemplar-retrieval and decision-bound models aimed at capturing the detailed, trial-by-trial response-time distribution data. The models combine assumptions involving: (i) strengthened perceptual/memory representations of stimuli that repeat across consecutive trials, and (ii) a bias to change category responses on trials in which the stimulus changes. These models can predict our observed effects and provide a more complete account of the underlying bases of performance in our modified Garner tasks. Copyright © 2016 Elsevier Inc. All rights reserved.

Comparing Features for Classification of MEG Responses to Motor Imagery

PubMed Central

Halme, Hanna-Leena; Parkkonen, Lauri

2016-01-01

Background Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. Methods MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio—spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. Results The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. Conclusions We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI-related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system. PMID:27992574

Early differential processing of material images: Evidence from ERP classification.

PubMed

Wiebel, Christiane B; Valsecchi, Matteo; Gegenfurtner, Karl R

2014-06-24

Investigating the temporal dynamics of natural image processing using event-related potentials (ERPs) has a long tradition in object recognition research. In a classical Go-NoGo task two characteristic effects have been emphasized: an early task independent category effect and a later task-dependent target effect. Here, we set out to use this well-established Go-NoGo paradigm to study the time course of material categorization. Material perception has gained more and more interest over the years as its importance in natural viewing conditions has been ignored for a long time. In addition to analyzing standard ERPs, we conducted a single trial ERP pattern analysis. To validate this procedure, we also measured ERPs in two object categories (people and animals). Our linear classification procedure was able to largely capture the overall pattern of results from the canonical analysis of the ERPs and even extend it. We replicate the known target effect (differential Go-NoGo potential at frontal sites) for the material images. Furthermore, we observe task-independent differential activity between the two material categories as early as 140 ms after stimulus onset. Using our linear classification approach, we show that material categories can be differentiated consistently based on the ERP pattern in single trials around 100 ms after stimulus onset, independent of the target-related status. This strengthens the idea of early differential visual processing of material categories independent of the task, probably due to differences in low-level image properties and suggests pattern classification of ERP topographies as a strong instrument for investigating electrophysiological brain activity. © 2014 ARVO.

Neural network classification of myoelectric signal for prosthesis control.

PubMed

Kelly, M F; Parker, P A; Scott, R N

1991-12-01

An alternate approach to deriving control for multidegree of freedom prosthetic arms is considered. By analyzing a single-channel myoelectric signal (MES), we can extract information that can be used to identify different contraction patterns in the upper arm. These contraction patterns are generated by subjects without previous training and are naturally associated with specific functions. Using a set of normalized MES spectral features, we can identify contraction patterns for four arm functions, specifically extension and flexion of the elbow and pronation and supination of the forearm. Performing identification independent of signal power is advantageous because this can then be used as a means for deriving proportional rate control for a prosthesis. An artificial neural network implementation is applied in the classification task. By using three single-layer perceptron networks, the MES is classified, with the spectral representations as input features. Trials performed on five subjects with normal limbs resulted in an average classification performance level of 85% for the four functions. Copyright © 1991. Published by Elsevier Ltd.

[Research on the methods for multi-class kernel CSP-based feature extraction].

PubMed

Wang, Jinjia; Zhang, Lingzhi; Hu, Bei

2012-04-01

To relax the presumption of strictly linear patterns in the common spatial patterns (CSP), we studied the kernel CSP (KCSP). A new multi-class KCSP (MKCSP) approach was proposed in this paper, which combines the kernel approach with multi-class CSP technique. In this approach, we used kernel spatial patterns for each class against all others, and extracted signal components specific to one condition from EEG data sets of multiple conditions. Then we performed classification using the Logistic linear classifier. Brain computer interface (BCI) competition III_3a was used in the experiment. Through the experiment, it can be proved that this approach could decompose the raw EEG singles into spatial patterns extracted from multi-class of single trial EEG, and could obtain good classification results.

Single Versus Multiple Events Error Potential Detection in a BCI-Controlled Car Game With Continuous and Discrete Feedback.

PubMed

Kreilinger, Alex; Hiebel, Hannah; Müller-Putz, Gernot R

2016-03-01

This work aimed to find and evaluate a new method for detecting errors in continuous brain-computer interface (BCI) applications. Instead of classifying errors on a single-trial basis, the new method was based on multiple events (MEs) analysis to increase the accuracy of error detection. In a BCI-driven car game, based on motor imagery (MI), discrete events were triggered whenever subjects collided with coins and/or barriers. Coins counted as correct events, whereas barriers were errors. This new method, termed ME method, combined and averaged the classification results of single events (SEs) and determined the correctness of MI trials, which consisted of event sequences instead of SEs. The benefit of this method was evaluated in an offline simulation. In an online experiment, the new method was used to detect erroneous MI trials. Such MI trials were discarded and could be repeated by the users. We found that, even with low SE error potential (ErrP) detection rates, feasible accuracies can be achieved when combining MEs to distinguish erroneous from correct MI trials. Online, all subjects reached higher scores with error detection than without, at the cost of longer times needed for completing the game. Findings suggest that ErrP detection may become a reliable tool for monitoring continuous states in BCI applications when combining MEs. This paper demonstrates a novel technique for detecting errors in online continuous BCI applications, which yields promising results even with low single-trial detection rates.

Electroencephalogy (EEG) Feedback in Decision-Making

DTIC Science & Technology

2015-08-26

19   Variability  in  individual  subject   BCI  classification...approach traditionally used in single-trial BCI (Brain-Computer Interface) tasks suggested a similar effect-size and scalp distribution. However...situation. Although nearly all BCI paradigms have used a variant of the RSVP technique, there was no indication in the literature as to why this was

Automatic classification of the sub-techniques (gears) used in cross-country ski skating employing a mobile phone.

PubMed

Stöggl, Thomas; Holst, Anders; Jonasson, Arndt; Andersson, Erik; Wunsch, Tobias; Norström, Christer; Holmberg, Hans-Christer

2014-10-31

The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

Refining the definition of mandibular osteoradionecrosis in clinical trials: The cancer research UK HOPON trial (Hyperbaric Oxygen for the Prevention of Osteoradionecrosis).

PubMed

Shaw, Richard; Tesfaye, Binyam; Bickerstaff, Matt; Silcocks, Paul; Butterworth, Christopher

2017-01-01

Mandibular osteoradionecrosis (ORN) is a common and serious complication of head and neck radiotherapy for which there is little reliable evidence for prevention or treatment. The diagnosis and classification of ORN have been inconsistently and imprecisely defined, even in clinical trials. A systematic review of diagnosis and classifications of ORN with specific focus on clinical trials is presented. The most suitable classification was evaluated for consistency using blinded independent review of outcome data (clinical photographs and radiographs) in the HOPON trial. Of 16 ORN classifications found, only one (Notani) appeared suitable as an endpoint in clinical trials. Clinical records of 217 timepoints were analysed amongst 94 randomised patients in the HOPON trial. The only inconsistency in classification arose where minor bone spicules (MBS) were apparent, which occurred in 19% of patients. Some trial investigators judged MBS as clinically unimportant and not reflecting ORN, others classified as ORN based on rigid definitions in common clinical use. When MBS was added as a distinct category to the Notani classification this ambiguity was resolved and agreement between observers was achieved. Most definitions and clinical classifications are based on retrospective case series and may be unsuitable for prospective interventional trials of ORN prevention or treatment. When ORN is used as a primary or secondary outcome in prospective clinical trials, the use of Notani classification with the additional category of MBS is recommended as it avoids subjectivity and enhances reliability and consistency of reporting. Copyright © 2016 Elsevier Ltd. All rights reserved.

Visual modifications on the P300 speller BCI paradigm

NASA Astrophysics Data System (ADS)

Salvaris, M.; Sepulveda, F.

2009-08-01

The best known P300 speller brain-computer interface (BCI) paradigm is the Farwell and Donchin paradigm. In this paper, various changes to the visual aspects of this protocol are explored as well as their effects on classification. Changes to the dimensions of the symbols, the distance between the symbols and the colours used were tested. The purpose of the present work was not to achieve the highest possible accuracy results, but to ascertain whether these simple modifications to the visual protocol will provide classification differences between them and what these differences will be. Eight subjects were used, with each subject carrying out a total of six different experiments. In each experiment, the user spelt a total of 39 characters. Two types of classifiers were trained and tested to determine whether the results were classifier dependant. These were a support vector machine (SVM) with a radial basis function (RBF) kernel and Fisher's linear discriminant (FLD). The single-trial classification results and multiple-trial classification results were recorded and compared. Although no visual protocol was the best for all subjects, the best performances, across both classifiers, were obtained with the white background (WB) visual protocol. The worst performance was obtained with the small symbol size (SSS) visual protocol.

Application of quantum-behaved particle swarm optimization to motor imagery EEG classification.

PubMed

Hsu, Wei-Yen

2013-12-01

In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.

The brain’s response to pleasant touch: an EEG investigation of tactile caressing

PubMed Central

Singh, Harsimrat; Bauer, Markus; Chowanski, Wojtek; Sui, Yi; Atkinson, Douglas; Baurley, Sharon; Fry, Martin; Evans, Joe; Bianchi-Berthouze, Nadia

2014-01-01

Somatosensation as a proximal sense can have a strong impact on our attitude toward physical objects and other human beings. However, relatively little is known about how hedonic valence of touch is processed at the cortical level. Here we investigated the electrophysiological correlates of affective tactile sensation during caressing of the right forearm with pleasant and unpleasant textile fabrics. We show dissociation between more physically driven differential brain responses to the different fabrics in early somatosensory cortex – the well-known mu-suppression (10–20 Hz) – and a beta-band response (25–30 Hz) in presumably higher-order somatosensory areas in the right hemisphere that correlated well with the subjective valence of tactile caressing. Importantly, when using single trial classification techniques, beta-power significantly distinguished between pleasant and unpleasant stimulation on a single trial basis with high accuracy. Our results therefore suggest a dissociation of the sensory and affective aspects of touch in the somatosensory system and may provide features that may be used for single trial decoding of affective mental states from simple electroencephalographic measurements. PMID:25426047

Stroke subtyping for genetic association studies? A comparison of the CCS and TOAST classifications.

PubMed

Lanfranconi, Silvia; Markus, Hugh S

2013-12-01

A reliable and reproducible classification system of stroke subtype is essential for epidemiological and genetic studies. The Causative Classification of Stroke system is an evidence-based computerized algorithm with excellent inter-rater reliability. It has been suggested that, compared to the Trial of ORG 10172 in Acute Stroke Treatment classification, it increases the proportion of cases with defined subtype that may increase power in genetic association studies. We compared Trial of ORG 10172 in Acute Stroke Treatment and Causative Classification of Stroke system classifications in a large cohort of well-phenotyped stroke patients. Six hundred ninety consecutively recruited patients with first-ever ischemic stroke were classified, using review of clinical data and original imaging, according to the Trial of ORG 10172 in Acute Stroke Treatment and Causative Classification of Stroke system classifications. There was excellent agreement subtype assigned by between Trial of ORG 10172 in Acute Stroke Treatment and Causative Classification of Stroke system (kappa = 0·85). The agreement was excellent for the major individual subtypes: large artery atherosclerosis kappa = 0·888, small-artery occlusion kappa = 0·869, cardiac embolism kappa = 0·89, and undetermined category kappa = 0·884. There was only moderate agreement (kappa = 0·41) for the subjects with at least two competing underlying mechanism. Thirty-five (5·8%) patients classified as undetermined by Trial of ORG 10172 in Acute Stroke Treatment were assigned to a definite subtype by Causative Classification of Stroke system. Thirty-two subjects assigned to a definite subtype by Trial of ORG 10172 in Acute Stroke Treatment were classified as undetermined by Causative Classification of Stroke system. There is excellent agreement between classification using Trial of ORG 10172 in Acute Stroke Treatment and Causative Classification of Stroke systems but no evidence that Causative Classification of Stroke system reduced the proportion of patients classified to undetermined subtypes. The excellent inter-rater reproducibility and web-based semiautomated nature make Causative Classification of Stroke system suitable for multicenter studies, but the benefit of reclassifying cases already classified using the Trial of ORG 10172 in Acute Stroke Treatment system on existing databases is likely to be small. © 2012 The Authors. International Journal of Stroke © 2012 World Stroke Organization.

Endpoint design for future renal denervation trials - Novel implications for a new definition of treatment response to renal denervation.

PubMed

Lambert, Thomas; Nahler, Alexander; Rohla, Miklos; Reiter, Christian; Grund, Michael; Kammler, Jürgen; Blessberger, Hermann; Kypta, Alexander; Kellermair, Jörg; Schwarz, Stefan; Starnawski, Jennifer A; Lichtenauer, Michael; Weiss, Thomas W; Huber, Kurt; Steinwender, Clemens

2016-10-01

Defining an adequate endpoint for renal denervation trials represents a major challenge. A high inter-individual and intra-individual variability of blood pressure levels as well as a partial or total non-adherence on antihypertensive drugs hamper treatment evaluations after renal denervation. Blood pressure measurements at a single point in time as used as primary endpoint in most clinical trials on renal denervation, might not be sufficient to discriminate between patients who do or do not respond to renal denervation. We compared the traditional responder classification (defined as systolic 24-hour blood pressure reduction of -5mmHg six months after renal denervation) with a novel definition of an ideal respondership (based on a 24h blood pressure reduction at no point in time, one, or all follow-up timepoints). We were able to re-classify almost a quarter of patients. Blood pressure variability was substantial in patients traditionally defined as responders. On the other hand, our novel classification of an ideal respondership seems to be clinically superior in discriminating sustained from pseudo-response to renal denervation. Based on our observations, we recommend that the traditional response classification should be reconsidered and possibly strengthened by using a composite endpoint of 24h-BP reductions at different follow-up-visits. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Interactions between pre-processing and classification methods for event-related-potential classification: best-practice guidelines for brain-computer interfacing.

PubMed

Farquhar, J; Hill, N J

2013-04-01

Detecting event related potentials (ERPs) from single trials is critical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging signal detection problem. Previous work has tended to focus on how best to detect a single ERP type (such as the visual oddball response). However, the underlying ERP detection problem is essentially the same regardless of stimulus modality (e.g., visual or tactile), ERP component (e.g., P300 oddball response, or the error-potential), measurement system or electrode layout. To investigate whether a single ERP detection method might work for a wider range of ERP BCIs we compare detection performance over a large corpus of more than 50 ERP BCI datasets whilst systematically varying the electrode montage, spectral filter, spatial filter and classifier training methods. We identify an interesting interaction between spatial whitening and regularised classification which made detection performance independent of the choice of spectral filter low-pass frequency. Our results show that pipeline consisting of spectral filtering, spatial whitening, and regularised classification gives near maximal performance in all cases. Importantly, this pipeline is simple to implement and completely automatic with no expert feature selection or parameter tuning required. Thus, we recommend this combination as a "best-practice" method for ERP detection problems.

You Can't Think and Hit at the Same Time: Neural Correlates of Baseball Pitch Classification.

PubMed

Sherwin, Jason; Muraskin, Jordan; Sajda, Paul

2012-01-01

Hitting a baseball is often described as the most difficult thing to do in sports. A key aptitude of a good hitter is the ability to determine which pitch is coming. This rapid decision requires the batter to make a judgment in a fraction of a second based largely on the trajectory and spin of the ball. When does this decision occur relative to the ball's trajectory and is it possible to identify neural correlates that represent how the decision evolves over a split second? Using single-trial analysis of electroencephalography (EEG) we address this question within the context of subjects discriminating three types of pitches (fastball, curveball, slider) based on pitch trajectories. We find clear neural signatures of pitch classification and, using signal detection theory, we identify the times of discrimination on a trial-to-trial basis. Based on these neural signatures we estimate neural discrimination distributions as a function of the distance the ball is from the plate. We find all three pitches yield unique distributions, namely the timing of the discriminating neural signatures relative to the position of the ball in its trajectory. For instance, fastballs are discriminated at the earliest points in their trajectory, relative to the two other pitches, which is consistent with the need for some constant time to generate and execute the motor plan for the swing (or inhibition of the swing). We also find incorrect discrimination of a pitch (errors) yields neural sources in Brodmann Area 10, which has been implicated in prospective memory, recall, and task difficulty. In summary, we show that single-trial analysis of EEG yields informative distributions of the relative point in a baseball's trajectory when the batter makes a decision on which pitch is coming.

Automatic Classification of the Sub-Techniques (Gears) Used in Cross-Country Ski Skating Employing a Mobile Phone

PubMed Central

Stöggl, Thomas; Holst, Anders; Jonasson, Arndt; Andersson, Erik; Wunsch, Tobias; Norström, Christer; Holmberg, Hans-Christer

2014-01-01

The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear. PMID:25365459

Towards a truly mobile auditory brain-computer interface: exploring the P300 to take away.

PubMed

De Vos, Maarten; Gandras, Katharina; Debener, Stefan

2014-01-01

In a previous study we presented a low-cost, small, and wireless 14-channel EEG system suitable for field recordings (Debener et al., 2012, psychophysiology). In the present follow-up study we investigated whether a single-trial P300 response can be reliably measured with this system, while subjects freely walk outdoors. Twenty healthy participants performed a three-class auditory oddball task, which included rare target and non-target distractor stimuli presented with equal probabilities of 16%. Data were recorded in a seated (control condition) and in a walking condition, both of which were realized outdoors. A significantly larger P300 event-related potential amplitude was evident for targets compared to distractors (p<.001), but no significant interaction with recording condition emerged. P300 single-trial analysis was performed with regularized stepwise linear discriminant analysis and revealed above chance-level classification accuracies for most participants (19 out of 20 for the seated, 16 out of 20 for the walking condition), with mean classification accuracies of 71% (seated) and 64% (walking). Moreover, the resulting information transfer rates for the seated and walking conditions were comparable to a recently published laboratory auditory brain-computer interface (BCI) study. This leads us to conclude that a truly mobile auditory BCI system is feasible. © 2013.

Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation

PubMed Central

Nicolae, Irina-Emilia; Acqualagna, Laura; Blankertz, Benjamin

2017-01-01

Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI) techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain. Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing) within three distinct domains (memory, language, and visual imagination). Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs) and in the extend and duration of event-related desynchronization (ERD) which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing. Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70–90% for all conditions and classification pairs. Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces. PMID:29046625

Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation.

PubMed

Nicolae, Irina-Emilia; Acqualagna, Laura; Blankertz, Benjamin

2017-01-01

Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI) techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain. Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing) within three distinct domains (memory, language, and visual imagination). Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs) and in the extend and duration of event-related desynchronization (ERD) which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing. Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70-90% for all conditions and classification pairs. Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces.

Reliability of the measures of weight-bearing distribution obtained during quiet stance by digital scales in subjects with and without hemiparesis.

PubMed

de Araujo-Barbosa, Paulo Henrique Ferreira; de Menezes, Lidiane Teles; Costa, Abraão Souza; Couto Paz, Clarissa Cardoso Dos Santos; Fachin-Martins, Emerson

2015-05-01

Described as an alternative way of assessing weight-bearing asymmetries, the measures obtained from digital scales have been used as an index to classify weight-bearing distribution. This study aimed to describe the intra-test and the test/retest reliability of measures in subjects with and without hemiparesis during quiet stance. The percentage of body weight borne by one limb was calculated for a sample of subjects with hemiparesis and for a control group that was matched by gender and age. A two-way analysis of variance was used to verify the intra-test reliability. This analysis was calculated using the differences between the averages of the measures obtained during single, double or triple trials. The intra-class correlation coefficient (ICC) was utilized and data plotted using the Bland-Altman method. The intra-test analysis showed significant differences, only observed in the hemiparesis group, between the measures obtained by single and triple trials. Excellent and moderate ICC values (0.69-0.84) between test and retest were observed in the hemiparesis group, while for control groups ICC values (0.41-0.74) were classified as moderate, progressing from almost poor for measures obtained by a single trial to almost excellent for those obtained by triple trials. In conclusion, good reliability ranging from moderate to excellent classifications was found for participants with and without hemiparesis. Moreover, an improvement of the repeatability was observed with fewer trials for participants with hemiparesis, and with more trials for participants without hemiparesis.

Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks

NASA Astrophysics Data System (ADS)

Zink, Rob; Hunyadi, Borbála; Van Huffel, Sabine; De Vos, Maarten

2016-08-01

Objective. In the past few years there has been a growing interest in studying brain functioning in natural, real-life situations. Mobile EEG allows to study the brain in real unconstrained environments but it faces the intrinsic challenge that it is impossible to disentangle observed changes in brain activity due to increase in cognitive demands by the complex natural environment or due to the physical involvement. In this work we aim to disentangle the influence of cognitive demands and distractions that arise from such outdoor unconstrained recordings. Approach. We evaluate the ERP and single trial characteristics of a three-class auditory oddball paradigm recorded in outdoor scenario’s while peddling on a fixed bike or biking freely around. In addition we also carefully evaluate the trial specific motion artifacts through independent gyro measurements and control for muscle artifacts. Main results. A decrease in P300 amplitude was observed in the free biking condition as compared to the fixed bike conditions. Above chance P300 single-trial classification in highly dynamic real life environments while biking outdoors was achieved. Certain significant artifact patterns were identified in the free biking condition, but neither these nor the increase in movement (as derived from continuous gyrometer measurements) can explain the differences in classification accuracy and P300 waveform differences with full clarity. The increased cognitive load in real-life scenarios is shown to play a major role in the observed differences. Significance. Our findings suggest that auditory oddball results measured in natural real-life scenarios are influenced mainly by increased cognitive load due to being in an unconstrained environment.

Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks.

PubMed

Zink, Rob; Hunyadi, Borbála; Huffel, Sabine Van; Vos, Maarten De

2016-08-01

In the past few years there has been a growing interest in studying brain functioning in natural, real-life situations. Mobile EEG allows to study the brain in real unconstrained environments but it faces the intrinsic challenge that it is impossible to disentangle observed changes in brain activity due to increase in cognitive demands by the complex natural environment or due to the physical involvement. In this work we aim to disentangle the influence of cognitive demands and distractions that arise from such outdoor unconstrained recordings. We evaluate the ERP and single trial characteristics of a three-class auditory oddball paradigm recorded in outdoor scenario's while peddling on a fixed bike or biking freely around. In addition we also carefully evaluate the trial specific motion artifacts through independent gyro measurements and control for muscle artifacts. A decrease in P300 amplitude was observed in the free biking condition as compared to the fixed bike conditions. Above chance P300 single-trial classification in highly dynamic real life environments while biking outdoors was achieved. Certain significant artifact patterns were identified in the free biking condition, but neither these nor the increase in movement (as derived from continuous gyrometer measurements) can explain the differences in classification accuracy and P300 waveform differences with full clarity. The increased cognitive load in real-life scenarios is shown to play a major role in the observed differences. Our findings suggest that auditory oddball results measured in natural real-life scenarios are influenced mainly by increased cognitive load due to being in an unconstrained environment.

SCoT: a Python toolbox for EEG source connectivity.

PubMed

Billinger, Martin; Brunner, Clemens; Müller-Putz, Gernot R

2014-01-01

Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT-a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT.

SCoT: a Python toolbox for EEG source connectivity

PubMed Central

Billinger, Martin; Brunner, Clemens; Müller-Putz, Gernot R.

2014-01-01

Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT—a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT. PMID:24653694

Robust artifactual independent component classification for BCI practitioners.

PubMed

Winkler, Irene; Brandl, Stephanie; Horn, Franziska; Waldburger, Eric; Allefeld, Carsten; Tangermann, Michael

2014-06-01

EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain-computer interfaces (BCIs). Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms. We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel strategy improves artifact classification. Addressing (2), ICA artifact cleaning has little influence on average BCI performance when analyzed by state-of-the-art BCI methods. When slow motor-related features are exploited, performance varies strongly between individuals, as artifacts may obstruct relevant neural activity or are inadvertently used for BCI control. Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Toward FRP-Based Brain-Machine Interfaces—Single-Trial Classification of Fixation-Related Potentials

PubMed Central

Finke, Andrea; Essig, Kai; Marchioro, Giuseppe; Ritter, Helge

2016-01-01

The co-registration of eye tracking and electroencephalography provides a holistic measure of ongoing cognitive processes. Recently, fixation-related potentials have been introduced to quantify the neural activity in such bi-modal recordings. Fixation-related potentials are time-locked to fixation onsets, just like event-related potentials are locked to stimulus onsets. Compared to existing electroencephalography-based brain-machine interfaces that depend on visual stimuli, fixation-related potentials have the advantages that they can be used in free, unconstrained viewing conditions and can also be classified on a single-trial level. Thus, fixation-related potentials have the potential to allow for conceptually different brain-machine interfaces that directly interpret cortical activity related to the visual processing of specific objects. However, existing research has investigated fixation-related potentials only with very restricted and highly unnatural stimuli in simple search tasks while participant’s body movements were restricted. We present a study where we relieved many of these restrictions while retaining some control by using a gaze-contingent visual search task. In our study, participants had to find a target object out of 12 complex and everyday objects presented on a screen while the electrical activity of the brain and eye movements were recorded simultaneously. Our results show that our proposed method for the classification of fixation-related potentials can clearly discriminate between fixations on relevant, non-relevant and background areas. Furthermore, we show that our classification approach generalizes not only to different test sets from the same participant, but also across participants. These results promise to open novel avenues for exploiting fixation-related potentials in electroencephalography-based brain-machine interfaces and thus providing a novel means for intuitive human-machine interaction. PMID:26812487

Single-trial lie detection using a combined fNIRS-polygraph system

PubMed Central

Bhutta, M. Raheel; Hong, Melissa J.; Kim, Yun-Hee; Hong, Keum-Shik

2015-01-01

Deception is a human behavior that many people experience in daily life. It involves complex neuronal activities in addition to several physiological changes in the body. A polygraph, which can measure some of the physiological responses from the body, has been widely employed in lie-detection. Many researchers, however, believe that lie detection can become more precise if the neuronal changes that occur in the process of deception can be isolated and measured. In this study, we combine both measures (i.e., physiological and neuronal changes) for enhanced lie-detection. Specifically, to investigate the deception-related hemodynamic response, functional near-infrared spectroscopy (fNIRS) is applied at the prefrontal cortex besides a commercially available polygraph system. A mock crime scenario with a single-trial stimulus is set up as a deception protocol. The acquired data are classified into “true” and “lie” classes based on the fNIRS-based hemoglobin-concentration changes and polygraph-based physiological signal changes. Linear discriminant analysis is utilized as a classifier. The results indicate that the combined fNIRS-polygraph system delivers much higher classification accuracy than that of a singular system. This study demonstrates a plausible solution toward single-trial lie-detection by combining fNIRS and the polygraph. PMID:26082733

Cosmetic outcomes of laparoendoscopic single-site hysterectomy compared with multi-port surgery: randomized controlled trial.

PubMed

Song, Taejong; Cho, Juhee; Kim, Tae-Joong; Kim, Im-Ryung; Hahm, Tae Soo; Kim, Byoung-Gie; Bae, Duk-Soo

2013-01-01

To compare cosmetic satisfaction with laparoendoscopic single-site surgery (LESS) compared with multi-port surgery. Randomized controlled trial (Canadian Task Force classification I). University hospital. Twenty women who underwent laparoscopically-assisted vaginal hysterectomy (LAVH) via LESS or multi-port surgery. Laparoendoscopic single-site surgery or multi-port surgery. Cosmetic satisfaction was assessed using the Body Image Questionnaire at baseline and at 1, 4, and 24 weeks after surgery. Of the 20 LESS procedures, 1 was converted to multi-port surgery because of severe adhesions, and 1 woman assigned to undergo multi-port surgery was lost to follow-up. The 2 surgery groups did not differ in clinical demographic data and surgical results or postoperative pain scores at 12, 24, and 36 hours. Compared with the multi-port group, the LESS group reported significantly higher cosmetic satisfaction at 1, 4, and 24 weeks after surgery (p < .01). Compared with multi-port surgery, LESS is not only a feasible approach with comparable operative outcomes but also has an advantage insofar as cosmetic outcome. Copyright © 2013 AAGL. Published by Elsevier Inc. All rights reserved.

Bayesian learning for spatial filtering in an EEG-based brain-computer interface.

PubMed

Zhang, Haihong; Yang, Huijuan; Guan, Cuntai

2013-07-01

Spatial filtering for EEG feature extraction and classification is an important tool in brain-computer interface. However, there is generally no established theory that links spatial filtering directly to Bayes classification error. To address this issue, this paper proposes and studies a Bayesian analysis theory for spatial filtering in relation to Bayes error. Following the maximum entropy principle, we introduce a gamma probability model for describing single-trial EEG power features. We then formulate and analyze the theoretical relationship between Bayes classification error and the so-called Rayleigh quotient, which is a function of spatial filters and basically measures the ratio in power features between two classes. This paper also reports our extensive study that examines the theory and its use in classification, using three publicly available EEG data sets and state-of-the-art spatial filtering techniques and various classifiers. Specifically, we validate the positive relationship between Bayes error and Rayleigh quotient in real EEG power features. Finally, we demonstrate that the Bayes error can be practically reduced by applying a new spatial filter with lower Rayleigh quotient.

Crowdsourcing as a novel technique for retinal fundus photography classification: analysis of images in the EPIC Norfolk cohort on behalf of the UK Biobank Eye and Vision Consortium.

PubMed

Mitry, Danny; Peto, Tunde; Hayat, Shabina; Morgan, James E; Khaw, Kay-Tee; Foster, Paul J

2013-01-01

Crowdsourcing is the process of outsourcing numerous tasks to many untrained individuals. Our aim was to assess the performance and repeatability of crowdsourcing for the classification of retinal fundus photography. One hundred retinal fundus photograph images with pre-determined disease criteria were selected by experts from a large cohort study. After reading brief instructions and an example classification, we requested that knowledge workers (KWs) from a crowdsourcing platform classified each image as normal or abnormal with grades of severity. Each image was classified 20 times by different KWs. Four study designs were examined to assess the effect of varying incentive and KW experience in classification accuracy. All study designs were conducted twice to examine repeatability. Performance was assessed by comparing the sensitivity, specificity and area under the receiver operating characteristic curve (AUC). Without restriction on eligible participants, two thousand classifications of 100 images were received in under 24 hours at minimal cost. In trial 1 all study designs had an AUC (95%CI) of 0.701(0.680-0.721) or greater for classification of normal/abnormal. In trial 1, the highest AUC (95%CI) for normal/abnormal classification was 0.757 (0.738-0.776) for KWs with moderate experience. Comparable results were observed in trial 2. In trial 1, between 64-86% of any abnormal image was correctly classified by over half of all KWs. In trial 2, this ranged between 74-97%. Sensitivity was ≥ 96% for normal versus severely abnormal detections across all trials. Sensitivity for normal versus mildly abnormal varied between 61-79% across trials. With minimal training, crowdsourcing represents an accurate, rapid and cost-effective method of retinal image analysis which demonstrates good repeatability. Larger studies with more comprehensive participant training are needed to explore the utility of this compelling technique in large scale medical image analysis.

An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli

PubMed Central

Hill, N J; Schölkopf, B

2012-01-01

We report on the development and online testing of an EEG-based brain-computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects’ modulation of N1 and P3 ERP components measured during single 5-second stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare “oddball” stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly-known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention-modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject’s attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology. PMID:22333135

An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli

NASA Astrophysics Data System (ADS)

Hill, N. J.; Schölkopf, B.

2012-04-01

We report on the development and online testing of an electroencephalogram-based brain-computer interface (BCI) that aims to be usable by completely paralysed users—for whom visual or motor-system-based BCIs may not be suitable, and among whom reports of successful BCI use have so far been very rare. The current approach exploits covert shifts of attention to auditory stimuli in a dichotic-listening stimulus design. To compare the efficacy of event-related potentials (ERPs) and steady-state auditory evoked potentials (SSAEPs), the stimuli were designed such that they elicited both ERPs and SSAEPs simultaneously. Trial-by-trial feedback was provided online, based on subjects' modulation of N1 and P3 ERP components measured during single 5 s stimulation intervals. All 13 healthy subjects were able to use the BCI, with performance in a binary left/right choice task ranging from 75% to 96% correct across subjects (mean 85%). BCI classification was based on the contrast between stimuli in the attended stream and stimuli in the unattended stream, making use of every stimulus, rather than contrasting frequent standard and rare ‘oddball’ stimuli. SSAEPs were assessed offline: for all subjects, spectral components at the two exactly known modulation frequencies allowed discrimination of pre-stimulus from stimulus intervals, and of left-only stimuli from right-only stimuli when one side of the dichotic stimulus pair was muted. However, attention modulation of SSAEPs was not sufficient for single-trial BCI communication, even when the subject's attention was clearly focused well enough to allow classification of the same trials via ERPs. ERPs clearly provided a superior basis for BCI. The ERP results are a promising step towards the development of a simple-to-use, reliable yes/no communication system for users in the most severely paralysed states, as well as potential attention-monitoring and -training applications outside the context of assistive technology.

Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification.

PubMed

Spinnato, J; Roubaud, M-C; Burle, B; Torrésani, B

2015-06-01

The main goal of this work is to develop a model for multisensor signals, such as magnetoencephalography or electroencephalography (EEG) signals that account for inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI-type experiments. The method involves the linear mixed effects statistical model, wavelet transform, and spatial filtering, and aims at the characterization of localized discriminant features in multisensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e., discriminant) and background noise, using a very simple Gaussian linear mixed model. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. The combination of the linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves upon earlier results on similar problems, and the three main ingredients all play an important role.

Classification of diffuse lung diseases: why and how.

PubMed

Hansell, David M

2013-09-01

The understanding of complex lung diseases, notably the idiopathic interstitial pneumonias and small airways diseases, owes as much to repeated attempts over the years to classify them as to any single conceptual breakthrough. One of the many benefits of a successful classification scheme is that it allows workers, within and between disciplines, to be clear that they are discussing the same disease. This may be of particular importance in the recruitment of individuals for a clinical trial that requires a standardized and homogeneous study population. Different specialties require fundamentally different things from a classification: for epidemiologic studies, a classification that requires categorization of individuals according to histopathologic pattern is not usually practicable. Conversely, a scheme that simply divides diffuse parenchymal disease into inflammatory and noninflammatory categories is unlikely to further the understanding about the pathogenesis of disease. Thus, for some disease groupings, for example, pulmonary vasculopathies, there may be several appropriate classifications, each with its merits and demerits. There has been an interesting shift in the past few years, from the accepted primacy of histopathology as the sole basis on which the classification of parenchymal lung disease has rested, to new ways of considering how these entities relate to each other. Some inventive thinking has resulted in new classifications that undoubtedly benefit patients and clinicians in their endeavor to improve management and outcome. The challenge of understanding the logic behind current classifications and their shortcomings are explored in various examples of lung diseases.

Towards psychologically adaptive brain-computer interfaces

NASA Astrophysics Data System (ADS)

Myrden, A.; Chau, T.

2016-12-01

Objective. Brain-computer interface (BCI) performance is sensitive to short-term changes in psychological states such as fatigue, frustration, and attention. This paper explores the design of a BCI that can adapt to these short-term changes. Approach. Eleven able-bodied individuals participated in a study during which they used a mental task-based EEG-BCI to play a simple maze navigation game while self-reporting their perceived levels of fatigue, frustration, and attention. In an offline analysis, a regression algorithm was trained to predict changes in these states, yielding Pearson correlation coefficients in excess of 0.45 between the self-reported and predicted states. Two means of fusing the resultant mental state predictions with mental task classification were investigated. First, single-trial mental state predictions were used to predict correct classification by the BCI during each trial. Second, an adaptive BCI was designed that retrained a new classifier for each testing sample using only those training samples for which predicted mental state was similar to that predicted for the current testing sample. Main results. Mental state-based prediction of BCI reliability exceeded chance levels. The adaptive BCI exhibited significant, but practically modest, increases in classification accuracy for five of 11 participants and no significant difference for the remaining six despite a smaller average training set size. Significance. Collectively, these findings indicate that adaptation to psychological state may allow the design of more accurate BCIs.

Muscle artifacts in single trial EEG data distinguish patients with Parkinson's disease from healthy individuals.

PubMed

Weyhenmeyer, Jonathan; Hernandez, Manuel E; Lainscsek, Claudia; Sejnowski, Terrence J; Poizner, Howard

2014-01-01

Parkinson's disease (PD) is known to lead to marked alterations in cortical-basal ganglia activity that may be amenable to serve as a biomarker for PD diagnosis. Using non-linear delay differential equations (DDE) for classification of PD patients on and off dopaminergic therapy (PD-on, PD-off, respectively) from healthy age-matched controls (CO), we show that 1 second of quasi-resting state clean and raw electroencephalogram (EEG) data can be used to classify CO from PD-on/off based on the area under the receiver operating characteristic curve (AROC). Raw EEG is shown to classify more robustly (AROC=0.59-0.86) than clean EEG data (AROC=0.57-0.72). Decomposition of the raw data into stereotypical and non-stereotypical artifacts provides evidence that increased classification of raw EEG time series originates from muscle artifacts. Thus, non-linear feature extraction and classification of raw EEG data in a low dimensional feature space is a potential biomarker for Parkinson's disease.

Is overall similarity classification less effortful than single-dimension classification?

PubMed

Wills, Andy J; Milton, Fraser; Longmore, Christopher A; Hester, Sarah; Robinson, Jo

2013-01-01

It is sometimes argued that the implementation of an overall similarity classification is less effortful than the implementation of a single-dimension classification. In the current article, we argue that the evidence securely in support of this view is limited, and report additional evidence in support of the opposite proposition--overall similarity classification is more effortful than single-dimension classification. Using a match-to-standards procedure, Experiments 1A, 1B and 2 demonstrate that concurrent load reduces the prevalence of overall similarity classification, and that this effect is robust to changes in the concurrent load task employed, the level of time pressure experienced, and the short-term memory requirements of the classification task. Experiment 3 demonstrates that participants who produced overall similarity classifications from the outset have larger working memory capacities than those who produced single-dimension classifications initially, and Experiment 4 demonstrates that instructions to respond meticulously increase the prevalence of overall similarity classification.

Estimating the Intended Sound Direction of the User: Toward an Auditory Brain-Computer Interface Using Out-of-Head Sound Localization

PubMed Central

Nambu, Isao; Ebisawa, Masashi; Kogure, Masumi; Yano, Shohei; Hokari, Haruhide; Wada, Yasuhiro

2013-01-01

The auditory Brain-Computer Interface (BCI) using electroencephalograms (EEG) is a subject of intensive study. As a cue, auditory BCIs can deal with many of the characteristics of stimuli such as tone, pitch, and voices. Spatial information on auditory stimuli also provides useful information for a BCI. However, in a portable system, virtual auditory stimuli have to be presented spatially through earphones or headphones, instead of loudspeakers. We investigated the possibility of an auditory BCI using the out-of-head sound localization technique, which enables us to present virtual auditory stimuli to users from any direction, through earphones. The feasibility of a BCI using this technique was evaluated in an EEG oddball experiment and offline analysis. A virtual auditory stimulus was presented to the subject from one of six directions. Using a support vector machine, we were able to classify whether the subject attended the direction of a presented stimulus from EEG signals. The mean accuracy across subjects was 70.0% in the single-trial classification. When we used trial-averaged EEG signals as inputs to the classifier, the mean accuracy across seven subjects reached 89.5% (for 10-trial averaging). Further analysis showed that the P300 event-related potential responses from 200 to 500 ms in central and posterior regions of the brain contributed to the classification. In comparison with the results obtained from a loudspeaker experiment, we confirmed that stimulus presentation by out-of-head sound localization achieved similar event-related potential responses and classification performances. These results suggest that out-of-head sound localization enables us to provide a high-performance and loudspeaker-less portable BCI system. PMID:23437338

The Effects of Specialization and Sex on Anterior Y-Balance Performance in High School Athletes.

PubMed

Miller, Madeline M; Trapp, Jessica L; Post, Eric G; Trigsted, Stephanie M; McGuine, Timothy A; Brooks, M Alison; Bell, David R

Sport specialization and movement asymmetry have been separately discussed as potential risk factors for lower extremity injury. Early specialization may lead to the development of movement asymmetries that can predispose an athlete to injury, but this has not been thoroughly examined. Athletes rated as specialized would exhibit greater between-limb anterior reach asymmetry and decreased anterior reach distance on the Y-balance test (YBT) as compared with nonspecialized high school athletes, and these differences would not be dependent on sex. Cross-sectional study. Level 3. Two hundred ninety-five athletes (117 male, 178 female; mean age, 15.6 ± 1.2 years) from 2 local high schools participating in basketball, soccer, volleyball, and tennis responded to a questionnaire regarding sport specialization status and performed trials of the YBT during preseason testing. Specialization was categorized according to 3 previously utilized specialization classification methods (single/multisport, 3-point scale, and 6-point scale), and interactions between specialization and sex with Y-balance performance were calculated using 2-way analyses of variance. Single-sport male athletes displayed greater anterior reach asymmetry than other interaction groups. A consistent main effect was observed for sex, with men displaying greater anterior asymmetry and decreased anterior reach distance than women. However, the interaction effects of specialization and sex on anterior Y-balance performance varied based on the classification method used. Single-sport male athletes displayed greater anterior reach asymmetry on the YBT than multisport and female athletes. Specialization classification method is important because the 6- and 3-point scales may not accurately identify balance abnormalities. Male athletes performed worse than female athletes on both of the Y-balance tasks. Clinicians should be aware that single-sport male athletes may display deficits in dynamic balance, potentially increasing their risk of injury.

Identifying auditory attention with ear-EEG: cEEGrid versus high-density cap-EEG comparison

NASA Astrophysics Data System (ADS)

Bleichner, Martin G.; Mirkovic, Bojana; Debener, Stefan

2016-12-01

Objective. This study presents a direct comparison of a classical EEG cap setup with a new around-the-ear electrode array (cEEGrid) to gain a better understanding of the potential of ear-centered EEG. Approach. Concurrent EEG was recorded from a classical scalp EEG cap and two cEEGrids that were placed around the left and the right ear. Twenty participants performed a spatial auditory attention task in which three sound streams were presented simultaneously. The sound streams were three seconds long and differed in the direction of origin (front, left, right) and the number of beats (3, 4, 5 respectively), as well as the timbre and pitch. The participants had to attend to either the left or the right sound stream. Main results. We found clear attention modulated ERP effects reflecting the attended sound stream for both electrode setups, which agreed in morphology and effect size. A single-trial template matching classification showed that the direction of attention could be decoded significantly above chance (50%) for at least 16 out of 20 participants for both systems. The comparably high classification results of the single trial analysis underline the quality of the signal recorded with the cEEGrids. Significance. These findings are further evidence for the feasibility of around the-ear EEG recordings and demonstrate that well described ERPs can be measured. We conclude that concealed behind-the-ear EEG recordings can be an alternative to classical cap EEG acquisition for auditory attention monitoring.

Identifying auditory attention with ear-EEG: cEEGrid versus high-density cap-EEG comparison.

PubMed

Bleichner, Martin G; Mirkovic, Bojana; Debener, Stefan

2016-12-01

This study presents a direct comparison of a classical EEG cap setup with a new around-the-ear electrode array (cEEGrid) to gain a better understanding of the potential of ear-centered EEG. Concurrent EEG was recorded from a classical scalp EEG cap and two cEEGrids that were placed around the left and the right ear. Twenty participants performed a spatial auditory attention task in which three sound streams were presented simultaneously. The sound streams were three seconds long and differed in the direction of origin (front, left, right) and the number of beats (3, 4, 5 respectively), as well as the timbre and pitch. The participants had to attend to either the left or the right sound stream. We found clear attention modulated ERP effects reflecting the attended sound stream for both electrode setups, which agreed in morphology and effect size. A single-trial template matching classification showed that the direction of attention could be decoded significantly above chance (50%) for at least 16 out of 20 participants for both systems. The comparably high classification results of the single trial analysis underline the quality of the signal recorded with the cEEGrids. These findings are further evidence for the feasibility of around the-ear EEG recordings and demonstrate that well described ERPs can be measured. We conclude that concealed behind-the-ear EEG recordings can be an alternative to classical cap EEG acquisition for auditory attention monitoring.

[Diagnostic criteria and risk assessment of complications after gastric cancer surgery in western countries].

PubMed

Wu, Zhouqiao; Wang, Qi; Shi, Jinyao; Cherry, Koh; Desiderio, Jacopo; Li, Ziyu; Ji, Jiafu

2017-02-25

Postoperative complications are important outcome measurements for surgical quality and safety control. However, the complication registration has always been problematic due to the lack of definition consensus and the other practical difficulties. This narrative review summarizes the data registry system for single institutional registry, national data registry, international multi-center trial registries in the western world, aiming to share the experience of complication classification and data registration. We interviewed Dr. Koh from Royal Prince Alfred Hospital in Australia for single institutional experience, Dr. van der Wielen and Dr. Desideriofor, from two international multi-center trial(STOMACH) and registry (IMIGASTRIC) respectively, and Prof. Dr. Wijnhoven from the Dutch Upper GI Audit(DUCA). The major questions include which complications are obligated to report in the respective registry, what are the definitions of those complications, who perform the registration, and how are the complications evaluated or classified. Four telephone conferences were initiated to discuss the above-mentioned topics. The DUCA and IMGASTRIC provided the definition of the major complications. The consent definition provided by DUCA was based on the LOW classification which came out after a four-year discussion and consensus meeting among international experts in the according field. However, none of the four registries asked for an obligatory standardization of the diagnostic criteria among the participating centers or surgeons. Instead, all the registries required a detailed recording of the diagnostic strategy and classification of the complications with the Clavien-Dindo scoring system. Most data were registered by surgeons or data managers during or immediately after the hospitalization. The investigators or an independent third party conducted the auditing of the data quality. Standardization of complication diagnosis among different centers is a difficult task, consuming much effort and time. On top of that, standardization of the complication registration is of critical and practical importance. We encourage all centers to register complications with the diagnostic criteria and following intervention. Based on this, the Clavien-Dindo classification can be properly justified, which has been widely accepted by most centers and should be routinely used as the standard evaluation system for postoperative complications in gastric tumor surgery.

Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns.

PubMed

Liao, Shih-Cheng; Wu, Chien-Te; Huang, Hao-Chuan; Cheng, Wei-Teng; Liu, Yi-Hung

2017-06-14

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.

Non-target adjacent stimuli classification improves performance of classical ERP-based brain computer interface

NASA Astrophysics Data System (ADS)

Ceballos, G. A.; Hernández, L. F.

2015-04-01

Objective. The classical ERP-based speller, or P300 Speller, is one of the most commonly used paradigms in the field of Brain Computer Interfaces (BCI). Several alterations to the visual stimuli presentation system have been developed to avoid unfavorable effects elicited by adjacent stimuli. However, there has been little, if any, regard to useful information contained in responses to adjacent stimuli about spatial location of target symbols. This paper aims to demonstrate that combining the classification of non-target adjacent stimuli with standard classification (target versus non-target) significantly improves classical ERP-based speller efficiency. Approach. Four SWLDA classifiers were trained and combined with the standard classifier: the lower row, upper row, right column and left column classifiers. This new feature extraction procedure and the classification method were carried out on three open databases: the UAM P300 database (Universidad Autonoma Metropolitana, Mexico), BCI competition II (dataset IIb) and BCI competition III (dataset II). Main results. The inclusion of the classification of non-target adjacent stimuli improves target classification in the classical row/column paradigm. A gain in mean single trial classification of 9.6% and an overall improvement of 25% in simulated spelling speed was achieved. Significance. We have provided further evidence that the ERPs produced by adjacent stimuli present discriminable features, which could provide additional information about the spatial location of intended symbols. This work promotes the searching of information on the peripheral stimulation responses to improve the performance of emerging visual ERP-based spellers.

Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS.

PubMed

Herff, Christian; Heger, Dominic; Fortmann, Ole; Hennrich, Johannes; Putze, Felix; Schultz, Tanja

2013-01-01

When interacting with technical systems, users experience mental workload. Particularly in multitasking scenarios (e.g., interacting with the car navigation system while driving) it is desired to not distract the users from their primary task. For such purposes, human-machine interfaces (HCIs) are desirable which continuously monitor the users' workload and dynamically adapt the behavior of the interface to the measured workload. While memory tasks have been shown to elicit hemodynamic responses in the brain when averaging over multiple trials, a robust single trial classification is a crucial prerequisite for the purpose of dynamically adapting HCIs to the workload of its user. The prefrontal cortex (PFC) plays an important role in the processing of memory and the associated workload. In this study of 10 subjects, we used functional Near-Infrared Spectroscopy (fNIRS), a non-invasive imaging modality, to sample workload activity in the PFC. The results show up to 78% accuracy for single-trial discrimination of three levels of workload from each other. We use an n-back task (n ∈ {1, 2, 3}) to induce different levels of workload, forcing subjects to continuously remember the last one, two, or three of rapidly changing items. Our experimental results show that measuring hemodynamic responses in the PFC with fNIRS, can be used to robustly quantify and classify mental workload. Single trial analysis is still a young field that suffers from a general lack of standards. To increase comparability of fNIRS methods and results, the data corpus for this study is made available online.

Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS

PubMed Central

Herff, Christian; Heger, Dominic; Fortmann, Ole; Hennrich, Johannes; Putze, Felix; Schultz, Tanja

2014-01-01

When interacting with technical systems, users experience mental workload. Particularly in multitasking scenarios (e.g., interacting with the car navigation system while driving) it is desired to not distract the users from their primary task. For such purposes, human-machine interfaces (HCIs) are desirable which continuously monitor the users' workload and dynamically adapt the behavior of the interface to the measured workload. While memory tasks have been shown to elicit hemodynamic responses in the brain when averaging over multiple trials, a robust single trial classification is a crucial prerequisite for the purpose of dynamically adapting HCIs to the workload of its user. The prefrontal cortex (PFC) plays an important role in the processing of memory and the associated workload. In this study of 10 subjects, we used functional Near-Infrared Spectroscopy (fNIRS), a non-invasive imaging modality, to sample workload activity in the PFC. The results show up to 78% accuracy for single-trial discrimination of three levels of workload from each other. We use an n-back task (n ∈ {1, 2, 3}) to induce different levels of workload, forcing subjects to continuously remember the last one, two, or three of rapidly changing items. Our experimental results show that measuring hemodynamic responses in the PFC with fNIRS, can be used to robustly quantify and classify mental workload. Single trial analysis is still a young field that suffers from a general lack of standards. To increase comparability of fNIRS methods and results, the data corpus for this study is made available online. PMID:24474913

A taxonomy has been developed for outcomes in medical research to help improve knowledge discovery.

PubMed

Dodd, Susanna; Clarke, Mike; Becker, Lorne; Mavergames, Chris; Fish, Rebecca; Williamson, Paula R

2018-04-01

There is increasing recognition that insufficient attention has been paid to the choice of outcomes measured in clinical trials. The lack of a standardized outcome classification system results in inconsistencies due to ambiguity and variation in how outcomes are described across different studies. Being able to classify by outcome would increase efficiency in searching sources such as clinical trial registries, patient registries, the Cochrane Database of Systematic Reviews, and the Core Outcome Measures in Effectiveness Trials (COMET) database of core outcome sets (COS), thus aiding knowledge discovery. A literature review was carried out to determine existing outcome classification systems, none of which were sufficiently comprehensive or granular for classification of all potential outcomes from clinical trials. A new taxonomy for outcome classification was developed, and as proof of principle, outcomes extracted from all published COS in the COMET database, selected Cochrane reviews, and clinical trial registry entries were classified using this new system. Application of this new taxonomy to COS in the COMET database revealed that 274/299 (92%) COS include at least one physiological outcome, whereas only 177 (59%) include at least one measure of impact (global quality of life or some measure of functioning) and only 105 (35%) made reference to adverse events. This outcome taxonomy will be used to annotate outcomes included in COS within the COMET database and is currently being piloted for use in Cochrane Reviews within the Cochrane Linked Data Project. Wider implementation of this standard taxonomy in trial and systematic review databases and registries will further promote efficient searching, reporting, and classification of trial outcomes. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Intersession consistency of single-trial classification of the prefrontal response to mental arithmetic and the no-control state by NIRS.

PubMed

Power, Sarah D; Kushki, Azadeh; Chau, Tom

2012-01-01

Near-infrared spectroscopy (NIRS) has been recently investigated for use in noninvasive brain-computer interface (BCI) technologies. Previous studies have demonstrated the ability to classify patterns of neural activation associated with different mental tasks (e.g., mental arithmetic) using NIRS signals. Though these studies represent an important step towards the realization of an NIRS-BCI, there is a paucity of literature regarding the consistency of these responses, and the ability to classify them on a single-trial basis, over multiple sessions. This is important when moving out of an experimental context toward a practical system, where performance must be maintained over longer periods. When considering response consistency across sessions, two questions arise: 1) can the hemodynamic response to the activation task be distinguished from a baseline (or other task) condition, consistently across sessions, and if so, 2) are the spatiotemporal characteristics of the response which best distinguish it from the baseline (or other task) condition consistent across sessions. The answers will have implications for the viability of an NIRS-BCI system, and the design strategies (especially in terms of classifier training protocols) adopted. In this study, we investigated the consistency of classification of a mental arithmetic task and a no-control condition over five experimental sessions. Mixed model linear regression on intrasession classification accuracies indicate that the task and baseline states remain differentiable across multiple sessions, with no significant decrease in accuracy (p = 0.67). Intersession analysis, however, revealed inconsistencies in spatiotemporal response characteristics. Based on these results, we investigated several different practical classifier training protocols, including scenarios in which the training and test data come from 1) different sessions, 2) the same session, and 3) a combination of both. Results indicate that when selecting optimal classifier training protocols for NIRS-BCI, a compromise between accuracy and convenience (e.g., in terms of duration/frequency of training data collection) must be considered.

Comparison promotes learning and transfer of relational categories.

PubMed

Kurtz, Kenneth J; Boukrina, Olga; Gentner, Dedre

2013-07-01

We investigated the effect of co-presenting training items during supervised classification learning of novel relational categories. Strong evidence exists that comparison induces a structural alignment process that renders common relational structure more salient. We hypothesized that comparisons between exemplars would facilitate learning and transfer of categories that cohere around a common relational property. The effect of comparison was investigated using learning trials that elicited a separate classification response for each item in presentation pairs that could be drawn from the same or different categories. This methodology ensures consideration of both items and invites comparison through an implicit same-different judgment inherent in making the two responses. In a test phase measuring learning and transfer, the comparison group significantly outperformed a control group receiving an equivalent training session of single-item classification learning. Comparison-based learners also outperformed the control group on a test of far transfer, that is, the ability to accurately classify items from a novel domain that was relationally alike, but surface-dissimilar, to the training materials. Theoretical and applied implications of this comparison advantage are discussed. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Effectiveness of aquatic versus land physiotherapy in the treatment of peripheral neuropathies: a randomized controlled trial.

PubMed

Zivi, Ilaria; Maffia, Sara; Ferrari, Vanessa; Zarucchi, Alessio; Molatore, Katia; Maestri, Roberto; Frazzitta, Giuseppe

2018-05-01

To compare the effects on gait and balance of aquatic physiotherapy versus on-land training, in the context of an inpatient rehabilitation treatment tailored for peripheral neuropathies. Parallel-group, single-center, single-blind randomized controlled trial. Consecutive patients affected by peripheral neuropathy admitted in our Neuro-Rehabilitation Unit. Patients received a four-week rehabilitation program composed by daily sessions of conventional physiotherapy and three sessions/week of specific treatment (aquatic vs. on-land). Primary outcome measures were Berg Balance Scale and Dynamic Gait Index. Secondary outcome measures were Neuropathic Pain Scale, Overall Neuropathy Limitations Scale, Functional Independence Measure, Functional Ambulation Classification, Conley Scale and Medical Research Council Scale score for the strength of hip and ankle flexor and extensor muscles. For each scale, we calculated the difference between the scores at discharge and admission and compared it between the two groups. Forty patients were enrolled: 21 in the water-based rehabilitation group and 19 in the land-based one. Patients were similar between groups. When comparing the groups, we found that "in-water" patients had a significant better improvement in the Dynamic Gait Index score (6.00 (4.00, 7.25) vs. 4.00 (1.25, 6.00), P = 0.0433). On the opposite, the "on-land" group showed a better improvement of the Functional Ambulation Classification score (1.0 (0.75, 1.0) vs. 1.0 (1.0, 2.0), P = 0.0386). Aquatic physiotherapy showed an effect comparable to the land-based rehabilitation on gait and balance dysfunctions of neuropathic patients.

Distributed Human Intelligence for Colonic Polyp Classification in Computer-aided Detection for CT Colonography

PubMed Central

Nguyen, Tan B.; Wang, Shijun; Anugu, Vishal; Rose, Natalie; McKenna, Matthew; Petrick, Nicholas; Burns, Joseph E.

2012-01-01

Purpose: To assess the diagnostic performance of distributed human intelligence for the classification of polyp candidates identified with computer-aided detection (CAD) for computed tomographic (CT) colonography. Materials and Methods: This study was approved by the institutional Office of Human Subjects Research. The requirement for informed consent was waived for this HIPAA-compliant study. CT images from 24 patients, each with at least one polyp of 6 mm or larger, were analyzed by using CAD software to identify 268 polyp candidates. Twenty knowledge workers (KWs) from a crowdsourcing platform labeled each polyp candidate as a true or false polyp. Two trials involving 228 KWs were conducted to assess reproducibility. Performance was assessed by comparing the area under the receiver operating characteristic curve (AUC) of KWs with the AUC of CAD for polyp classification. Results: The detection-level AUC for KWs was 0.845 ± 0.045 (standard error) in trial 1 and 0.855 ± 0.044 in trial 2. These were not significantly different from the AUC for CAD, which was 0.859 ± 0.043. When polyp candidates were stratified by difficulty, KWs performed better than CAD on easy detections; AUCs were 0.951 ± 0.032 in trial 1, 0.966 ± 0.027 in trial 2, and 0.877 ± 0.048 for CAD (P = .039 for trial 2). KWs who participated in both trials showed a significant improvement in performance going from trial 1 to trial 2; AUCs were 0.759 ± 0.052 in trial 1 and 0.839 ± 0.046 in trial 2 (P = .041). Conclusion: The performance of distributed human intelligence is not significantly different from that of CAD for colonic polyp classification. © RSNA, 2012 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11110938/-/DC1 PMID:22274839

The Design of Cluster Randomized Trials with Random Cross-Classifications

ERIC Educational Resources Information Center

Moerbeek, Mirjam; Safarkhani, Maryam

2018-01-01

Data from cluster randomized trials do not always have a pure hierarchical structure. For instance, students are nested within schools that may be crossed by neighborhoods, and soldiers are nested within army units that may be crossed by mental health-care professionals. It is important that the random cross-classification is taken into account…

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP.

PubMed

Winkler, Irene; Debener, Stefan; Müller, Klaus-Robert; Tangermann, Michael

2015-01-01

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

Short-lived brain state after cued motor imagery in naive subjects.

PubMed

Pfurtscheller, G; Scherer, R; Müller-Putz, G R; Lopes da Silva, F H

2008-10-01

Multi-channel electroencephalography recordings have shown that a visual cue, indicating right hand, left hand or foot motor imagery, can induce a short-lived brain state in the order of about 500 ms. In the present study, 10 able-bodied subjects without any motor imagery experience (naive subjects) were asked to imagine the indicated limb movement for some seconds. Common spatial filtering and linear single-trial classification was applied to discriminate between two conditions (two brain states: right hand vs. left hand, left hand vs. foot and right hand vs. foot). The corresponding classification accuracies (mean +/- SD) were 80.0 +/- 10.6%, 83.3 +/- 10.2% and 83.6 +/- 8.8%, respectively. Inspection of central mu and beta rhythms revealed a short-lasting somatotopically specific event-related desynchronization (ERD) in the upper mu and/or beta bands starting approximately 300 ms after the cue onset and lasting for less than 1 s.

Comparison of Single and Multi-Scale Method for Leaf and Wood Points Classification from Terrestrial Laser Scanning Data

NASA Astrophysics Data System (ADS)

Wei, Hongqiang; Zhou, Guiyun; Zhou, Junjie

2018-04-01

The classification of leaf and wood points is an essential preprocessing step for extracting inventory measurements and canopy characterization of trees from the terrestrial laser scanning (TLS) data. The geometry-based approach is one of the widely used classification method. In the geometry-based method, it is common practice to extract salient features at one single scale before the features are used for classification. It remains unclear how different scale(s) used affect the classification accuracy and efficiency. To assess the scale effect on the classification accuracy and efficiency, we extracted the single-scale and multi-scale salient features from the point clouds of two oak trees of different sizes and conducted the classification on leaf and wood. Our experimental results show that the balanced accuracy of the multi-scale method is higher than the average balanced accuracy of the single-scale method by about 10 % for both trees. The average speed-up ratio of single scale classifiers over multi-scale classifier for each tree is higher than 30.

The Landscape of Clinical Trials Evaluating the Theranostic Role of PET Imaging in Oncology: Insights from an Analysis of ClinicalTrials.gov Database.

PubMed

Chen, Yu-Pei; Lv, Jia-Wei; Liu, Xu; Zhang, Yuan; Guo, Ying; Lin, Ai-Hua; Sun, Ying; Mao, Yan-Ping; Ma, Jun

2017-01-01

In the war on cancer marked by personalized medicine, positron emission tomography (PET)-based theranostic strategy is playing an increasingly important role. Well-designed clinical trials are of great significance for validating the PET applications and ensuring evidence-based cancer care. This study aimed to provide a comprehensive landscape of the characteristics of PET clinical trials using the substantial resource of ClinicalTrials.gov database. We identified 25,599 oncology trials registered with ClinicalTrials.gov in the last ten-year period (October 2005-September 2015). They were systematically reviewed to validate classification into 519 PET trials and 25,080 other oncology trials used for comparison. We found that PET trials were predominantly phase 1-2 studies (86.2%) and were more likely to be single-arm (78.9% vs. 57.9%, P <0.001) using non-randomized assignment (90.1% vs. 66.7%, P <0.001) than other oncology trials. Furthermore, PET trials were small in scale, generally enrolling fewer than 100 participants (20.3% vs. 25.7% for other oncology trials, P = 0.014), which might be too small to detect a significant theranostic effect. The funding support from industry or National Institutes of Health shrunk over time (both decreased by about 5%), and PET trials were more likely to be conducted in only one region lacking international collaboration (97.0% vs. 89.3% for other oncology trials, P <0.001). These findings raise concerns that clinical trials evaluating PET imaging in oncology are not receiving the attention or efforts necessary to generate high-quality evidence. Advancing the clinical application of PET imaging will require a concerted effort to improve the quality of trials.

The Landscape of Clinical Trials Evaluating the Theranostic Role of PET Imaging in Oncology: Insights from an Analysis of ClinicalTrials.gov Database

PubMed Central

Chen, Yu-Pei; Lv, Jia-Wei; Liu, Xu; Zhang, Yuan; Guo, Ying; Lin, Ai-Hua; Sun, Ying; Mao, Yan-Ping; Ma, Jun

2017-01-01

In the war on cancer marked by personalized medicine, positron emission tomography (PET)-based theranostic strategy is playing an increasingly important role. Well-designed clinical trials are of great significance for validating the PET applications and ensuring evidence-based cancer care. This study aimed to provide a comprehensive landscape of the characteristics of PET clinical trials using the substantial resource of ClinicalTrials.gov database. We identified 25,599 oncology trials registered with ClinicalTrials.gov in the last ten-year period (October 2005-September 2015). They were systematically reviewed to validate classification into 519 PET trials and 25,080 other oncology trials used for comparison. We found that PET trials were predominantly phase 1-2 studies (86.2%) and were more likely to be single-arm (78.9% vs. 57.9%, P <0.001) using non-randomized assignment (90.1% vs. 66.7%, P <0.001) than other oncology trials. Furthermore, PET trials were small in scale, generally enrolling fewer than 100 participants (20.3% vs. 25.7% for other oncology trials, P = 0.014), which might be too small to detect a significant theranostic effect. The funding support from industry or National Institutes of Health shrunk over time (both decreased by about 5%), and PET trials were more likely to be conducted in only one region lacking international collaboration (97.0% vs. 89.3% for other oncology trials, P <0.001). These findings raise concerns that clinical trials evaluating PET imaging in oncology are not receiving the attention or efforts necessary to generate high-quality evidence. Advancing the clinical application of PET imaging will require a concerted effort to improve the quality of trials. PMID:28042342

Classification of Types of Stuttering Symptoms Based on Brain Activity

PubMed Central

Jiang, Jing; Lu, Chunming; Peng, Danling; Zhu, Chaozhe; Howell, Peter

2012-01-01

Among the non-fluencies seen in speech, some are more typical (MT) of stuttering speakers, whereas others are less typical (LT) and are common to both stuttering and fluent speakers. No neuroimaging work has evaluated the neural basis for grouping these symptom types. Another long-debated issue is which type (LT, MT) whole-word repetitions (WWR) should be placed in. In this study, a sentence completion task was performed by twenty stuttering patients who were scanned using an event-related design. This task elicited stuttering in these patients. Each stuttered trial from each patient was sorted into the MT or LT types with WWR put aside. Pattern classification was employed to train a patient-specific single trial model to automatically classify each trial as MT or LT using the corresponding fMRI data. This model was then validated by using test data that were independent of the training data. In a subsequent analysis, the classification model, just established, was used to determine which type the WWR should be placed in. The results showed that the LT and the MT could be separated with high accuracy based on their brain activity. The brain regions that made most contribution to the separation of the types were: the left inferior frontal cortex and bilateral precuneus, both of which showed higher activity in the MT than in the LT; and the left putamen and right cerebellum which showed the opposite activity pattern. The results also showed that the brain activity for WWR was more similar to that of the LT and fluent speech than to that of the MT. These findings provide a neurological basis for separating the MT and the LT types, and support the widely-used MT/LT symptom grouping scheme. In addition, WWR play a similar role as the LT, and thus should be placed in the LT type. PMID:22761887

The Effects of Specialization and Sex on Anterior Y-Balance Performance in High School Athletes

PubMed Central

Miller, Madeline M.; Trapp, Jessica L.; Post, Eric G.; Trigsted, Stephanie M.; McGuine, Timothy A.; Brooks, M. Alison; Bell, David R.

2017-01-01

Background: Sport specialization and movement asymmetry have been separately discussed as potential risk factors for lower extremity injury. Early specialization may lead to the development of movement asymmetries that can predispose an athlete to injury, but this has not been thoroughly examined. Hypothesis: Athletes rated as specialized would exhibit greater between-limb anterior reach asymmetry and decreased anterior reach distance on the Y-balance test (YBT) as compared with nonspecialized high school athletes, and these differences would not be dependent on sex. Study Design: Cross-sectional study. Level of Evidence: Level 3. Methods: Two hundred ninety-five athletes (117 male, 178 female; mean age, 15.6 ± 1.2 years) from 2 local high schools participating in basketball, soccer, volleyball, and tennis responded to a questionnaire regarding sport specialization status and performed trials of the YBT during preseason testing. Specialization was categorized according to 3 previously utilized specialization classification methods (single/multisport, 3-point scale, and 6-point scale), and interactions between specialization and sex with Y-balance performance were calculated using 2-way analyses of variance. Results: Single-sport male athletes displayed greater anterior reach asymmetry than other interaction groups. A consistent main effect was observed for sex, with men displaying greater anterior asymmetry and decreased anterior reach distance than women. However, the interaction effects of specialization and sex on anterior Y-balance performance varied based on the classification method used. Conclusion: Single-sport male athletes displayed greater anterior reach asymmetry on the YBT than multisport and female athletes. Specialization classification method is important because the 6- and 3-point scales may not accurately identify balance abnormalities. Male athletes performed worse than female athletes on both of the Y-balance tasks. Clinical Relevance: Clinicians should be aware that single-sport male athletes may display deficits in dynamic balance, potentially increasing their risk of injury. PMID:28447871

Characteristics of oncology clinical trials: insights from a systematic analysis of ClinicalTrials.gov.

PubMed

Hirsch, Bradford R; Califf, Robert M; Cheng, Steven K; Tasneem, Asba; Horton, John; Chiswell, Karen; Schulman, Kevin A; Dilts, David M; Abernethy, Amy P

2013-06-10

Clinical trials are essential to cancer care, and data about the current state of research in oncology are needed to develop benchmarks and set the stage for improvement. To perform a comprehensive analysis of the national oncology clinical research portfolio. All interventional clinical studies registered on ClinicalTrials.gov between October 2007 and September 2010 were identified using Medical Subject Heading terms and submitted conditions. They were reviewed to validate classification, subcategorized by cancer type, and stratified by design characteristics to facilitate comparison across cancer types and with other specialties. Of 40 970 interventional studies registered between October 2007 and September 2010, a total of 8942 (21.8%) focused on oncology. Compared with other specialties, oncology trials were more likely to be single arm (62.3% vs 23.8%; P < .001), open label (87.8% vs 47.3%; P < .001), and nonrandomized (63.9% vs 22.7%; P < .001). There was moderate but significant correlation between number of trials conducted by cancer type and associated incidence and mortality (Spearman rank correlation coefficient, 0.56 [P = .04] and 0.77 [P = .001], respectively). More than one-third of all oncology trials were conducted solely outside North America. There are significant variations between clinical trials in oncology and other diseases, as well as among trials within oncology. The differences must be better understood to improve both the impact of cancer research on clinical practice and the use of constrained resources.

From ClinicalTrials.gov trial registry to an analysis-ready database of clinical trial results.

PubMed

Cepeda, M Soledad; Lobanov, Victor; Berlin, Jesse A

2013-04-01

The ClinicalTrials.gov web site provides a convenient interface to look up study results, but it does not allow downloading data in a format that can be readily used for quantitative analyses. To develop a system that automatically downloads study results from ClinicalTrials.gov and provides an interface to retrieve study results in a spreadsheet format ready for analysis. Sherlock(®) identifies studies by intervention, population, or outcome of interest and in seconds creates an analytic database of study results ready for analyses. The outcome classification algorithms used in Sherlock were validated against a classification by an expert. Having a database ready for analysis that can be updated automatically, dramatically extends the utility of the ClinicalTrials.gov trial registry. It increases the speed of comparative research, reduces the need for manual extraction of data, and permits answering a vast array of questions.

Toward Automated Cochlear Implant Fitting Procedures Based on Event-Related Potentials.

PubMed

Finke, Mareike; Billinger, Martin; Büchner, Andreas

Cochlear implants (CIs) restore hearing to the profoundly deaf by direct electrical stimulation of the auditory nerve. To provide an optimal electrical stimulation pattern the CI must be individually fitted to each CI user. To date, CI fitting is primarily based on subjective feedback from the user. However, not all CI users are able to provide such feedback, for example, small children. This study explores the possibility of using the electroencephalogram (EEG) to objectively determine if CI users are able to hear differences in tones presented to them, which has potential applications in CI fitting or closed loop systems. Deviant and standard stimuli were presented to 12 CI users in an active auditory oddball paradigm. The EEG was recorded in two sessions and classification of the EEG data was performed with shrinkage linear discriminant analysis. Also, the impact of CI artifact removal on classification performance and the possibility to reuse a trained classifier in future sessions were evaluated. Overall, classification performance was above chance level for all participants although performance varied considerably between participants. Also, artifacts were successfully removed from the EEG without impairing classification performance. Finally, reuse of the classifier causes only a small loss in classification performance. Our data provide first evidence that EEG can be automatically classified on single-trial basis in CI users. Despite the slightly poorer classification performance over sessions, classifier and CI artifact correction appear stable over successive sessions. Thus, classifier and artifact correction weights can be reused without repeating the set-up procedure in every session, which makes the technique easier applicable. With our present data, we can show successful classification of event-related cortical potential patterns in CI users. In the future, this has the potential to objectify and automate parts of CI fitting procedures.

National Cancer Institute's Precision Medicine Initiatives for the new National Clinical Trials Network.

PubMed

Abrams, Jeffrey; Conley, Barbara; Mooney, Margaret; Zwiebel, James; Chen, Alice; Welch, John J; Takebe, Naoko; Malik, Shakun; McShane, Lisa; Korn, Edward; Williams, Mickey; Staudt, Louis; Doroshow, James

2014-01-01

The promise of precision medicine will only be fully realized if the research community can adapt its clinical trials methodology to study molecularly characterized tumors instead of the traditional histologic classification. Such trials will depend on adequate tissue collection, availability of quality controlled, high throughput molecular assays, and the ability to screen large numbers of tumors to find those with the desired molecular alterations. The National Cancer Institute's (NCI) new National Clinical Trials Network (NCTN) is well positioned to conduct such trials. The NCTN has the ability to seamlessly perform ethics review, register patients, manage data, and deliver investigational drugs across its many sites including both in cities and rural communities, academic centers, and private practices. The initial set of trials will focus on different questions: (1) Exceptional Responders Initiative-why do a minority of patients with solid tumors or lymphoma respond very well to some drugs even if the majority do not?; (2) NCI MATCH trial-can molecular markers predict response to targeted therapies in patients with advanced cancer resistant to standard treatment?; (3) ALCHEMIST trial-will targeted epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) inhibitors improve survival for adenocarcinoma of the lung in the adjuvant setting?; and (4) Lung Cancer Master Protocol trial for advanced squamous cell lung cancer-is there an advantage to developing drugs for small subsets of molecularly characterized tumors in a single, multiarm trial design? These studies will hopefully spawn a new era of treatment trials that will carefully select the tumors that may respond best to investigational therapy.

Chronic daily headache: correlation between the 2004 and the 1988 International Headache Society diagnostic criteria.

PubMed

Bigal, Marcelo E; Tepper, Stewart J; Sheftell, Fred D; Rapoport, Alan M; Lipton, Richard B

2004-01-01

In a previous study, we compared the 1988 International Headache Society (IHS) criteria and the Silberstein-Lipton criteria (S-L) in a subspeciality clinic sample of 638 patients with chronic daily headache (CDH) assessed both clinically and with headache diaries. Both systems allowed for the classification of most patients with CDH. The 1988 IHS classification required multiple diagnoses and was more complex to apply. The aim of this study was to revisit the same database, now comparing the prior classification systems with the new 2004 IHS classification. In contrast with the 1st edition, the 2nd edition includes criteria for chronic migraine (CM), new daily persistent headache (NDPH), and hemicrania continua (HC). We reviewed the clinical records and the headache diaries of 638 patients seen between 1980 and 2001 at a headache center. All patients had primary CDH according to the S-L criteria. Using the S-L criteria as a reference, of the 158 patients with transformed migraine (TM) without medication overuse, just 9 (5.6%) met 2004 IHS criteria for CM. Most of the subjects were classified using combinations of migraine and CTTH diagnoses, much like the 1988 IHS classification. Similarly, using the new IHS system, just 41/399 (10.2%) subjects with TM with medication overuse were classified as probable CM with probable medication overuse. Most patients with NDPH without overuse were easily classified using the 2004 criteria (95.8%). Regarding NDPH with medication overuse, the diagnostic groups were much like results for the 1st edition. All patients with chronic tension-type headache (CTTH) and hemicrania continua (HC) according to the S-L system were easily classified using the 2004 IHS criteria. We conclude that the 2004 IHS criteria facilitate the classification of NDPH without medication overuse and HC. For subjects with TM according to the S-L system, the new IHS criteria are complex to use and require multiple diagnoses. Very few patients with TM in the S-L system could be classified with a single diagnosis in the 2004 IHS classification. In fact, CM was so rare that it would be virtually impossible to conduct clinical trials of this entity using the 2004 IHS criteria. Clinical trials of this entity should therefore be conducted using the S-L criteria. Finally, we propose that in the 3rd edition of the IHS classification, the diagnosis of NDPH be revised so as not to exclude migraine features.

The cost-effectiveness of a treatment-based classification system for low back pain: design of a randomised controlled trial and economic evaluation

PubMed Central

2010-01-01

Background Systematic reviews have shown that exercise therapy and spinal manipulation are both more effective for low back pain (LBP) than no treatment at all. However, the effects are at best modest. To enhance the clinical outcomes, recommendations are to improve the patient selection process, and to identify relevant subgroups to guide clinical decision-making. One of the systems that has potentials to improve clinical decision-making is a treatment-based classification system that is intended to identify those patients who are most likely to respond to direction-specific exercises, manipulation, or stabilisation exercises. Methods/Design The primary aim of this randomised controlled trial will be to assess the effectiveness of a classification-based system. A sample of 150 patients with subacute and chronic LBP who attend a private physical therapy clinic for treatment will be recruited. At baseline, all participants will undergo a standard evaluation by trained research physical therapists and will be classified into one of the following subgroups: direction-specific exercises, manipulation, or stabilisation. The patient will not be informed about the results of the examination. Patients will be randomly assigned to classification-based treatment or usual care according to the Dutch LBP guidelines, and will complete questionnaires at baseline, and 8, 26, and 52 weeks after the start of the treatment. The primary outcomes will be general perceived recovery, functional status, and pain intensity. Alongside this trial, an economic evaluation of cost-effectiveness and cost-utility will be conducted from a societal perspective. Discussion The present study will contribute to our knowledge about the effectiveness and cost-effectiveness of classification-based treatment in patients with LBP. Trial registration Trial registration number: NTR1176 PMID:20346133

Upper limb robot-assisted therapy in cerebral palsy: a single-blind randomized controlled trial.

PubMed

Gilliaux, Maxime; Renders, Anne; Dispa, Delphine; Holvoet, Dominique; Sapin, Julien; Dehez, Bruno; Detrembleur, Christine; Lejeune, Thierry M; Stoquart, Gaëtan

2015-02-01

Several pilot studies have evoked interest in robot-assisted therapy (RAT) in children with cerebral palsy (CP). To assess the effectiveness of RAT in children with CP through a single-blind randomized controlled trial. Sixteen children with CP were randomized into 2 groups. Eight children performed 5 conventional therapy sessions per week over 8 weeks (control group). Eight children completed 3 conventional therapy sessions and 2 robot-assisted sessions per week over 8 weeks (robotic group). For both groups, each therapy session lasted 45 minutes. Throughout each RAT session, the patient attempted to reach several targets consecutively with the REAPlan. The REAPlan is a distal effector robot that allows for displacements of the upper limb in the horizontal plane. A blinded assessment was performed before and after the intervention with respect to the International Classification of Functioning framework: body structure and function (upper limb kinematics, Box and Block test, Quality of Upper Extremity Skills Test, strength, and spasticity), activities (Abilhand-Kids, Pediatric Evaluation of Disability Inventory), and participation (Life Habits). During each RAT session, patients performed 744 movements on average with the REAPlan. Among the variables assessed, the smoothness of movement (P < .01) and manual dexterity assessed by the Box and Block test (P = .04) improved significantly more in the robotic group than in the control group. This single-blind randomized controlled trial provides the first evidence that RAT is effective in children with CP. Future studies should investigate the long-term effects of this therapy. © The Author(s) 2014.

A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses

PubMed Central

Dimitriadis, Stavros I.; Laskaris, Nikolaos A.; Bitzidou, Malamati P.; Tarnanas, Ioannis; Tsolaki, Magda N.

2015-01-01

The detection of mild cognitive impairment (MCI), the transitional stage between normal cognitive changes of aging and the cognitive decline caused by AD, is of paramount clinical importance, since MCI patients are at increased risk of progressing into AD. Electroencephalographic (EEG) alterations in the spectral content of brainwaves and connectivity at resting state have been associated with early-stage AD. Recently, cognitive event-related potentials (ERPs) have entered into the picture as an easy to perform screening test. Motivated by the recent findings about the role of cross-frequency coupling (CFC) in cognition, we introduce a relevant methodological approach for detecting MCI based on cognitive responses from a standard auditory oddball paradigm. By using the single trial signals recorded at Pz sensor and comparing the responses to target and non-target stimuli, we first demonstrate that increased CFC is associated with the cognitive task. Then, considering the dynamic character of CFC, we identify instances during which the coupling between particular pairs of brainwave frequencies carries sufficient information for discriminating between normal subjects and patients with MCI. In this way, we form a multiparametric signature of impaired cognition. The new composite biomarker was tested using data from a cohort that consists of 25 amnestic MCI patients and 15 age-matched controls. Standard machine-learning algorithms were employed so as to implement the binary classification task. Based on leave-one-out cross-validation, the measured classification rate was found reaching very high levels (95%). Our approach compares favorably with the traditional alternative of using the morphology of averaged ERP response to make the diagnosis and the usage of features from spectro-temporal analysis of single-trial responses. This further indicates that task-related CFC measurements can provide invaluable analytics in AD diagnosis and prognosis. PMID:26539070

Stratification of a cityscape using census and land use variables for inventory of building materials

USGS Publications Warehouse

Rosenfield, G.H.; Fitzpatrick-Lins, K.; Johnson, T.L.

1987-01-01

A cityscape (or any landscape) can be stratified into environmental units using multiple variables of information. For the purposes of sampling building materials, census and land use variables were used to identify similar strata. In the Metropolitan Statistical Area of a cityscape, the census tract is the smallest unit for which census data are summarized and digitized boundaries are available. For purposes of this analysis, census data on total population, total number of housing units, and number of singleunit dwellings were aggregated into variables of persons per square kilometer and proportion of housing units in single-unit dwellings. The level 2 categories of the U.S. Geological Survey's land use and land cover data base were aggregated into variables of proportion of residential land with buildings, proportion of nonresidential land with buildings, and proportion of open land. The cityscape was stratified, from these variables, into environmental strata of Urban Central Business District, Urban Livelihood Industrial Commercial, Urban Multi-Family Residential, Urban Single Family Residential, Non-Urban Suburbanizing, and Non-Urban Rural. The New England region was chosen as a region with commonality of building materials, and a procedure developed for trial classification of census tracts into one of the strata. Final stratification was performed by discriminant analysis using the trial classification and prior probabilities as weights. The procedure was applied to several cities, and the results analyzed by correlation analysis from a field sample of building materials. The methodology developed for stratification of a cityscape using multiple variables has application to many other types of environmental studies, including forest inventory, hydrologic unit management, waste disposal, transportation studies, and other urban studies. Multivariate analysis techniques have recently been used for urban stratification in England. ?? 1987 Annals of Regional Science.

Effectiveness of aquatic versus land physiotherapy in the treatment of peripheral neuropathies: a randomized controlled trial

PubMed Central

Zivi, Ilaria; Maffia, Sara; Ferrari, Vanessa; Zarucchi, Alessio; Molatore, Katia; Maestri, Roberto; Frazzitta, Giuseppe

2017-01-01

Objective: To compare the effects on gait and balance of aquatic physiotherapy versus on-land training, in the context of an inpatient rehabilitation treatment tailored for peripheral neuropathies. Design: Parallel-group, single-center, single-blind randomized controlled trial. Subjects and setting: Consecutive patients affected by peripheral neuropathy admitted in our Neuro-Rehabilitation Unit. Interventions: Patients received a four-week rehabilitation program composed by daily sessions of conventional physiotherapy and three sessions/week of specific treatment (aquatic vs. on-land). Main measures: Primary outcome measures were Berg Balance Scale and Dynamic Gait Index. Secondary outcome measures were Neuropathic Pain Scale, Overall Neuropathy Limitations Scale, Functional Independence Measure, Functional Ambulation Classification, Conley Scale and Medical Research Council Scale score for the strength of hip and ankle flexor and extensor muscles. For each scale, we calculated the difference between the scores at discharge and admission and compared it between the two groups. Results: Forty patients were enrolled: 21 in the water-based rehabilitation group and 19 in the land-based one. Patients were similar between groups. When comparing the groups, we found that “in-water” patients had a significant better improvement in the Dynamic Gait Index score (6.00 (4.00, 7.25) vs. 4.00 (1.25, 6.00), P = 0.0433). On the opposite, the “on-land” group showed a better improvement of the Functional Ambulation Classification score (1.0 (0.75, 1.0) vs. 1.0 (1.0, 2.0), P = 0.0386). Conclusion: Aquatic physiotherapy showed an effect comparable to the land-based rehabilitation on gait and balance dysfunctions of neuropathic patients. PMID:29232980

Macrophage Responses to Epithelial Dysfunction Promote Lung Fibrosis in Aging

DTIC Science & Technology

2017-10-01

alveolar macrophages based on single cell molecular classification in patients with pulmonary fibrosis. We have recruited a planned number of patients...biomarkers expressed by human tissue-resident and monocyte-derived alveolar macrophages based on single cell molecular classification in patients with...identify novel biomarkers expressed by human tissue-resident and monocyte- derived alveolar macrophages based on single cell molecular classification

Towards a ternary NIRS-BCI: single-trial classification of verbal fluency task, Stroop task and unconstrained rest

NASA Astrophysics Data System (ADS)

Schudlo, Larissa C.; Chau, Tom

2015-12-01

Objective. The majority of near-infrared spectroscopy (NIRS) brain-computer interface (BCI) studies have investigated binary classification problems. Limited work has considered differentiation of more than two mental states, or multi-class differentiation of higher-level cognitive tasks using measurements outside of the anterior prefrontal cortex. Improvements in accuracies are needed to deliver effective communication with a multi-class NIRS system. We investigated the feasibility of a ternary NIRS-BCI that supports mental states corresponding to verbal fluency task (VFT) performance, Stroop task performance, and unconstrained rest using prefrontal and parietal measurements. Approach. Prefrontal and parietal NIRS signals were acquired from 11 able-bodied adults during rest and performance of the VFT or Stroop task. Classification was performed offline using bagging with a linear discriminant base classifier trained on a 10 dimensional feature set. Main results. VFT, Stroop task and rest were classified at an average accuracy of 71.7% ± 7.9%. The ternary classification system provided a statistically significant improvement in information transfer rate relative to a binary system controlled by either mental task (0.87 ± 0.35 bits/min versus 0.73 ± 0.24 bits/min). Significance. These results suggest that effective communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest via measurements from the frontal and parietal cortices. Further development of such a system is warranted. Accurate ternary classification can enhance communication rates offered by NIRS-BCIs, improving the practicality of this technology.

Single-trial analysis of the neural correlates of speech quality perception.

PubMed

Porbadnigk, Anne K; Treder, Matthias S; Blankertz, Benjamin; Antons, Jan-Niklas; Schleicher, Robert; Möller, Sebastian; Curio, Gabriel; Müller, Klaus-Robert

2013-10-01

Assessing speech quality perception is a challenge typically addressed in behavioral and opinion-seeking experiments. Only recently, neuroimaging methods were introduced, which were used to study the neural processing of quality at group level. However, our electroencephalography (EEG) studies show that the neural correlates of quality perception are highly individual. Therefore, it became necessary to establish dedicated machine learning methods for decoding subject-specific effects. The effectiveness of our methods is shown by the data of an EEG study that investigates how the quality of spoken vowels is processed neurally. Participants were asked to indicate whether they had perceived a degradation of quality (signal-correlated noise) in vowels, presented in an oddball paradigm. We find that the P3 amplitude is attenuated with increasing noise. Single-trial analysis allows one to show that this is partly due to an increasing jitter of the P3 component. A novel classification approach helps to detect trials with presumably non-conscious processing at the threshold of perception. We show that this approach uncovers a non-trivial confounder between neural hits and neural misses. The combined use of EEG signals and machine learning methods results in a significant 'neural' gain in sensitivity (in processing quality loss) when compared to standard behavioral evaluation; averaged over 11 subjects, this amounts to a relative improvement in sensitivity of 35%.

Decoding the individual finger movements from single-trial functional magnetic resonance imaging recordings of human brain activity.

PubMed

Shen, Guohua; Zhang, Jing; Wang, Mengxing; Lei, Du; Yang, Guang; Zhang, Shanmin; Du, Xiaoxia

2014-06-01

Multivariate pattern classification analysis (MVPA) has been applied to functional magnetic resonance imaging (fMRI) data to decode brain states from spatially distributed activation patterns. Decoding upper limb movements from non-invasively recorded human brain activation is crucial for implementing a brain-machine interface that directly harnesses an individual's thoughts to control external devices or computers. The aim of this study was to decode the individual finger movements from fMRI single-trial data. Thirteen healthy human subjects participated in a visually cued delayed finger movement task, and only one slight button press was performed in each trial. Using MVPA, the decoding accuracy (DA) was computed separately for the different motor-related regions of interest. For the construction of feature vectors, the feature vectors from two successive volumes in the image series for a trial were concatenated. With these spatial-temporal feature vectors, we obtained a 63.1% average DA (84.7% for the best subject) for the contralateral primary somatosensory cortex and a 46.0% average DA (71.0% for the best subject) for the contralateral primary motor cortex; both of these values were significantly above the chance level (20%). In addition, we implemented searchlight MVPA to search for informative regions in an unbiased manner across the whole brain. Furthermore, by applying searchlight MVPA to each volume of a trial, we visually demonstrated the information for decoding, both spatially and temporally. The results suggest that the non-invasive fMRI technique may provide informative features for decoding individual finger movements and the potential of developing an fMRI-based brain-machine interface for finger movement. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Quantifying functional mobility progress for chronic disease management.

PubMed

Boyle, Justin; Karunanithi, Mohan; Wark, Tim; Chan, Wilbur; Colavitti, Christine

2006-01-01

A method for quantifying improvements in functional mobility is presented based on patient-worn accelerometer devices. For patients with cardiovascular, respiratory, or other chronic disease, increasing the amount of functional mobility is a large component of rehabilitation programs. We have conducted an observational trial on the use of accelerometers for quantifying mobility improvements in a small group of chronic disease patients (n=15, 48 - 86 yrs). Cognitive impairments precluded complex instrumentation of patients, and movement data was obtained from a single 2-axis accelerometer device worn at the hip. In our trial, movement data collected from accelerometer devices was classified into Lying vs Sitting/Standing vs Walking/Activity movements. This classification enabled the amount of walking to be quantified and graphically presented to clinicians and carers for feedback on exercise efficacy. Presenting long term trends in this data to patients also provides valuable feedback for self managed care and assisting with compliance.

A Randomized Clinical Trial of Cognitive-Behavioral Treatment for PTSD in Women

DTIC Science & Technology

2003-10-01

Post Traumatic Stress Disorder ( PTSD ) in 384 female veterans and active duty personnel at 11 sites. This is a VA Cooperative Study. Walter...14. SUBJECT TERMS 15. NUMBER OF PAGES Post - Traumatic Stress Disorder 6 16. PRICE CODE 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19...Clinical Trial of Cognitive-Behavioral Treatment for Post Traumatic Stress Disorder in Women for this study, from the protocol Additionally, a new

Sequenced subjective accents for brain-computer interfaces

NASA Astrophysics Data System (ADS)

Vlek, R. J.; Schaefer, R. S.; Gielen, C. C. A. M.; Farquhar, J. D. R.; Desain, P.

2011-06-01

Subjective accenting is a cognitive process in which identical auditory pulses at an isochronous rate turn into the percept of an accenting pattern. This process can be voluntarily controlled, making it a candidate for communication from human user to machine in a brain-computer interface (BCI) system. In this study we investigated whether subjective accenting is a feasible paradigm for BCI and how its time-structured nature can be exploited for optimal decoding from non-invasive EEG data. Ten subjects perceived and imagined different metric patterns (two-, three- and four-beat) superimposed on a steady metronome. With an offline classification paradigm, we classified imagined accented from non-accented beats on a single trial (0.5 s) level with an average accuracy of 60.4% over all subjects. We show that decoding of imagined accents is also possible with a classifier trained on perception data. Cyclic patterns of accents and non-accents were successfully decoded with a sequence classification algorithm. Classification performances were compared by means of bit rate. Performance in the best scenario translates into an average bit rate of 4.4 bits min-1 over subjects, which makes subjective accenting a promising paradigm for an online auditory BCI.

EEG alpha spindles and prolonged brake reaction times during auditory distraction in an on-road driving study.

PubMed

Sonnleitner, Andreas; Treder, Matthias Sebastian; Simon, Michael; Willmann, Sven; Ewald, Arne; Buchner, Axel; Schrauf, Michael

2014-01-01

Driver distraction is responsible for a substantial number of traffic accidents. This paper describes the impact of an auditory secondary task on drivers' mental states during a primary driving task. N=20 participants performed the test procedure in a car following task with repeated forced braking on a non-public test track. Performance measures (provoked reaction time to brake lights) and brain activity (EEG alpha spindles) were analyzed to describe distracted drivers. Further, a classification approach was used to investigate whether alpha spindles can predict drivers' mental states. Results show that reaction times and alpha spindle rate increased with time-on-task. Moreover, brake reaction times and alpha spindle rate were significantly higher while driving with auditory secondary task opposed to driving only. In single-trial classification, a combination of spindle parameters yielded a median classification error of about 8% in discriminating the distracted from the alert driving. Reduced driving performance (i.e., prolonged brake reaction times) during increased cognitive load is assumed to be indicated by EEG alpha spindles, enabling the quantification of driver distraction in experiments on public roads without verbally assessing the drivers' mental states. Copyright © 2013 Elsevier Ltd. All rights reserved.

Real-time classification of auditory sentences using evoked cortical activity in humans

NASA Astrophysics Data System (ADS)

Moses, David A.; Leonard, Matthew K.; Chang, Edward F.

2018-06-01

Objective. Recent research has characterized the anatomical and functional basis of speech perception in the human auditory cortex. These advances have made it possible to decode speech information from activity in brain regions like the superior temporal gyrus, but no published work has demonstrated this ability in real-time, which is necessary for neuroprosthetic brain-computer interfaces. Approach. Here, we introduce a real-time neural speech recognition (rtNSR) software package, which was used to classify spoken input from high-resolution electrocorticography signals in real-time. We tested the system with two human subjects implanted with electrode arrays over the lateral brain surface. Subjects listened to multiple repetitions of ten sentences, and rtNSR classified what was heard in real-time from neural activity patterns using direct sentence-level and HMM-based phoneme-level classification schemes. Main results. We observed single-trial sentence classification accuracies of 90% or higher for each subject with less than 7 minutes of training data, demonstrating the ability of rtNSR to use cortical recordings to perform accurate real-time speech decoding in a limited vocabulary setting. Significance. Further development and testing of the package with different speech paradigms could influence the design of future speech neuroprosthetic applications.

Convolutional neural networks for event-related potential detection: impact of the architecture.

PubMed

Cecotti, H

2017-07-01

The detection of brain responses at the single-trial level in the electroencephalogram (EEG) such as event-related potentials (ERPs) is a difficult problem that requires different processing steps to extract relevant discriminant features. While most of the signal and classification techniques for the detection of brain responses are based on linear algebra, different pattern recognition techniques such as convolutional neural network (CNN), as a type of deep learning technique, have shown some interests as they are able to process the signal after limited pre-processing. In this study, we propose to investigate the performance of CNNs in relation of their architecture and in relation to how they are evaluated: a single system for each subject, or a system for all the subjects. More particularly, we want to address the change of performance that can be observed between specifying a neural network to a subject, or by considering a neural network for a group of subjects, taking advantage of a larger number of trials from different subjects. The results support the conclusion that a convolutional neural network trained on different subjects can lead to an AUC above 0.9 by using an appropriate architecture using spatial filtering and shift invariant layers.

EEG-based classification of imaginary left and right foot movements using beta rebound.

PubMed

Hashimoto, Yasunari; Ushiba, Junichi

2013-11-01

The purpose of this study was to investigate cortical lateralization of event-related (de)synchronization during left and right foot motor imagery tasks and to determine classification accuracy of the two imaginary movements in a brain-computer interface (BCI) paradigm. We recorded 31-channel scalp electroencephalograms (EEGs) from nine healthy subjects during brisk imagery tasks of left and right foot movements. EEG was analyzed with time-frequency maps and topographies, and the accuracy rate of classification between left and right foot movements was calculated. Beta rebound at the end of imagination (increase of EEG beta rhythm amplitude) was identified from the two EEGs derived from the right-shift and left-shift bipolar pairs at the vertex. This process enabled discrimination between right or left foot imagery at a high accuracy rate (maximum 81.6% in single trial analysis). These data suggest that foot motor imagery has potential to elicit left-right differences in EEG, while BCI using the unilateral foot imagery can achieve high classification accuracy, similar to ordinary BCI, based on hand motor imagery. By combining conventional discrimination techniques, the left-right discrimination of unilateral foot motor imagery provides a novel BCI system that could control a foot neuroprosthesis or a robotic foot. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Etiological classifications of transient ischemic attacks: subtype classification by TOAST, CCS and ASCO--a pilot study.

PubMed

Amort, Margareth; Fluri, Felix; Weisskopf, Florian; Gensicke, Henrik; Bonati, Leo H; Lyrer, Philippe A; Engelter, Stefan T

2012-01-01

In patients with transient ischemic attacks (TIA), etiological classification systems are not well studied. The Trial of ORG 10172 in Acute Stroke Treatment (TOAST), the Causative Classification System (CCS), and the Atherosclerosis Small Vessel Disease Cardiac Source Other Cause (ASCO) classification may be useful to determine the underlying etiology. We aimed at testing the feasibility of each of the 3 systems. Furthermore, we studied and compared their prognostic usefulness. In a single-center TIA registry prospectively ascertained over 2 years, we applied 3 etiological classification systems. We compared the distribution of underlying etiologies, the rates of patients with determined versus undetermined etiology, and studied whether etiological subtyping distinguished TIA patients with versus without subsequent stroke or TIA within 3 months. The 3 systems were applicable in all 248 patients. A determined etiology with the highest level of causality was assigned similarly often with TOAST (35.9%), CCS (34.3%), and ASCO (38.7%). However, the frequency of undetermined causes differed significantly between the classification systems and was lowest for ASCO (TOAST: 46.4%; CCS: 37.5%; ASCO: 18.5%; p < 0.001). In TOAST, CCS, and ASCO, cardioembolism (19.4/14.5/18.5%) was the most common etiology, followed by atherosclerosis (11.7/12.9/14.5%). At 3 months, 33 patients (13.3%, 95% confidence interval 9.3-18.2%) had recurrent cerebral ischemic events. These were strokes in 13 patients (5.2%; 95% confidence interval 2.8-8.8%) and TIAs in 20 patients (8.1%, 95% confidence interval 5.0-12.2%). Patients with a determined etiology (high level of causality) had higher rates of subsequent strokes than those without a determined etiology [TOAST: 6.7% (95% confidence interval 2.5-14.1%) vs. 4.4% (95% confidence interval 1.8-8.9%); CSS: 9.3% (95% confidence interval 4.1-17.5%) vs. 3.1% (95% confidence interval 1.0-7.1%); ASCO: 9.4% (95% confidence interval 4.4-17.1%) vs. 2.6% (95% confidence interval 0.7-6.6%)]. However, this difference was only significant in the ASCO classification (p = 0.036). Using ASCO, there was neither an increase in risk of subsequent stroke among patients with incomplete diagnostic workup (at least one subtype scored 9) compared with patients with adequate workup (no subtype scored 9), nor among patients with multiple causes compared with patients with a single cause. In TIA patients, all etiological classification systems provided a similar distribution of underlying etiologies. The increase in stroke risk in TIA patients with determined versus undetermined etiology was most evident using the ASCO classification. Copyright © 2012 S. Karger AG, Basel.

Consensus classification of posterior cortical atrophy

PubMed Central

Crutch, Sebastian J.; Schott, Jonathan M.; Rabinovici, Gil D.; Murray, Melissa; Snowden, Julie S.; van der Flier, Wiesje M.; Dickerson, Bradford C.; Vandenberghe, Rik; Ahmed, Samrah; Bak, Thomas H.; Boeve, Bradley F.; Butler, Christopher; Cappa, Stefano F.; Ceccaldi, Mathieu; de Souza, Leonardo Cruz; Dubois, Bruno; Felician, Olivier; Galasko, Douglas; Graff-Radford, Jonathan; Graff-Radford, Neill R.; Hof, Patrick R.; Krolak-Salmon, Pierre; Lehmann, Manja; Magnin, Eloi; Mendez, Mario F.; Nestor, Peter J.; Onyike, Chiadi U.; Pelak, Victoria S.; Pijnenburg, Yolande; Primativo, Silvia; Rossor, Martin N.; Ryan, Natalie S.; Scheltens, Philip; Shakespeare, Timothy J.; González, Aida Suárez; Tang-Wai, David F.; Yong, Keir X. X.; Carrillo, Maria; Fox, Nick C.

2017-01-01

Introduction A classification framework for posterior cortical atrophy (PCA) is proposed to improve the uniformity of definition of the syndrome in a variety of research settings. Methods Consensus statements about PCA were developed through a detailed literature review, the formation of an international multidisciplinary working party which convened on four occasions, and a Web-based quantitative survey regarding symptom frequency and the conceptualization of PCA. Results A three-level classification framework for PCA is described comprising both syndrome- and disease-level descriptions. Classification level 1 (PCA) defines the core clinical, cognitive, and neuroimaging features and exclusion criteria of the clinico-radiological syndrome. Classification level 2 (PCA-pure, PCA-plus) establishes whether, in addition to the core PCA syndrome, the core features of any other neurodegenerative syndromes are present. Classification level 3 (PCA attributable to AD [PCA-AD], Lewy body disease [PCA-LBD], corticobasal degeneration [PCA-CBD], prion disease [PCA-prion]) provides a more formal determination of the underlying cause of the PCA syndrome, based on available pathophysiological biomarker evidence. The issue of additional syndrome-level descriptors is discussed in relation to the challenges of defining stages of syndrome severity and characterizing phenotypic heterogeneity within the PCA spectrum. Discussion There was strong agreement regarding the definition of the core clinico-radiological syndrome, meaning that the current consensus statement should be regarded as a refinement, development, and extension of previous single-center PCA criteria rather than any wholesale alteration or redescription of the syndrome. The framework and terminology may facilitate the interpretation of research data across studies, be applicable across a broad range of research scenarios (e.g., behavioral interventions, pharmacological trials), and provide a foundation for future collaborative work. PMID:28259709

Consensus classification of posterior cortical atrophy.

PubMed

Crutch, Sebastian J; Schott, Jonathan M; Rabinovici, Gil D; Murray, Melissa; Snowden, Julie S; van der Flier, Wiesje M; Dickerson, Bradford C; Vandenberghe, Rik; Ahmed, Samrah; Bak, Thomas H; Boeve, Bradley F; Butler, Christopher; Cappa, Stefano F; Ceccaldi, Mathieu; de Souza, Leonardo Cruz; Dubois, Bruno; Felician, Olivier; Galasko, Douglas; Graff-Radford, Jonathan; Graff-Radford, Neill R; Hof, Patrick R; Krolak-Salmon, Pierre; Lehmann, Manja; Magnin, Eloi; Mendez, Mario F; Nestor, Peter J; Onyike, Chiadi U; Pelak, Victoria S; Pijnenburg, Yolande; Primativo, Silvia; Rossor, Martin N; Ryan, Natalie S; Scheltens, Philip; Shakespeare, Timothy J; Suárez González, Aida; Tang-Wai, David F; Yong, Keir X X; Carrillo, Maria; Fox, Nick C

2017-08-01

A classification framework for posterior cortical atrophy (PCA) is proposed to improve the uniformity of definition of the syndrome in a variety of research settings. Consensus statements about PCA were developed through a detailed literature review, the formation of an international multidisciplinary working party which convened on four occasions, and a Web-based quantitative survey regarding symptom frequency and the conceptualization of PCA. A three-level classification framework for PCA is described comprising both syndrome- and disease-level descriptions. Classification level 1 (PCA) defines the core clinical, cognitive, and neuroimaging features and exclusion criteria of the clinico-radiological syndrome. Classification level 2 (PCA-pure, PCA-plus) establishes whether, in addition to the core PCA syndrome, the core features of any other neurodegenerative syndromes are present. Classification level 3 (PCA attributable to AD [PCA-AD], Lewy body disease [PCA-LBD], corticobasal degeneration [PCA-CBD], prion disease [PCA-prion]) provides a more formal determination of the underlying cause of the PCA syndrome, based on available pathophysiological biomarker evidence. The issue of additional syndrome-level descriptors is discussed in relation to the challenges of defining stages of syndrome severity and characterizing phenotypic heterogeneity within the PCA spectrum. There was strong agreement regarding the definition of the core clinico-radiological syndrome, meaning that the current consensus statement should be regarded as a refinement, development, and extension of previous single-center PCA criteria rather than any wholesale alteration or redescription of the syndrome. The framework and terminology may facilitate the interpretation of research data across studies, be applicable across a broad range of research scenarios (e.g., behavioral interventions, pharmacological trials), and provide a foundation for future collaborative work. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Gender classification of running subjects using full-body kinematics

NASA Astrophysics Data System (ADS)

Williams, Christina M.; Flora, Jeffrey B.; Iftekharuddin, Khan M.

2016-05-01

This paper proposes novel automated gender classification of subjects while engaged in running activity. The machine learning techniques include preprocessing steps using principal component analysis followed by classification with linear discriminant analysis, and nonlinear support vector machines, and decision-stump with AdaBoost. The dataset consists of 49 subjects (25 males, 24 females, 2 trials each) all equipped with approximately 80 retroreflective markers. The trials are reflective of the subject's entire body moving unrestrained through a capture volume at a self-selected running speed, thus producing highly realistic data. The classification accuracy using leave-one-out cross validation for the 49 subjects is improved from 66.33% using linear discriminant analysis to 86.74% using the nonlinear support vector machine. Results are further improved to 87.76% by means of implementing a nonlinear decision stump with AdaBoost classifier. The experimental findings suggest that the linear classification approaches are inadequate in classifying gender for a large dataset with subjects running in a moderately uninhibited environment.

Renoprotection and the Bardoxolone Methyl Story - Is This the Right Way Forward? A Novel View of Renoprotection in CKD Trials: A New Classification Scheme for Renoprotective Agents.

PubMed

Onuigbo, Macaulay

2013-01-01

In the June 2011 issue of the New England Journal of Medicine, the BEAM (Bardoxolone Methyl Treatment: Renal Function in CKD/Type 2 Diabetes) trial investigators rekindled new interest and also some controversy regarding the concept of renoprotection and the role of renoprotective agents, when they reported significant increases in the mean estimated glomerular filtration rate (eGFR) in diabetic chronic kidney disease (CKD) patients with an eGFR of 20-45 ml/min/1.73 m(2) of body surface area at enrollment who received the trial drug bardoxolone methyl versus placebo. Unfortunately, subsequent phase IIIb trials failed to show that the drug is a safe alternative renoprotective agent. Current renoprotection paradigms depend wholly and entirely on angiotensin blockade; however, these agents [angiotensin converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs)] have proved to be imperfect renoprotective agents. In this review, we examine the mechanistic limitations of the various previous randomized controlled trials on CKD renoprotection, including the paucity of veritable, elaborate and systematic assessment methods for the documentation and reporting of individual patient-level, drug-related adverse events. We review the evidence base for the presence of putative, multiple independent and unrelated pathogenetic mechanisms that drive (diabetic and non-diabetic) CKD progression. Furthermore, we examine the validity, or lack thereof, of the hyped notion that the blockade of a single molecule (angiotensin II), which can only antagonize the angiotensin cascade, would veritably successfully, consistently and unfailingly deliver adequate and qualitative renoprotection results in (diabetic and non-diabetic) CKD patients. We clearly posit that there is this overarching impetus to arrive at the inference that multiple, disparately diverse and independent pathways, including any veritable combination of the mechanisms that we examine in this review, and many more others yet to be identified, do concurrently and asymmetrically contribute to CKD initiation and propagation to end-stage renal disease (ESRD) in our CKD patients. We conclude that current knowledge of CKD initiation and progression to ESRD, the natural history of CKD and the impacts of acute kidney injury on this continuum remain in their infancy and call for more research. Finally, we suggest a new classification scheme for renoprotective agents: (1) the single-pathway blockers that block a single putative pathogenetic pathway involved in CKD progression, as typified by ACE inhibitors and/or ARBs, and (2) the multiple-pathway blockers that are able to block or antagonize the effects of multiple pathogenetic pathways through their ability to simultaneously block, downstream, the effects of several pathways or mechanisms of CKD to ESRD progression and could therefore concurrently interfere with several unrelated upstream pathways or mechanisms. We surmise that maybe the ideal and truly renoprotective agent, clearly a multiple-pathway blocker, is on the horizon. This calls for more research efforts from all.

Renoprotection and the Bardoxolone Methyl Story – Is This the Right Way Forward? A Novel View of Renoprotection in CKD Trials: A New Classification Scheme for Renoprotective Agents

PubMed Central

Onuigbo, Macaulay

2013-01-01

In the June 2011 issue of the New England Journal of Medicine, the BEAM (Bardoxolone Methyl Treatment: Renal Function in CKD/Type 2 Diabetes) trial investigators rekindled new interest and also some controversy regarding the concept of renoprotection and the role of renoprotective agents, when they reported significant increases in the mean estimated glomerular filtration rate (eGFR) in diabetic chronic kidney disease (CKD) patients with an eGFR of 20-45 ml/min/1.73 m2 of body surface area at enrollment who received the trial drug bardoxolone methyl versus placebo. Unfortunately, subsequent phase IIIb trials failed to show that the drug is a safe alternative renoprotective agent. Current renoprotection paradigms depend wholly and entirely on angiotensin blockade; however, these agents [angiotensin converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs)] have proved to be imperfect renoprotective agents. In this review, we examine the mechanistic limitations of the various previous randomized controlled trials on CKD renoprotection, including the paucity of veritable, elaborate and systematic assessment methods for the documentation and reporting of individual patient-level, drug-related adverse events. We review the evidence base for the presence of putative, multiple independent and unrelated pathogenetic mechanisms that drive (diabetic and non-diabetic) CKD progression. Furthermore, we examine the validity, or lack thereof, of the hyped notion that the blockade of a single molecule (angiotensin II), which can only antagonize the angiotensin cascade, would veritably successfully, consistently and unfailingly deliver adequate and qualitative renoprotection results in (diabetic and non-diabetic) CKD patients. We clearly posit that there is this overarching impetus to arrive at the inference that multiple, disparately diverse and independent pathways, including any veritable combination of the mechanisms that we examine in this review, and many more others yet to be identified, do concurrently and asymmetrically contribute to CKD initiation and propagation to end-stage renal disease (ESRD) in our CKD patients. We conclude that current knowledge of CKD initiation and progression to ESRD, the natural history of CKD and the impacts of acute kidney injury on this continuum remain in their infancy and call for more research. Finally, we suggest a new classification scheme for renoprotective agents: (1) the single-pathway blockers that block a single putative pathogenetic pathway involved in CKD progression, as typified by ACE inhibitors and/or ARBs, and (2) the multiple-pathway blockers that are able to block or antagonize the effects of multiple pathogenetic pathways through their ability to simultaneously block, downstream, the effects of several pathways or mechanisms of CKD to ESRD progression and could therefore concurrently interfere with several unrelated upstream pathways or mechanisms. We surmise that maybe the ideal and truly renoprotective agent, clearly a multiple-pathway blocker, is on the horizon. This calls for more research efforts from all. PMID:23687511

Neuroimaging-Aided Prediction of the Effect of Methylphenidate in Children with Attention-Deficit Hyperactivity Disorder: A Randomized Controlled Trial.

PubMed

Ishii-Takahashi, Ayaka; Takizawa, Ryu; Nishimura, Yukika; Kawakubo, Yuki; Hamada, Kasumi; Okuhata, Shiho; Kawasaki, Shingo; Kuwabara, Hitoshi; Shimada, Takafumi; Todokoro, Ayako; Igarashi, Takashi; Watanabe, Kei-Ichiro; Yamasue, Hidenori; Kato, Nobumasa; Kasai, Kiyoto; Kano, Yukiko

2015-11-01

Although methylphenidate hydrochloride (MPH) is a first-line treatment for children with attention-deficit hyperactivity disorder (ADHD), the non-response rate is 30%. Our aim was to develop a supplementary neuroimaging biomarker for predicting the clinical effect of continuous MPH administration by using near-infrared spectroscopy (NIRS). After baseline assessment, we performed a double-blind, placebo-controlled, crossover trial with a single dose of MPH, followed by a prospective 4-to-8-week open trial with continuous MPH administration, and an ancillary 1-year follow-up. Twenty-two drug-naïve and eight previously treated children with ADHD (NAÏVE and NON-NAÏVE) were compared with 20 healthy controls (HCs) who underwent multiple NIRS measurements without intervention. We tested whether NIRS signals at the baseline assessment or ΔNIRS (single dose of MPH minus baseline assessment) predict the Clinical Global Impressions-Severity (CGI-S) score after 4-to-8-week or 1-year MPH administration. The secondary outcomes were the effect of MPH on NIRS signals after single-dose, 4-to-8-week, and 1-year administration. ΔNIRS significantly predicted CGI-S after 4-to-8-week MPH administration. The leave-one-out classification algorithm had 81% accuracy using the NIRS signal. ΔNIRS also significantly predicted CGI-S scores after 1 year of MPH administration. For secondary analyses, NAÏVE exhibited significantly lower prefrontal activation than HCs at the baseline assessment, whereas NON-NAÏVE and HCs showed similar activation. A single dose of MPH significantly increased activation compared with the placebo in NAÏVE. After 4-to-8-week administration, and even after MPH washout following 1-year administration, NAÏVE demonstrated normalized prefrontal activation. Supplementary NIRS measurements may serve as an objective biomarker for clinical decisions and monitoring concerning continuous MPH treatment in children with ADHD.

Comparison of ESSDAI and ClinESSDAI in potential optimisation of trial outcomes in primary Sjögren's syndrome: examination of data from the UK Primary Sjögren's Syndrome Registry.

PubMed

Dumusc, Alexandre; Ng, Wan-Fai; James, Katherine; Griffiths, Bridget; Price, Elizabeth; Pease, Colin; Emery, Paul; Lanyon, Peter; Jones, Adrian; Bombardieri, Michele; Sutcliffe, Nurhan; Pitzalis, Costantino; Gupta, Monica; McLaren, John; Cooper, Annie; Giles, Ian; Isenberg, David; Saravanan, Vadivelu; Coady, David; Dasgupta, Bhaskar; McHugh, Neil; Young-Min, Steven; Moots, Robert; Gendi, Nagui; Akil, Mohammed; Barone, Francesca; Fisher, Benjamin; Rauz, Saaeha; Richards, Andrea; Bowman, Simon

2018-02-14

To assess the use of the Clinical EULAR Sjögren's Syndrome Disease Activity Index (ClinESSDAI), a version of the ESSDAI without the biological domain, for assessing potential eligibility and outcomes for clinical trials in patients with primary Sjögren's syndrome (pSS), according to the new ACR-EULAR classification criteria, from the UK Primary Sjögren's Syndrome Registry (UKPSSR). A total of 665 patients from the UKPSSR cohort were analysed at their time of inclusion in the registry. ESSDAI and ClinESSDAI were calculated for each patient. For different disease activity index cut-off values, more potentially eligible participants were found when ClinESSDAI was used than with ESSDAI. The distribution of patients according to defined disease activity levels did not differ statistically (chi2 p = 0.57) between ESSDAI and ClinESSDAI for moderate disease activity (score ≥5 and <14; ESSDAI 36.4%; ClinESSDA 36.5%) or high disease activity (score ≥14; ESSDAI 5.4%; ClinESSDAI 6.8%). We did not find significant differences between the indexes in terms of activity levels for individual domains, with the exception of the articular domain. We found a good level of agreement between both indexes, and a positive correlation between lymphadenopathy and glandular domains with the use of either index and with different cut-off values. With the use of ClinESSDAI, the minimal clinically important improvement value was more often achievable with a one grade improvement of a single domain than with ESSDAI. We observed similar results when using the new ACR-EULAR classification criteria or the previously used American-European Consensus Group (AECG) classification criteria for pSS. In the UKPSSR population, the use of ClinESSDAI instead of ESSDAI did not lead to significant changes in score distribution, potential eligibility or outcome measurement in trials, or in routine care when immunological tests are not available. These results need to be confirmed in other cohorts and with longitudinal data.

An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine

PubMed Central

Pettersson-Yeo, William; Benetti, Stefania; Marquand, Andre F.; Joules, Richard; Catani, Marco; Williams, Steve C. R.; Allen, Paul; McGuire, Philip; Mechelli, Andrea

2014-01-01

In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine, that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that classification accuracies must be improved for such utility to be realized. One possible solution is to integrate different data types to provide a single combined output classification; either by generating a single decision function based on an integrated kernel matrix, or, by creating an ensemble of multiple single modality classifiers and integrating their predictions. Here, we describe four integrative approaches: (1) an un-weighted sum of kernels, (2) multi-kernel learning, (3) prediction averaging, and (4) majority voting, and compare their ability to enhance classification accuracy relative to the best single-modality classification accuracy. We achieve this by integrating structural, functional, and diffusion tensor magnetic resonance imaging data, in order to compare ultra-high risk (n = 19), first episode psychosis (n = 19) and healthy control subjects (n = 23). Our results show that (i) whilst integration can enhance classification accuracy by up to 13%, the frequency of such instances may be limited, (ii) where classification can be enhanced, simple methods may yield greater increases relative to more computationally complex alternatives, and, (iii) the potential for classification enhancement is highly influenced by the specific diagnostic comparison under consideration. In conclusion, our findings suggest that for moderately sized clinical neuroimaging datasets, combining different imaging modalities in a data-driven manner is no “magic bullet” for increasing classification accuracy. However, it remains possible that this conclusion is dependent on the use of neuroimaging modalities that had little, or no, complementary information to offer one another, and that the integration of more diverse types of data would have produced greater classification enhancement. We suggest that future studies ideally examine a greater variety of data types (e.g., genetic, cognitive, and neuroimaging) in order to identify the data types and combinations optimally suited to the classification of early stage psychosis. PMID:25076868

An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine.

PubMed

Pettersson-Yeo, William; Benetti, Stefania; Marquand, Andre F; Joules, Richard; Catani, Marco; Williams, Steve C R; Allen, Paul; McGuire, Philip; Mechelli, Andrea

2014-01-01

In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine, that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that classification accuracies must be improved for such utility to be realized. One possible solution is to integrate different data types to provide a single combined output classification; either by generating a single decision function based on an integrated kernel matrix, or, by creating an ensemble of multiple single modality classifiers and integrating their predictions. Here, we describe four integrative approaches: (1) an un-weighted sum of kernels, (2) multi-kernel learning, (3) prediction averaging, and (4) majority voting, and compare their ability to enhance classification accuracy relative to the best single-modality classification accuracy. We achieve this by integrating structural, functional, and diffusion tensor magnetic resonance imaging data, in order to compare ultra-high risk (n = 19), first episode psychosis (n = 19) and healthy control subjects (n = 23). Our results show that (i) whilst integration can enhance classification accuracy by up to 13%, the frequency of such instances may be limited, (ii) where classification can be enhanced, simple methods may yield greater increases relative to more computationally complex alternatives, and, (iii) the potential for classification enhancement is highly influenced by the specific diagnostic comparison under consideration. In conclusion, our findings suggest that for moderately sized clinical neuroimaging datasets, combining different imaging modalities in a data-driven manner is no "magic bullet" for increasing classification accuracy. However, it remains possible that this conclusion is dependent on the use of neuroimaging modalities that had little, or no, complementary information to offer one another, and that the integration of more diverse types of data would have produced greater classification enhancement. We suggest that future studies ideally examine a greater variety of data types (e.g., genetic, cognitive, and neuroimaging) in order to identify the data types and combinations optimally suited to the classification of early stage psychosis.

Study of Tranexamic Acid During Air Medical Prehospital Transport Trial (STAAMP trial)

DTIC Science & Technology

2015-10-01

AWARD NUMBER: W81XWH-13-2-0080 TITLE: Study of Tranexamic Acid During Air Medical Prehospital Transport Trial (STAAMP trial) PRINCIPAL INVESTIGATOR...TITLE AND SUBTITLE 5a. CONTRACT NUMBER Study of Tranexamic Acid During Air Medical Prehospital Transport Trial (STAAMP trial) 5b. GRANT NUMBER W81XWH...IRB approval regarding changes to the protocol language. 15. SUBJECT TERMS Prehospital; Tranexamic acid 16. SECURITY CLASSIFICATION OF: 17. LIMITATION

A systematic review of the quality of homeopathic clinical trials

PubMed Central

Jonas, Wayne B; Anderson, Rachel L; Crawford, Cindy C; Lyons, John S

2001-01-01

Background While a number of reviews of homeopathic clinical trials have been done, all have used methods dependent on allopathic diagnostic classifications foreign to homeopathic practice. In addition, no review has used established and validated quality criteria allowing direct comparison of the allopathic and homeopathic literature. Methods In a systematic review, we compared the quality of clinical-trial research in homeopathy to a sample of research on conventional therapies using a validated and system-neutral approach. All clinical trials on homeopathic treatments with parallel treatment groups published between 1945–1995 in English were selected. All were evaluated with an established set of 33 validity criteria previously validated on a broad range of health interventions across differing medical systems. Criteria covered statistical conclusion, internal, construct and external validity. Reliability of criteria application is greater than 0.95. Results 59 studies met the inclusion criteria. Of these, 79% were from peer-reviewed journals, 29% used a placebo control, 51% used random assignment, and 86% failed to consider potentially confounding variables. The main validity problems were in measurement where 96% did not report the proportion of subjects screened, and 64% did not report attrition rate. 17% of subjects dropped out in studies where this was reported. There was practically no replication of or overlap in the conditions studied and most studies were relatively small and done at a single-site. Compared to research on conventional therapies the overall quality of studies in homeopathy was worse and only slightly improved in more recent years. Conclusions Clinical homeopathic research is clearly in its infancy with most studies using poor sampling and measurement techniques, few subjects, single sites and no replication. Many of these problems are correctable even within a "holistic" paradigm given sufficient research expertise, support and methods. PMID:11801202

Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces.

PubMed

Micera, Silvestro; Rossini, Paolo M; Rigosa, Jacopo; Citi, Luca; Carpaneto, Jacopo; Raspopovic, Stanisa; Tombini, Mario; Cipriani, Christian; Assenza, Giovanni; Carrozza, Maria C; Hoffmann, Klaus-Peter; Yoshida, Ken; Navarro, Xavier; Dario, Paolo

2011-09-05

The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting. Thin-film longitudinal intra-fascicular electrodes were implanted in the median and ulnar nerves of an amputee's stump during a four-week trial. The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time in this research field by implementing a spike sorting and classification algorithm. The results showed that motor information (e.g., grip types and single finger movements) could be extracted with classification accuracy around 85% (for three classes plus rest) and that the user could improve his ability to govern motor commands over time as shown by the improved discrimination ability of our classification algorithm. These results open up new and promising possibilities for the development of a neuro-controlled hand prosthesis.

Full-motion video analysis for improved gender classification

NASA Astrophysics Data System (ADS)

Flora, Jeffrey B.; Lochtefeld, Darrell F.; Iftekharuddin, Khan M.

2014-06-01

The ability of computer systems to perform gender classification using the dynamic motion of the human subject has important applications in medicine, human factors, and human-computer interface systems. Previous works in motion analysis have used data from sensors (including gyroscopes, accelerometers, and force plates), radar signatures, and video. However, full-motion video, motion capture, range data provides a higher resolution time and spatial dataset for the analysis of dynamic motion. Works using motion capture data have been limited by small datasets in a controlled environment. In this paper, we explore machine learning techniques to a new dataset that has a larger number of subjects. Additionally, these subjects move unrestricted through a capture volume, representing a more realistic, less controlled environment. We conclude that existing linear classification methods are insufficient for the gender classification for larger dataset captured in relatively uncontrolled environment. A method based on a nonlinear support vector machine classifier is proposed to obtain gender classification for the larger dataset. In experimental testing with a dataset consisting of 98 trials (49 subjects, 2 trials per subject), classification rates using leave-one-out cross-validation are improved from 73% using linear discriminant analysis to 88% using the nonlinear support vector machine classifier.

Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China.

PubMed

Yang, Xiaoyan; Chen, Longgao; Li, Yingkui; Xi, Wenjia; Chen, Longqian

2015-07-01

Land use/land cover (LULC) inventory provides an important dataset in regional planning and environmental assessment. To efficiently obtain the LULC inventory, we compared the LULC classifications based on single satellite imagery with a rule-based classification based on multi-seasonal imagery in Lianyungang City, a coastal city in China, using CBERS-02 (the 2nd China-Brazil Environmental Resource Satellites) images. The overall accuracies of the classification based on single imagery are 78.9, 82.8, and 82.0% in winter, early summer, and autumn, respectively. The rule-based classification improves the accuracy to 87.9% (kappa 0.85), suggesting that combining multi-seasonal images can considerably improve the classification accuracy over any single image-based classification. This method could also be used to analyze seasonal changes of LULC types, especially for those associated with tidal changes in coastal areas. The distribution and inventory of LULC types with an overall accuracy of 87.9% and a spatial resolution of 19.5 m can assist regional planning and environmental assessment efficiently in Lianyungang City. This rule-based classification provides a guidance to improve accuracy for coastal areas with distinct LULC temporal spectral features.

Prognostic Classification Factors Associated With Development of Multiple Autoantibodies, Dysglycemia, and Type 1 Diabetes—A Recursive Partitioning Analysis

PubMed Central

Krischer, Jeffrey P.

2016-01-01

OBJECTIVE To define prognostic classification factors associated with the progression from single to multiple autoantibodies, multiple autoantibodies to dysglycemia, and dysglycemia to type 1 diabetes onset in relatives of individuals with type 1 diabetes. RESEARCH DESIGN AND METHODS Three distinct cohorts of subjects from the Type 1 Diabetes TrialNet Pathway to Prevention Study were investigated separately. A recursive partitioning analysis (RPA) was used to determine the risk classes. Clinical characteristics, including genotype, antibody titers, and metabolic markers were analyzed. RESULTS Age and GAD65 autoantibody (GAD65Ab) titers defined three risk classes for progression from single to multiple autoantibodies. The 5-year risk was 11% for those subjects >16 years of age with low GAD65Ab titers, 29% for those ≤16 years of age with low GAD65Ab titers, and 45% for those subjects with high GAD65Ab titers regardless of age. Progression to dysglycemia was associated with islet antigen 2 Ab titers, and 2-h glucose and fasting C-peptide levels. The 5-year risk is 28%, 39%, and 51% for respective risk classes defined by the three predictors. Progression to type 1 diabetes was associated with the number of positive autoantibodies, peak C-peptide level, HbA1c level, and age. Four risk classes defined by RPA had a 5-year risk of 9%, 33%, 62%, and 80%, respectively. CONCLUSIONS The use of RPA offered a new classification approach that could predict the timing of transitions from one preclinical stage to the next in the development of type 1 diabetes. Using these RPA classes, new prevention techniques can be tailored based on the individual prognostic risk characteristics at different preclinical stages. PMID:27208341

Using Wearable Sensors and Machine Learning Models to Separate Functional Upper Extremity Use From Walking-Associated Arm Movements.

PubMed

McLeod, Adam; Bochniewicz, Elaine M; Lum, Peter S; Holley, Rahsaan J; Emmer, Geoff; Dromerick, Alexander W

2016-02-01

To improve measurement of upper extremity (UE) use in the community by evaluating the feasibility of using body-worn sensor data and machine learning models to distinguish productive prehensile and bimanual UE activity use from extraneous movements associated with walking. Comparison of machine learning classification models with criterion standard of manually scored videos of performance in UE prosthesis users. Rehabilitation hospital training apartment. Convenience sample of UE prosthesis users (n=5) and controls (n=13) similar in age and hand dominance (N=18). Participants were filmed executing a series of functional activities; a trained observer annotated each frame to indicate either UE movement directed at functional activity or walking. Synchronized data from an inertial sensor attached to the dominant wrist were similarly classified as indicating either a functional use or walking. These data were used to train 3 classification models to predict the functional versus walking state given the associated sensor information. Models were trained over 4 trials: on UE amputees and controls and both within subject and across subject. Model performance was also examined with and without preprocessing (centering) in the across-subject trials. Percent correct classification. With the exception of the amputee/across-subject trial, at least 1 model classified >95% of test data correctly for all trial types. The top performer in the amputee/across-subject trial classified 85% of test examples correctly. We have demonstrated that computationally lightweight classification models can use inertial data collected from wrist-worn sensors to reliably distinguish prosthetic UE movements during functional use from walking-associated movement. This approach has promise in objectively measuring real-world UE use of prosthetic limbs and may be helpful in clinical trials and in measuring response to treatment of other UE pathologies. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Dissociable neural response signatures for slow amplitude and frequency modulation in human auditory cortex.

PubMed

Henry, Molly J; Obleser, Jonas

2013-01-01

Natural auditory stimuli are characterized by slow fluctuations in amplitude and frequency. However, the degree to which the neural responses to slow amplitude modulation (AM) and frequency modulation (FM) are capable of conveying independent time-varying information, particularly with respect to speech communication, is unclear. In the current electroencephalography (EEG) study, participants listened to amplitude- and frequency-modulated narrow-band noises with a 3-Hz modulation rate, and the resulting neural responses were compared. Spectral analyses revealed similar spectral amplitude peaks for AM and FM at the stimulation frequency (3 Hz), but amplitude at the second harmonic frequency (6 Hz) was much higher for FM than for AM. Moreover, the phase delay of neural responses with respect to the full-band stimulus envelope was shorter for FM than for AM. Finally, the critical analysis involved classification of single trials as being in response to either AM or FM based on either phase or amplitude information. Time-varying phase, but not amplitude, was sufficient to accurately classify AM and FM stimuli based on single-trial neural responses. Taken together, the current results support the dissociable nature of cortical signatures of slow AM and FM. These cortical signatures potentially provide an efficient means to dissect simultaneously communicated slow temporal and spectral information in acoustic communication signals.

Dissociable Neural Response Signatures for Slow Amplitude and Frequency Modulation in Human Auditory Cortex

PubMed Central

Henry, Molly J.; Obleser, Jonas

2013-01-01

Natural auditory stimuli are characterized by slow fluctuations in amplitude and frequency. However, the degree to which the neural responses to slow amplitude modulation (AM) and frequency modulation (FM) are capable of conveying independent time-varying information, particularly with respect to speech communication, is unclear. In the current electroencephalography (EEG) study, participants listened to amplitude- and frequency-modulated narrow-band noises with a 3-Hz modulation rate, and the resulting neural responses were compared. Spectral analyses revealed similar spectral amplitude peaks for AM and FM at the stimulation frequency (3 Hz), but amplitude at the second harmonic frequency (6 Hz) was much higher for FM than for AM. Moreover, the phase delay of neural responses with respect to the full-band stimulus envelope was shorter for FM than for AM. Finally, the critical analysis involved classification of single trials as being in response to either AM or FM based on either phase or amplitude information. Time-varying phase, but not amplitude, was sufficient to accurately classify AM and FM stimuli based on single-trial neural responses. Taken together, the current results support the dissociable nature of cortical signatures of slow AM and FM. These cortical signatures potentially provide an efficient means to dissect simultaneously communicated slow temporal and spectral information in acoustic communication signals. PMID:24205309

Agricultural Land Cover from Multitemporal C-Band SAR Data

NASA Astrophysics Data System (ADS)

Skriver, H.

2013-12-01

Henning Skriver DTU Space, Technical University of Denmark Ørsteds Plads, Building 348, DK-2800 Lyngby e-mail: hs@space.dtu.dk Problem description This paper focuses on land cover type from SAR data using high revisit acquisitions, including single and dual polarisation and fully polarimetric data, at C-band. The data set were acquired during an ESA-supported campaign, AgriSAR09, with the Radarsat-2 system. Ground surveys to obtain detailed land cover maps were performed during the campaign. Classification methods using single- and dual-polarisation data, and fully polarimetric data are used with multitemporal data with short revisit time. Results for airborne campaigns have previously been reported in Skriver et al. (2011) and Skriver (2012). In this paper, the short revisit satellite SAR data will be used to assess the trade-off between polarimetric SAR data and data as single or dual polarisation SAR data. This is particularly important in relation to the future GMES Sentinel-1 SAR satellites, where two satellites with a relatively wide swath will ensure a short revisit time globally. Questions dealt with are: which accuracy can we expect from a mission like the Sentinel-1, what is the improvement of using polarimetric SAR compared to single or dual polarisation SAR, and what is the optimum number of acquisitions needed. Methodology The data have sufficient number of looks for the Gaussian assumption to be valid for the backscatter coefficients for the individual polarizations. The classification method used for these data is therefore the standard Bayesian classification method for multivariate Gaussian statistics. For the full-polarimetric cases two classification methods have been applied, the standard ML Wishart classifier, and a method based on a reversible transform of the covariance matrix into backscatter intensities. The following pre-processing steps were performed on both data sets: The scattering matrix data in the form of SLC products were coregistered, converted to covariance matrix format and multilooked to a specific equivalent number of looks. Results The multitemporal data improve significantly the classification results, and single acquisition data cannot provide the necessary classification performance. The multitemporal data are especially important for the single and dual polarization data, but less important for the fully polarimetric data. The satellite data set produces realistic classification results based on about 2000 fields. The best classification results for the single-polarized mode provide classification errors in the mid-twenties. Using the dual-polarized mode reduces the classification error with about 5 percentage points, whereas the polarimetric mode reduces it with about 10 percentage points. These results show, that it will be possible to obtain reasonable results with relatively simple systems with short revisit time. This very important result shows that systems like the Sentinel-1 mission will be able to produce fairly good results for global land cover classification. References Skriver, H. et al., 2011, 'Crop Classification using Short-Revisit Multitemporal SAR Data', IEEE J. Sel. Topics in Appl. Earth Obs. Rem. Sens., vol. 4, pp. 423-431. Skriver, H., 2012, 'Crop classification by multitemporal C- and L-band single- and dual-polarization and fully polarimetric SAR', IEEE Trans. Geosc. Rem. Sens., vol. 50, pp. 2138-2149.

[Study on biopharmaceutics classification system for Chinese materia medica of extract of Huanglian].

PubMed

Liu, Yang; Yin, Xiu-Wen; Wang, Zi-Yu; Li, Xue-Lian; Pan, Meng; Li, Yan-Ping; Dong, Ling

2017-11-01

One of the advantages of biopharmaceutics classification system of Chinese materia medica (CMMBCS) is expanding the classification research level from single ingredient to multi-components of Chinese herb, and from multi-components research to holistic research of the Chinese materia medica. In present paper, the alkaloids of extract of huanglian were chosen as the main research object to explore their change rules in solubility and intestinal permeability of single-component and multi-components, and to determine the biopharmaceutical classification of extract of Huanglian from holistic level. The typical shake-flask method and HPLC were used to detect the solubility of single ingredient of alkaloids from extract of huanglian. The quantitative research of alkaloids in intestinal absorption was measured in single-pass intestinal perfusion experiment while permeability coefficient of extract of huanglian was calculated by self-defined weight coefficient method. Copyright© by the Chinese Pharmaceutical Association.

Patients classification on weaning trials using neural networks and wavelet transform.

PubMed

Arizmendi, Carlos; Viviescas, Juan; González, Hernando; Giraldo, Beatriz

2014-01-01

The determination of the optimal time of the patients in weaning trial process from mechanical ventilation, between patients capable of maintaining spontaneous breathing and patients that fail to maintain spontaneous breathing, is a very important task in intensive care unit. Wavelet Transform (WT) and Neural Networks (NN) techniques were applied in order to develop a classifier for the study of patients on weaning trial process. The respiratory pattern of each patient was characterized through different time series. Genetic Algorithms (GA) and Forward Selection were used as feature selection techniques. A classification performance of 77.00±0.06% of well classified patients, was obtained using a NN and GA combination, with only 6 variables of the 14 initials.

MERRF Classification: Implications for Diagnosis and Clinical Trials.

PubMed

Finsterer, Josef; Zarrouk-Mahjoub, Sinda; Shoffner, John M

2018-03-01

Given the etiologic heterogeneity of disease classification using clinical phenomenology, we employed contemporary criteria to classify variants associated with myoclonic epilepsy with ragged-red fibers (MERRF) syndrome and to assess the strength of evidence of gene-disease associations. Standardized approaches are used to clarify the definition of MERRF, which is essential for patient diagnosis, patient classification, and clinical trial design. Systematic literature and database search with application of standardized assessment of gene-disease relationships using modified Smith criteria and of variants reported to be associated with MERRF using modified Yarham criteria. Review of available evidence supports a gene-disease association for two MT-tRNAs and for POLG. Using modified Smith criteria, definitive evidence of a MERRF gene-disease association is identified for MT-TK. Strong gene-disease evidence is present for MT-TL1 and POLG. Functional assays that directly associate variants with oxidative phosphorylation impairment were critical to mtDNA variant classification. In silico analysis was of limited utility to the assessment of individual MT-tRNA variants. With the use of contemporary classification criteria, several mtDNA variants previously reported as pathogenic or possibly pathogenic are reclassified as neutral variants. MERRF is primarily an MT-TK disease, with pathogenic variants in this gene accounting for ~90% of MERRF patients. Although MERRF is phenotypically and genotypically heterogeneous, myoclonic epilepsy is the clinical feature that distinguishes MERRF from other categories of mitochondrial disorders. Given its low frequency in mitochondrial disorders, myoclonic epilepsy is not explained simply by an impairment of cellular energetics. Although MERRF phenocopies can occur in other genes, additional data are needed to establish a MERRF disease-gene association. This approach to MERRF emphasizes standardized classification rather than clinical phenomenology, thus improving patient diagnosis and clinical trial design. Copyright © 2017 Elsevier Inc. All rights reserved.

Comparison of wheat classification accuracy using different classifiers of the image-100 system

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Chen, S. C.; Moreira, M. A.; Delima, A. M.

1981-01-01

Classification results using single-cell and multi-cell signature acquisition options, a point-by-point Gaussian maximum-likelihood classifier, and K-means clustering of the Image-100 system are presented. Conclusions reached are that: a better indication of correct classification can be provided by using a test area which contains various cover types of the study area; classification accuracy should be evaluated considering both the percentages of correct classification and error of commission; supervised classification approaches are better than K-means clustering; Gaussian distribution maximum likelihood classifier is better than Single-cell and Multi-cell Signature Acquisition Options of the Image-100 system; and in order to obtain a high classification accuracy in a large and heterogeneous crop area, using Gaussian maximum-likelihood classifier, homogeneous spectral subclasses of the study crop should be created to derive training statistics.

EEG neural correlates of goal-directed movement intention.

PubMed

Pereira, Joana; Ofner, Patrick; Schwarz, Andreas; Sburlea, Andreea Ioana; Müller-Putz, Gernot R

2017-04-01

Using low-frequency time-domain electroencephalographic (EEG) signals we show, for the same type of upper limb movement, that goal-directed movements have different neural correlates than movements without a particular goal. In a reach-and-touch task, we explored the differences in the movement-related cortical potentials (MRCPs) between goal-directed and non-goal-directed movements. We evaluated if the detection of movement intention was influenced by the goal-directedness of the movement. In a single-trial classification procedure we found that classification accuracies are enhanced if there is a goal-directed movement in mind. Furthermore, by using the classifier patterns and estimating the corresponding brain sources, we show the importance of motor areas and the additional involvement of the posterior parietal lobule in the discrimination between goal-directed movements and non-goal-directed movements. We discuss next the potential contribution of our results on goal-directed movements to a more reliable brain-computer interface (BCI) control that facilitates recovery in spinal-cord injured or stroke end-users. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

An evaluation of ISOCLS and CLASSY clustering algorithms for forest classification in northern Idaho. [Elk River quadrange of the Clearwater National Forest

NASA Technical Reports Server (NTRS)

Werth, L. F. (Principal Investigator)

1981-01-01

Both the iterative self-organizing clustering system (ISOCLS) and the CLASSY algorithms were applied to forest and nonforest classes for one 1:24,000 quadrangle map of northern Idaho and the classification and mapping accuracies were evaluated with 1:30,000 color infrared aerial photography. Confusion matrices for the two clustering algorithms were generated and studied to determine which is most applicable to forest and rangeland inventories in future projects. In an unsupervised mode, ISOCLS requires many trial-and-error runs to find the proper parameters to separate desired information classes. CLASSY tells more in a single run concerning the classes that can be separated, shows more promise for forest stratification than ISOCLS, and shows more promise for consistency. One major drawback to CLASSY is that important forest and range classes that are smaller than a minimum cluster size will be combined with other classes. The algorithm requires so much computer storage that only data sets as small as a quadrangle can be used at one time.

Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.

PubMed

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.

Impact of oesophagitis classification in evaluating healing of erosive oesophagitis after therapy with proton pump inhibitors: a pooled analysis.

PubMed

Yaghoobi, Mohammad; Padol, Sara; Yuan, Yuhong; Hunt, Richard H

2010-05-01

The results of clinical trials with proton pump inhibitors (PPIs) are usually based on the Hetzel-Dent (HD), Savary-Miller (SM), or Los Angeles (LA) classifications to describe the severity and assess the healing of erosive oesophagitis. However, it is not known whether these classifications are comparable. The aim of this study was to review systematically the literature to compare the healing rates of erosive oesophagitis with PPIs in clinical trials assessed by the HD, SM, or LA classifications. A recursive, English language literature search in PubMed and Cochrane databases to December 2006 was performed. Double-blind randomized control trials comparing a PPI with another PPI, an H2-RA or placebo using endoscopic assessment of the healing of oesophagitis by the HD, SM or LA, or their modified classifications at 4 or 8 weeks, were included in the study. The healing rates on treatment with the same PPI(s), and same endoscopic grade(s) were pooled and compared between different classifications using Fisher's exact test or chi2 test where appropriate. Forty-seven studies from 965 potential citations met inclusion criteria. Seventy-eight PPI arms were identified, with 27 using HD, 29 using SM, and 22 using LA for five marketed PPIs. There was insufficient data for rabeprazole and esomeprazole (week 4 only) to compare because they were evaluated by only one classification. When data from all PPIs were pooled, regardless of baseline oesophagitis grades, the LA healing rate was significantly higher than SM and HD at both 4 and 8 weeks (74, 71, and 68% at 4 weeks and 89, 84, and 83% at 8 weeks, respectively). The distribution of different grades in study population was available only for pantoprazole where it was not significantly different between LA and SM subgroups. When analyzing data for PPI and dose, the LA classification showed a higher healing rate for omeprazole 20 mg/day and pantoprazole 40 mg/day (significant at 8 weeks), whereas healing by SM classification was significantly higher for omeprazole 40 mg/day (no data for LA) and lansoprazole 30 mg/day at 4 and 8 weeks. The healing rate by individual oesophagitis grade was not always available or robust enough for meaningful analysis. However, a difference between classifications remained. There is a significant, but not always consistent, difference in oesophagitis healing rates with the same PPI(s) reported by the LA, SM, or HD classifications. The possible difference between grading classifications should be considered when interpreting or comparing healing rates for oesophagitis from different studies.

EXTENDING AQUATIC CLASSIFICATION TO THE LANDSCAPE SCALE HYDROLOGY-BASED STRATEGIES

EPA Science Inventory

Aquatic classification of single water bodies (lakes, wetlands, estuaries) is often based on geologic origin, while stream classification has relied on multiple factors related to landform, geomorphology, and soils. We have developed an approach to aquatic classification based o...

EEG Subspace Analysis and Classification Using Principal Angles for Brain-Computer Interfaces

NASA Astrophysics Data System (ADS)

Ashari, Rehab Bahaaddin

Brain-Computer Interfaces (BCIs) help paralyzed people who have lost some or all of their ability to communicate and control the outside environment from loss of voluntary muscle control. Most BCIs are based on the classification of multichannel electroencephalography (EEG) signals recorded from users as they respond to external stimuli or perform various mental activities. The classification process is fraught with difficulties caused by electrical noise, signal artifacts, and nonstationarity. One approach to reducing the effects of similar difficulties in other domains is the use of principal angles between subspaces, which has been applied mostly to video sequences. This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations. One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only, should make this approach more appropriate for online BCI applications. In order to understand and test how EEG signals are different from one subject to another, different users are tested in this dissertation, some with motor impairments. Furthermore, the concept of transferring information between subjects is examined by training the approach on one subject and testing it on the other subject using the training subject's EEG subspaces to classify the testing subject's trials.

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China

PubMed Central

Hao, Pengyu; Wang, Li; Niu, Zheng

2015-01-01

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern. PMID:26360597

Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor.

PubMed

Xu, Chang; Wang, Yingguan; Bao, Xinghe; Li, Fengrong

2018-05-24

This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.

What's in a title? An assessment of whether randomized controlled trial in a title means that it is one.

PubMed

Koletsi, Despina; Pandis, Nikolaos; Polychronopoulou, Argy; Eliades, Theodore

2012-06-01

In this study, we aimed to investigate whether studies published in orthodontic journals and titled as randomized clinical trials are truly randomized clinical trials. A second objective was to explore the association of journal type and other publication characteristics on correct classification. American Journal of Orthodontics and Dentofacial Orthopedics, European Journal of Orthodontics, Angle Orthodontist, Journal of Orthodontics, Orthodontics and Craniofacial Research, World Journal of Orthodontics, Australian Orthodontic Journal, and Journal of Orofacial Orthopedics were hand searched for clinical trials labeled in the title as randomized from 1979 to July 2011. The data were analyzed by using descriptive statistics, and univariable and multivariable examinations of statistical associations via ordinal logistic regression modeling (proportional odds model). One hundred twelve trials were identified. Of the included trials, 33 (29.5%) were randomized clinical trials, 52 (46.4%) had an unclear status, and 27 (24.1%) were not randomized clinical trials. In the multivariable analysis among the included journal types, year of publication, number of authors, multicenter trial, and involvement of statistician were significant predictors of correctly classifying a study as a randomized clinical trial vs unclear and not a randomized clinical trial. From 112 clinical trials in the orthodontic literature labeled as randomized clinical trials, only 29.5% were identified as randomized clinical trials based on clear descriptions of appropriate random number generation and allocation concealment. The type of journal, involvement of a statistician, multicenter trials, greater numbers of authors, and publication year were associated with correct clinical trial classification. This study indicates the need of clear and accurate reporting of clinical trials and the need for educating investigators on randomized clinical trial methodology. Copyright © 2012 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

Single-Frame Terrain Mapping Software for Robotic Vehicles

NASA Technical Reports Server (NTRS)

Rankin, Arturo L.

2011-01-01

This software is a component in an unmanned ground vehicle (UGV) perception system that builds compact, single-frame terrain maps for distribution to other systems, such as a world model or an operator control unit, over a local area network (LAN). Each cell in the map encodes an elevation value, terrain classification, object classification, terrain traversability, terrain roughness, and a confidence value into four bytes of memory. The input to this software component is a range image (from a lidar or stereo vision system), and optionally a terrain classification image and an object classification image, both registered to the range image. The single-frame terrain map generates estimates of the support surface elevation, ground cover elevation, and minimum canopy elevation; generates terrain traversability cost; detects low overhangs and high-density obstacles; and can perform geometry-based terrain classification (ground, ground cover, unknown). A new origin is automatically selected for each single-frame terrain map in global coordinates such that it coincides with the corner of a world map cell. That way, single-frame terrain maps correctly line up with the world map, facilitating the merging of map data into the world map. Instead of using 32 bits to store the floating-point elevation for a map cell, the vehicle elevation is assigned to the map origin elevation and reports the change in elevation (from the origin elevation) in terms of the number of discrete steps. The single-frame terrain map elevation resolution is 2 cm. At that resolution, terrain elevation from 20.5 to 20.5 m (with respect to the vehicle's elevation) is encoded into 11 bits. For each four-byte map cell, bits are assigned to encode elevation, terrain roughness, terrain classification, object classification, terrain traversability cost, and a confidence value. The vehicle s current position and orientation, the map origin, and the map cell resolution are all included in a header for each map. The map is compressed into a vector prior to delivery to another system.

Numerical trials of HISSE

NASA Technical Reports Server (NTRS)

Peters, C.; Kampe, F. (Principal Investigator)

1980-01-01

The mathematical description and implementation of the statistical estimation procedure known as the Houston integrated spatial/spectral estimator (HISSE) is discussed. HISSE is based on a normal mixture model and is designed to take advantage of spectral and spatial information of LANDSAT data pixels, utilizing the initial classification and clustering information provided by the AMOEBA algorithm. The HISSE calculates parametric estimates of class proportions which reduce the error inherent in estimates derived from typical classify and count procedures common to nonparametric clustering algorithms. It also singles out spatial groupings of pixels which are most suitable for labeling classes. These calculations are designed to aid the analyst/interpreter in labeling patches with a crop class label. Finally, HISSE's initial performance on an actual LANDSAT agricultural ground truth data set is reported.

Decoding motor responses from the EEG during altered states of consciousness induced by propofol

NASA Astrophysics Data System (ADS)

Blokland, Yvonne; Farquhar, Jason; Lerou, Jos; Mourisse, Jo; Scheffer, Gert Jan; van Geffen, Geert-Jan; Spyrou, Loukianos; Bruhn, Jörgen

2016-04-01

Objective. Patients undergoing general anesthesia may awaken and become aware of the surgical procedure. Due to neuromuscular blocking agents, patients could be conscious yet unable to move. Using brain-computer interface (BCI) technology, it may be possible to detect movement attempts from the EEG. However, it is unknown how an anesthetic influences the brain response to motor tasks. Approach. We tested the offline classification performance of a movement-based BCI in 12 healthy subjects at two effect-site concentrations of propofol. For each subject a second classifier was trained on the subject’s data obtained before sedation, then tested on the data obtained during sedation (‘transfer classification’). Main results. At concentration 0.5 μg ml-1, despite an overall propofol EEG effect, the mean single trial classification accuracy was 85% (95% CI 81%-89%), and 83% (79%-88%) for the transfer classification. At 1.0 μg ml-1, the accuracies were 81% (76%-86%), and 72% (66%-79%), respectively. At the highest propofol concentration for four subjects, unlike the remaining subjects, the movement-related brain response had been largely diminished, and the transfer classification accuracy was not significantly above chance. These subjects showed a slower and more erratic task response, indicating an altered state of consciousness distinct from that of the other subjects. Significance. The results show the potential of using a BCI to detect intra-operative awareness and justify further development of this paradigm. At the same time, the relationship between motor responses and consciousness and its clinical relevance for intraoperative awareness requires further investigation.

Classification of feeding and eating disorders: review of evidence and proposals for ICD-11

PubMed Central

UHER, RUDOLF; RUTTER, MICHAEL

2012-01-01

Current classification of eating disorders is failing to classify most clinical presentations; ignores continuities between child, adolescent and adult manifestations; and requires frequent changes of diagnosis to accommodate the natural course of these disorders. The classification is divorced from clinical practice, and investigators of clinical trials have felt compelled to introduce unsystematic modifications. Classification of feeding and eating disorders in ICD-11 requires substantial changes to remediate the shortcomings. We review evidence on the developmental and cross-cultural differences and continuities, course and distinctive features of feeding and eating disorders. We make the following recommendations: a) feeding and eating disorders should be merged into a single grouping with categories applicable across age groups; b) the category of anorexia nervosa should be broadened through dropping the requirement for amenorrhoea, extending the weight criterion to any significant underweight, and extending the cognitive criterion to include developmentally and culturally relevant presentations; c) a severity qualifier “with dangerously low body weight” should distinguish the severe cases of anorexia nervosa that carry the riskiest prognosis; d) bulimia nervosa should be extended to include subjective binge eating; e) binge eating disorder should be included as a specific category defined by subjective or objective binge eating in the absence of regular compensatory behaviour; f) combined eating disorder should classify subjects who sequentially or concurrently fulfil criteria for both anorexia and bulimia nervosa; g) avoidant/restrictive food intake disorder should classify restricted food intake in children or adults that is not accompanied by body weight and shape related psychopathology; h) a uniform minimum duration criterion of four weeks should apply. PMID:22654933

Predict or classify: The deceptive role of time-locking in brain signal classification

NASA Astrophysics Data System (ADS)

Rusconi, Marco; Valleriani, Angelo

2016-06-01

Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

Single classifier, OvO, OvA and RCC multiclass classification method in handheld based smartphone gait identification

NASA Astrophysics Data System (ADS)

Raziff, Abdul Rafiez Abdul; Sulaiman, Md Nasir; Mustapha, Norwati; Perumal, Thinagaran

2017-10-01

Gait recognition is widely used in many applications. In the application of the gait identification especially in people, the number of classes (people) is many which may comprise to more than 20. Due to the large amount of classes, the usage of single classification mapping (direct classification) may not be suitable as most of the existing algorithms are mostly designed for the binary classification. Furthermore, having many classes in a dataset may result in the possibility of having a high degree of overlapped class boundary. This paper discusses the application of multiclass classifier mappings such as one-vs-all (OvA), one-vs-one (OvO) and random correction code (RCC) on handheld based smartphone gait signal for person identification. The results is then compared with a single J48 decision tree for benchmark. From the result, it can be said that using multiclass classification mapping method thus partially improved the overall accuracy especially on OvO and RCC with width factor more than 4. For OvA, the accuracy result is worse than a single J48 due to a high number of classes.

Current Assessment and Classification of Suicidal Phenomena using the FDA 2012 Draft Guidance Document on Suicide Assessment: A Critical Review.

PubMed

Sheehan, David V; Giddens, Jennifer M; Sheehan, Kathy Harnett

2014-09-01

Standard international classification criteria require that classification categories be comprehensive to avoid type II error. Categories should be mutually exclusive and definitions should be clear and unambiguous (to avoid type I and type II errors). In addition, the classification system should be robust enough to last over time and provide comparability between data collections. This article was designed to evaluate the extent to which the classification system contained in the United States Food and Drug Administration 2012 Draft Guidance for the prospective assessment and classification of suicidal ideation and behavior in clinical trials meets these criteria. A critical review is used to assess the extent to which the proposed categories contained in the Food and Drug Administration 2012 Draft Guidance are comprehensive, unambiguous, and robust. Assumptions that underlie the classification system are also explored. The Food and Drug Administration classification system contained in the 2012 Draft Guidance does not capture the full range of suicidal ideation and behavior (type II error). Definitions, moreover, are frequently ambiguous (susceptible to multiple interpretations), and the potential for misclassification (type I and type II errors) is compounded by frequent mismatches in category titles and definitions. These issues have the potential to compromise data comparability within clinical trial sites, across sites, and over time. These problems need to be remedied because of the potential for flawed data output and consequent threats to public health, to research on the safety of medications, and to the search for effective medication treatments for suicidality.

Evidence of emotion-antecedent appraisal checks in electroencephalography and facial electromyography

PubMed Central

Scherer, Klaus R.; Schuller, Björn W.

2018-01-01

In the present study, we applied Machine Learning (ML) methods to identify psychobiological markers of cognitive processes involved in the process of emotion elicitation as postulated by the Component Process Model (CPM). In particular, we focused on the automatic detection of five appraisal checks—novelty, intrinsic pleasantness, goal conduciveness, control, and power—in electroencephalography (EEG) and facial electromyography (EMG) signals. We also evaluated the effects on classification accuracy of averaging the raw physiological signals over different numbers of trials, and whether the use of minimal sets of EEG channels localized over specific scalp regions of interest are sufficient to discriminate between appraisal checks. We demonstrated the effectiveness of our approach on two data sets obtained from previous studies. Our results show that novelty and power appraisal checks can be consistently detected in EEG signals above chance level (binary tasks). For novelty, the best classification performance in terms of accuracy was achieved using features extracted from the whole scalp, and by averaging across 20 individual trials in the same experimental condition (UAR = 83.5 ± 4.2; N = 25). For power, the best performance was obtained by using the signals from four pre-selected EEG channels averaged across all trials available for each participant (UAR = 70.6 ± 5.3; N = 24). Together, our results indicate that accurate classification can be achieved with a relatively small number of trials and channels, but that averaging across a larger number of individual trials is beneficial for the classification for both appraisal checks. We were not able to detect any evidence of the appraisal checks under study in the EMG data. The proposed methodology is a promising tool for the study of the psychophysiological mechanisms underlying emotional episodes, and their application to the development of computerized tools (e.g., Brain-Computer Interface) for the study of cognitive processes involved in emotions. PMID:29293572

Evidence of emotion-antecedent appraisal checks in electroencephalography and facial electromyography.

PubMed

Coutinho, Eduardo; Gentsch, Kornelia; van Peer, Jacobien; Scherer, Klaus R; Schuller, Björn W

2018-01-01

In the present study, we applied Machine Learning (ML) methods to identify psychobiological markers of cognitive processes involved in the process of emotion elicitation as postulated by the Component Process Model (CPM). In particular, we focused on the automatic detection of five appraisal checks-novelty, intrinsic pleasantness, goal conduciveness, control, and power-in electroencephalography (EEG) and facial electromyography (EMG) signals. We also evaluated the effects on classification accuracy of averaging the raw physiological signals over different numbers of trials, and whether the use of minimal sets of EEG channels localized over specific scalp regions of interest are sufficient to discriminate between appraisal checks. We demonstrated the effectiveness of our approach on two data sets obtained from previous studies. Our results show that novelty and power appraisal checks can be consistently detected in EEG signals above chance level (binary tasks). For novelty, the best classification performance in terms of accuracy was achieved using features extracted from the whole scalp, and by averaging across 20 individual trials in the same experimental condition (UAR = 83.5 ± 4.2; N = 25). For power, the best performance was obtained by using the signals from four pre-selected EEG channels averaged across all trials available for each participant (UAR = 70.6 ± 5.3; N = 24). Together, our results indicate that accurate classification can be achieved with a relatively small number of trials and channels, but that averaging across a larger number of individual trials is beneficial for the classification for both appraisal checks. We were not able to detect any evidence of the appraisal checks under study in the EMG data. The proposed methodology is a promising tool for the study of the psychophysiological mechanisms underlying emotional episodes, and their application to the development of computerized tools (e.g., Brain-Computer Interface) for the study of cognitive processes involved in emotions.

Effects of virtual reality for stroke individuals based on the International Classification of Functioning and Health: a systematic review.

PubMed

Palma, Gisele Carla Dos Santos; Freitas, Tatiana Beline; Bonuzzi, Giordano Márcio Gatinho; Soares, Marcos Antonio Arlindo; Leite, Paulo Henrique Wong; Mazzini, Natália Araújo; Almeida, Murilo Ruas Groschitz; Pompeu, José Eduardo; Torriani-Pasin, Camila

2017-05-01

This review determines the effects of virtual reality interventions for stroke subjects based on the International Classification of Functioning, Disability,and Health (ICF) framework. Virtual reality is a promising tool for therapy for stroke rehabilitation, but the effects of virtual reality interventions on post-stroke patients based on the specific ICF domains (Body Structures, Body Functions, Activity, and Participation) have not been investigated. A systematic review was conducted, including trials with adults with a clinical diagnosis of a chronic, subacute, or acute stroke. Eligible trials had to include studies with an intervention protocol and follow-up, with a focus on upper limbs and/or lower limbs and/or balance. The Physiotherapy Evidence Database (PEDro) was used to assess the methodological quality of randomized controlled trials. Each trial was separated according to methodological quality into a high-quality trial (PEDro ≥ 6) and a low-quality trial (PEDro ≤ 6). Only high-quality trials were analyzed specifically based on the outcome of these trials. In total, 54 trials involving 1811 participants were included. Of the papers included and considered high quality, 14 trials evaluated areas of the Body Structures component, 20 trials of the Body Functions domain, 17 trials of the Activity component, and 8 trials of the Participation domain. In relation to ICF Part 2, four trials evaluated areas of the Personal Factors component and one trial evaluated domains of the Environmental Factors component. The effects of virtual reality on stroke rehabilitation based on the ICF framework are positive in Body Function and Body Structure. However, the results in the domains Activity and Participation are inconclusive. More high-quality clinical trials are needed to confirm the effectiveness of virtual reality in the domains of Activity and Participation.

Neural signatures of attention: insights from decoding population activity patterns.

PubMed

Sapountzis, Panagiotis; Gregoriou, Georgia G

2018-01-01

Understanding brain function and the computations that individual neurons and neuronal ensembles carry out during cognitive functions is one of the biggest challenges in neuroscientific research. To this end, invasive electrophysiological studies have provided important insights by recording the activity of single neurons in behaving animals. To average out noise, responses are typically averaged across repetitions and across neurons that are usually recorded on different days. However, the brain makes decisions on short time scales based on limited exposure to sensory stimulation by interpreting responses of populations of neurons on a moment to moment basis. Recent studies have employed machine-learning algorithms in attention and other cognitive tasks to decode the information content of distributed activity patterns across neuronal ensembles on a single trial basis. Here, we review results from studies that have used pattern-classification decoding approaches to explore the population representation of cognitive functions. These studies have offered significant insights into population coding mechanisms. Moreover, we discuss how such advances can aid the development of cognitive brain-computer interfaces.

The Hand Eczema Trial (HET): Design of a randomised clinical trial of the effect of classification and individual counselling versus no intervention among health-care workers with hand eczema.

PubMed

Ibler, Kristina Sophie; Agner, Tove; Hansen, Jane Lindschou; Gluud, Christian

2010-08-31

Hand eczema is the most frequently recognized occupational disease in Denmark with an incidence of approximately 0.32 per 1000 person-years. Consequences of hand eczema include chronic severe eczema, prolonged sick leave, unemployment, and impaired quality of life. New preventive strategies are needed to reduce occupational hand eczema. We describe the design of a randomised clinical trial to investigate the effects of classification of hand eczema plus individual counselling versus no intervention. The trial includes health-care workers with hand eczema identified from a self-administered questionnaire delivered to 3181 health-care workers in three Danish hospitals. The questionnaire identifies the prevalence of hand eczema, knowledge of skin-protection, and exposures that can lead to hand eczema. At entry, all participants are assessed regarding: disease severity (Hand Eczema Severity Index); self-evaluated disease severity; number of eruptions; quality of life; skin protective behaviour, and knowledge of skin protection. The patients are centrally randomised to intervention versus no intervention 1:1 stratified for hospital, profession, and severity score. The experimental group undergoes patch and prick testing; classification of the hand eczema; demonstration of hand washing and appliance of emollients; individual counselling, and a skin-care programme. The control group receives no intervention. All participants are reassessed after six months. The primary outcome is observer-blinded assessment of disease severity and the secondary outcomes are unblinded assessments of disease severity; number of eruptions; knowledge of skin protection; skin-protective behaviour, and quality of life. The trial is registered in ClinicalTrials.Gov, NCT01012453.

Single-Use Energy Sources and Operating Room Time for Laparoscopic Hysterectomy: A Randomized Controlled Trial.

PubMed

Holloran-Schwartz, M Brigid; Gavard, Jeffrey A; Martin, Jared C; Blaskiewicz, Robert J; Yeung, Patrick P

2016-01-01

To compare the intraoperative direct costs of a single-use energy device with reusable energy devices during laparoscopic hysterectomy. A randomized controlled trial (Canadian Task Force Classification I). An academic hospital. Forty-six women who underwent laparoscopic hysterectomy from March 2013 to September 2013. Each patient served as her own control. One side of the uterine attachments was desiccated and transected with the single-use device (Ligasure 5-mm Blunt Tip LF1537 with the Force Triad generator). The other side was desiccated and transected with reusable bipolar forceps (RoBi 5 mm), and transected with monopolar scissors using the same Covidien Force Triad generator. The instrument approach used was randomized to the attending physician who was always on the patient's left side. Resident physicians always operated on the patient's right side and used the converse instruments of the attending physician. Start time was recorded at the utero-ovarian pedicle and end time was recorded after transection of the uterine artery on the same side. Costs included the single-use device; amortized costs of the generator, reusable instruments, and cords; cleaning and packaging of reusable instruments; and disposal of the single-use device. Operating room time was $94.14/min. We estimated that our single use-device cost $630.14 and had a total time savings of 6.7 min per case, or 3.35 min per side, which could justify the expense of the device. The single-use energy device had significant median time savings (-4.7 min per side, p < .001) and total intraoperative direct cost savings ($254.16 per case). A single-use energy device that both desiccates and cuts significantly reduced operating room time to justify its own cost, and it also reduced total intraoperative direct costs during laparoscopic hysterectomy in our institution. Operating room cost per minute varies between institutions and must be considered before generalizing our results. Copyright © 2016 AAGL. Published by Elsevier Inc. All rights reserved.

33 CFR 150.110 - What are the notification requirements upon receipt of classification society certifications?

Code of Federal Regulations, 2010 CFR

2010-07-01

... requirements upon receipt of classification society certifications? 150.110 Section 150.110 Navigation and... society certifications? The licensee must notify the Captain of the Port, in writing, upon receipt of a classification society certification, interim class certificate, or single point mooring classification...

33 CFR 150.110 - What are the notification requirements upon receipt of classification society certifications?

Code of Federal Regulations, 2011 CFR

2011-07-01

... requirements upon receipt of classification society certifications? 150.110 Section 150.110 Navigation and... society certifications? The licensee must notify the Captain of the Port, in writing, upon receipt of a classification society certification, interim class certificate, or single point mooring classification...

Single trial detection of hand poses in human ECoG using CSP based feature extraction.

PubMed

Kapeller, C; Schneider, C; Kamada, K; Ogawa, H; Kunii, N; Ortner, R; Pruckl, R; Guger, C

2014-01-01

Decoding brain activity of corresponding highlevel tasks may lead to an independent and intuitively controlled Brain-Computer Interface (BCI). Most of today's BCI research focuses on analyzing the electroencephalogram (EEG) which provides only limited spatial and temporal resolution. Derived electrocorticographic (ECoG) signals allow the investigation of spatially highly focused task-related activation within the high-gamma frequency band, making the discrimination of individual finger movements or complex grasping tasks possible. Common spatial patterns (CSP) are commonly used for BCI systems and provide a powerful tool for feature optimization and dimensionality reduction. This work focused on the discrimination of (i) three complex hand movements, as well as (ii) hand movement and idle state. Two subjects S1 and S2 performed single `open', `peace' and `fist' hand poses in multiple trials. Signals in the high-gamma frequency range between 100 and 500 Hz were spatially filtered based on a CSP algorithm for (i) and (ii). Additionally, a manual feature selection approach was tested for (i). A multi-class linear discriminant analysis (LDA) showed for (i) an error rate of 13.89 % / 7.22 % and 18.42 % / 1.17 % for S1 and S2 using manually / CSP selected features, where for (ii) a two class LDA lead to a classification error of 13.39 % and 2.33 % for S1 and S2, respectively.

Behavioral state classification in epileptic brain using intracranial electrophysiology

NASA Astrophysics Data System (ADS)

Kremen, Vaclav; Duque, Juliano J.; Brinkmann, Benjamin H.; Berry, Brent M.; Kucewicz, Michal T.; Khadjevand, Fatemeh; Van Gompel, Jamie; Stead, Matt; St. Louis, Erik K.; Worrell, Gregory A.

2017-04-01

Objective. Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach. Data from seven patients (age 34+/- 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1-600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results. Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80-250 Hz), Fast Ripple (250-600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  ⩾90% using a single electrode contact and single spectral feature. Significance. Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

Interrater reliability of the international consensus definition of drug-resistant epilepsy: a pilot study.

PubMed

Hao, Xiao-ting; Wong, Irina S M; Kwan, Patrick

2011-10-01

We evaluated the interrater reliability of the consensus definition of drug-resistant epilepsy proposed by the International League Against Epilepsy. According to the definition framework, outcome of each antiepileptic drug (AED) trial was categorized as "seizure freedom" or "treatment failure." This level 1 assessment was used to determine the level 2 classification, which defined drug-resistant epilepsy as the failure of adequate trials of two or more AED schedules to achieve sustained seizure freedom. Two raters classified treatment outcomes of 150 patients independently. The patients had received a total of 428 trials of AEDs. Categorization of level 1 outcome to individual AED trials by the raters was consistent in 413 (96.5%). For the level 2 classification of drug-resistant or drug-responsive epilepsy, there was absolute agreement between the raters in 141 patients (94%), with a κ index of 0.91 (P<0.001). The definition appeared to have a high degree of interrater reliability in this setting. Copyright © 2011 Elsevier Inc. All rights reserved.

Mixture of autoregressive modeling orders and its implication on single trial EEG classification

PubMed Central

Atyabi, Adham; Shic, Frederick; Naples, Adam

2016-01-01

Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR’s modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE). This article assesses the hypothesis that appropriate mixture of multiple AR orders is likely to better represent the true signal compared to any single order. Better spectral representation of underlying EEG patterns can increase utility of AR features in Brain Computer Interface (BCI) systems by increasing timely & correctly responsiveness of such systems to operator’s thoughts. Two mechanisms of Evolutionary-based fusion and Ensemble-based mixture are utilized for identifying such appropriate mixture of modeling orders. The classification performance of the resultant AR-mixtures are assessed against several conventional methods utilized by the community including 1) A well-known set of commonly used orders suggested by the literature, 2) conventional order estimation approaches (e.g., AIC, BIC and FPE), 3) blind mixture of AR features originated from a range of well-known orders. Five datasets from BCI competition III that contain 2, 3 and 4 motor imagery tasks are considered for the assessment. The results indicate superiority of Ensemble-based modeling order mixture and evolutionary-based order fusion methods within all datasets. PMID:28740331

Multiple linear regression to estimate time-frequency electrophysiological responses in single trials

PubMed Central

Hu, L.; Zhang, Z.G.; Mouraux, A.; Iannetti, G.D.

2015-01-01

Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical oscillations, obtaining single-trial estimate of response latency, frequency, and magnitude. This permits within-subject statistical comparisons, correlation with pre-stimulus features, and integration of simultaneously-recorded EEG and fMRI. PMID:25665966

Slow updating of the achromatic point after a change in illumination

PubMed Central

Lee, R. J.; Dawson, K. A.; Smithson, H. E.

2015-01-01

For a colour constant observer, the colour appearance of a surface is independent of the spectral composition of the light illuminating it. We ask how rapidly colour appearance judgements are updated following a change in illumination. We obtained repeated binary colour classifications for a set of stimuli defined by their reflectance functions and rendered under either sunlight or skylight. We used these classifications to derive boundaries in colour space that identify the observer’s achromatic point. In steady-state conditions of illumination, the achromatic point lay close to the illuminant chromaticity. In our experiment the illuminant changed abruptly every 21 seconds (at the onset of every 10th trial), allowing us to track changes in the achromatic point that were caused by the cycle of illuminant changes. In one condition, the test reflectance was embedded in a spatial pattern of reflectance samples under consistent illumination. The achromatic point migrated across colour space between the chromaticities of the steady-state achromatic points. This update took several trials rather than being immediate. To identify the factors that governed perceptual updating of appearance judgements we used two further conditions, one in which the test reflectance was presented in isolation and one in which the surrounding reflectances were rendered under an inconsistent and unchanging illumination. Achromatic settings were not well predicted by the information available from scenes at a single time-point. Instead the achromatic points showed a strong dependence on the history of chromatic samples. The strength of this dependence differed between observers and was modulated by the spatial context. PMID:22275468

Trial of Naltrexone and Dextromethorphan for Gulf War Veterans’ Illness

DTIC Science & Technology

2010-07-01

09-2-0065 TITLE: Trial of Naltrexone and Dextromethorphan for Gulf War Veterans’ Illness PRINCIPAL INVESTIGATOR: William Joel Meggs, MD, PhD...From - To) 1 JUL 2009 - 30 JUN 2010 4. TITLE AND SUBTITLE Trial of Naltrexone and Dextromethorphan for Gulf War Veteravns’ Illness 5a... dextromethorphan & naltrexone for gulf war illness. 15. SUBJECT TERMS Dextromethorphan , naltexone, gulf war illness 16. SECURITY CLASSIFICATION OF

Identification of terrain cover using the optimum polarimetric classifier

NASA Technical Reports Server (NTRS)

Kong, J. A.; Swartz, A. A.; Yueh, H. A.; Novak, L. M.; Shin, R. T.

1988-01-01

A systematic approach for the identification of terrain media such as vegetation canopy, forest, and snow-covered fields is developed using the optimum polarimetric classifier. The covariance matrices for various terrain cover are computed from theoretical models of random medium by evaluating the scattering matrix elements. The optimal classification scheme makes use of a quadratic distance measure and is applied to classify a vegetation canopy consisting of both trees and grass. Experimentally measured data are used to validate the classification scheme. Analytical and Monte Carlo simulated classification errors using the fully polarimetric feature vector are compared with classification based on single features which include the phase difference between the VV and HH polarization returns. It is shown that the full polarimetric results are optimal and provide better classification performance than single feature measurements.

Feasibility of a multicentre, randomised controlled trial of laparoscopic versus open colorectal surgery in the acute setting: the LaCeS feasibility trial protocol.

PubMed

Harji, Deena; Marshall, Helen; Gordon, Katie; Crow, Hannah; Hiley, Victoria; Burke, Dermot; Griffiths, Ben; Moriarty, Catherine; Twiddy, Maureen; O'Dwyer, John L; Verjee, Azmina; Brown, Julia; Sagar, Peter

2018-02-22

Acute colorectal surgery forms a significant proportion of emergency admissions within the National Health Service. There is limited evidence to suggest minimally invasive surgery may be associated with improved clinical outcomes in this cohort of patients. Consequently, there is a need to assess the clinical effectiveness and cost-effectiveness of laparoscopic surgery in the acute colorectal setting. However,emergency colorectal surgical trials have previously been difficult to conduct due to issues surrounding recruitment and equipoise. The LaCeS (randomised controlled trial of Laparoscopic versus open Colorectal Surgery in the acute setting) feasibility trial will determine the feasibility of conducting a definitive, phase III trial of laparoscopic versus open acute colorectal resection. The LaCeS feasibility trial is a prospective, multicentre, single-blinded, parallel group, pragmatic randomised controlled feasibility trial. Patients will be randomised on a 1:1 basis to receive eitherlaparoscopic or open surgery. The trial aims to recruit at least 66 patients from five acute general surgical units across the UK. Patients over the age of 18 with a diagnosis of acute colorectal pathology requiring resection on clinical and radiological/endoscopic investigations, with a National Confidential Enquiry into Patient Outcome and Death classification of urgent will be considered eligible for participation. The primary outcome is recruitment. Secondary outcomes include assessing the safety profile of laparoscopic surgery using intraoperative and postoperative complication rates, conversion rates and patient-safety indicators as surrogate markers. Clinical and patient-reported outcomes will also be reported. The trial will contain an embedded qualitative study to assess clinician and patient acceptability of trial processes. The LaCeS feasibility trial is approved by the Yorkshire and The Humber, Bradford Leeds Research Ethics Committee (REC reference: 15/ YH/0542). The results from the trial will be presented at national and international colorectal conferences and will be submitted for publication to peer-reviewed journals. ISRCTN15681041; Pre-results. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Hysterectomy for complications after uterine artery embolization for leiomyoma: results of a Canadian multicenter clinical trial.

PubMed

Pron, Gaylene; Mocarski, Eva; Cohen, Marsha; Colgan, Terence; Bennett, John; Common, Andrew; Vilos, George; Kung, Rose

2003-02-01

To determine the complication-related hysterectomy rate after uterine artery embolization (UAE) for symptomatic uterine leiomyomas. Prospective, multicenter, nonrandomized, single-arm clinical trial (Canadian Task Force classification II-2). Eight Ontario University-affiliated teaching and community hospitals. Five hundred fifty-five women. Polyvinyl alcohol particles were delivered through a catheter into uterine arteries under fluoroscopic guidance. Prospective follow-up investigations consisted of telephone interviews, ultrasound examinations, and reviews of pathology and surgery reports. Median follow-up was 8.1 months, and all but five patients had complete 3-month follow-up. At 3 months, eight women (1.5%, 95% CI 0.6-2.8) underwent complication-related hysterectomy. Half of the surgeries were performed at institutions other than where UAE had been performed. Indications for hysterectomies were infections (2), postembolization pain (4), vaginal bleeding (1), and prolapsed leiomyoma (1). The 3-month complication rate resulting in hysterectomy after UAE in a large cohort of women was low. Hysterectomy after UAE is an important measure of safety and a key outcome measure of this new therapy.

Cross-Classification and Category Representation in Children's Concepts

ERIC Educational Resources Information Center

Nguyen, Simone P.

2007-01-01

Items commonly belong to many categories. Cross-classification is the classification of a single item into more than one category. This research explored 2- to 6-year-old children's use of 2 different category systems for cross-classification: script (e.g., school-time items, birthday party items) and taxonomic (e.g., animals, clothes). The…

Development and Field Test of the Trial Battery for Project A. Improving the Selection, Classification and Utilization of Army Enlisted Personnel. Project A: Improving the Selection, Classification and Utilization of Army Enlisted Personnel. ARI Technical Report 739.

ERIC Educational Resources Information Center

Peterson, Norman G., Ed.

As part of the United States Army's Project A, research has been conducted to develop and field test a battery of experimental tests to complement the Armed Services Vocational Aptitude Battery in predicting soldiers' job performance. Project A is the United States Army's large-scale manpower effort to improve selection, classification, and…

CLASSIFICATION FRAMEWORK FOR COASTAL ECOSYSTEM RESPONSES TO AQUATIC STRESSORS

EPA Science Inventory

Many classification schemes have been developed to group ecosystems based on similar characteristics. To date, however, no single scheme has addressed coastal ecosystem responses to multiple stressors. We developed a classification framework for coastal ecosystems to improve the ...

Liver Function Assessment Using Technetium 99m-Galactosyl Single-Photon Emission Computed Tomography/CT Fusion Imaging: A Prospective Trial.

PubMed

Okabayashi, Takehiro; Shima, Yasuo; Morita, Sojiro; Shimada, Yasuhiro; Sumiyoshi, Tatsuaki; Sui, Kenta; Iwata, Jun; Iiyama, Tatsuo

2017-12-01

The prediction of postoperative liver function remains a largely subjective practice based on CT volumetric analysis. However, future liver volume after a hepatectomy is not the only factor that contributes to postoperative liver function and outcomes. In this prospective trial, 185 consecutive patients who underwent liver operations between 2014 and 2015 were studied. Volumetric and functional rates of remnant liver were measured using technetium 99m-galactosyl human serum albumin single-photon emission computed tomography/CT fusion imaging to evaluate post-hepatectomy remnant liver function. Remnant indocyanine green clearance rate using galactosyl (KGSA) (KGSA × functional rate) was used to predict future remnant liver function. Hepatectomy was considered safe for patients with remnant KGSA values ≥0.05, and the primary end point was to determine the accuracy and reliability of this criteria. The prediction of the 90-day major complication and mortality rates was assessed. Median hospital stay was 9 days and median ICU stay was 1 day, with only 1 in-hospital death (90-day mortality rate 0.5%). Overall morbidity rate evaluated according to the Clavien-Dindo classification was 9%. For post-hepatectomy liver failure definitions, the International Study Group of Liver Surgery definition was fulfilled in 14 patients (8%), with the majority being grade B (50%), compared with 2 patients (1%) fulfilling the "50-50" criteria, and 0 patients (0%) fulfilling the Peak Bili >7 criteria. Results of this study showed that remnant KGSA provided information that allowed us to predict remnant liver function. This information will be important for surgeons when deciding on a treatment plan for patients with liver diseases. (ClinicalTrials.gov ID: NCT02013895). Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Classification systems for natural resource management

USGS Publications Warehouse

Kleckner, Richard L.

1981-01-01

Resource managers employ various types of resource classification systems in their management activities such as inventory, mapping, and data analysis. Classification is the ordering or arranging of objects into groups or sets on the basis of their relationships, and as such, provide the resource managers with a structure for organizing their needed information. In addition of conforming to certain logical principles, resource classifications should be flexible, widely applicable to a variety of environmental conditions, and useable with minimal training. The process of classification may be approached from the bottom up (aggregation) or the top down (subdivision) or a combination of both, depending on the purpose of the classification. Most resource classification systems in use today focus on a single resource and are used for a single, limited purpose. However, resource managers now must employ the concept of multiple use in their management activities. What they need is an integrated, ecologically based approach to resource classification which would fulfill multiple-use mandates. In an effort to achieve resource-data compatibility and data sharing among Federal agencies, and interagency agreement has been signed by five Federal agencies to coordinate and cooperate in the area of resource classification and inventory.

Classification of cloud fields based on textural characteristics

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Chen, D. W.

1987-01-01

The present study reexamines the applicability of texture-based features for automatic cloud classification using very high spatial resolution (57 m) Landsat multispectral scanner digital data. It is concluded that cloud classification can be accomplished using only a single visible channel.

Comparing the Behavior of Polarimetric SAR Imagery (TerraSAR-X and Radarsat-2) for Automated Sea Ice Classification

NASA Astrophysics Data System (ADS)

Ressel, Rudolf; Singha, Suman; Lehner, Susanne

2016-08-01

Arctic Sea ice monitoring has attracted increasing attention over the last few decades. Besides the scientific interest in sea ice, the operational aspect of ice charting is becoming more important due to growing navigational possibilities in an increasingly ice free Arctic. For this purpose, satellite borne SAR imagery has become an invaluable tool. In past, mostly single polarimetric datasets were investigated with supervised or unsupervised classification schemes for sea ice investigation. Despite proven sea ice classification achievements on single polarimetric data, a fully automatic, general purpose classifier for single-pol data has not been established due to large variation of sea ice manifestations and incidence angle impact. Recently, through the advent of polarimetric SAR sensors, polarimetric features have moved into the focus of ice classification research. The higher information content four polarimetric channels promises to offer greater insight into sea ice scattering mechanism and overcome some of the shortcomings of single- polarimetric classifiers. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction).

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task

PubMed Central

2017-01-01

Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making. PMID:28961245

Natural stimuli improve auditory BCIs with respect to ergonomics and performance

NASA Astrophysics Data System (ADS)

Höhne, Johannes; Krenzlin, Konrad; Dähne, Sven; Tangermann, Michael

2012-08-01

Moving from well-controlled, brisk artificial stimuli to natural and less-controlled stimuli seems counter-intuitive for event-related potential (ERP) studies. As natural stimuli typically contain a richer internal structure, they might introduce higher levels of variance and jitter in the ERP responses. Both characteristics are unfavorable for a good single-trial classification of ERPs in the context of a multi-class brain-computer interface (BCI) system, where the class-discriminant information between target stimuli and non-target stimuli must be maximized. For the application in an auditory BCI system, however, the transition from simple artificial tones to natural syllables can be useful despite the variance introduced. In the presented study, healthy users (N = 9) participated in an offline auditory nine-class BCI experiment with artificial and natural stimuli. It is shown that the use of syllables as natural stimuli does not only improve the users’ ergonomic ratings; also the classification performance is increased. Moreover, natural stimuli obtain a better balance in multi-class decisions, such that the number of systematic confusions between the nine classes is reduced. Hopefully, our findings may contribute to make auditory BCI paradigms more user friendly and applicable for patients.

Suppressed Alpha Oscillations Predict Intelligibility of Speech and its Acoustic Details

PubMed Central

Weisz, Nathan

2012-01-01

Modulations of human alpha oscillations (8–13 Hz) accompany many cognitive processes, but their functional role in auditory perception has proven elusive: Do oscillatory dynamics of alpha reflect acoustic details of the speech signal and are they indicative of comprehension success? Acoustically presented words were degraded in acoustic envelope and spectrum in an orthogonal design, and electroencephalogram responses in the frequency domain were analyzed in 24 participants, who rated word comprehensibility after each trial. First, the alpha power suppression during and after a degraded word depended monotonically on spectral and, to a lesser extent, envelope detail. The magnitude of this alpha suppression exhibited an additional and independent influence on later comprehension ratings. Second, source localization of alpha suppression yielded superior parietal, prefrontal, as well as anterior temporal brain areas. Third, multivariate classification of the time–frequency pattern across participants showed that patterns of late posterior alpha power allowed best for above-chance classification of word intelligibility. Results suggest that both magnitude and topography of late alpha suppression in response to single words can indicate a listener's sensitivity to acoustic features and the ability to comprehend speech under adverse listening conditions. PMID:22100354

Computer implemented classification of vegetation using aircraft acquired multispectral scanner data

NASA Technical Reports Server (NTRS)

Cibula, W. G.

1975-01-01

The use of aircraft 24-channel multispectral scanner data in conjunction with computer processing techniques to obtain an automated classification of plant species association was discussed. The classification of various plant species associations was related to information needed for specific applications. In addition, the necessity for multiple selection of training fields for a single class in situations where the study area consists of highly irregular terrain was detailed. A single classification was illuminated differently in different areas, resulting in the existence of multiple spectral signatures for a given class. These different signatures result since different qualities of radiation upwell to the detector from portions that have differing qualities of incident radiation. Techniques of training field selection were outlined, and a classification obtained from a natural area in Tishomingo State Park in northern Mississippi was presented.

Prognostic Classification Factors Associated With Development of Multiple Autoantibodies, Dysglycemia, and Type 1 Diabetes-A Recursive Partitioning Analysis.

PubMed

Xu, Ping; Krischer, Jeffrey P

2016-06-01

To define prognostic classification factors associated with the progression from single to multiple autoantibodies, multiple autoantibodies to dysglycemia, and dysglycemia to type 1 diabetes onset in relatives of individuals with type 1 diabetes. Three distinct cohorts of subjects from the Type 1 Diabetes TrialNet Pathway to Prevention Study were investigated separately. A recursive partitioning analysis (RPA) was used to determine the risk classes. Clinical characteristics, including genotype, antibody titers, and metabolic markers were analyzed. Age and GAD65 autoantibody (GAD65Ab) titers defined three risk classes for progression from single to multiple autoantibodies. The 5-year risk was 11% for those subjects >16 years of age with low GAD65Ab titers, 29% for those ≤16 years of age with low GAD65Ab titers, and 45% for those subjects with high GAD65Ab titers regardless of age. Progression to dysglycemia was associated with islet antigen 2 Ab titers, and 2-h glucose and fasting C-peptide levels. The 5-year risk is 28%, 39%, and 51% for respective risk classes defined by the three predictors. Progression to type 1 diabetes was associated with the number of positive autoantibodies, peak C-peptide level, HbA1c level, and age. Four risk classes defined by RPA had a 5-year risk of 9%, 33%, 62%, and 80%, respectively. The use of RPA offered a new classification approach that could predict the timing of transitions from one preclinical stage to the next in the development of type 1 diabetes. Using these RPA classes, new prevention techniques can be tailored based on the individual prognostic risk characteristics at different preclinical stages. © 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

Hierarchic Agglomerative Clustering Methods for Automatic Document Classification.

ERIC Educational Resources Information Center

Griffiths, Alan; And Others

1984-01-01

Considers classifications produced by application of single linkage, complete linkage, group average, and word clustering methods to Keen and Cranfield document test collections, and studies structure of hierarchies produced, extent to which methods distort input similarity matrices during classification generation, and retrieval effectiveness…

Effect of cerebrolysin on gross motor function of children with cerebral palsy: a clinical trial.

PubMed

Nasiri, Jafar; Safavifar, Faezeh

2017-06-01

Gross motor dysfunction is considered as the most challenging problem in cerebral palsy (CP). It is proven that improvement of gross motor function could reduce CP-related disabilities and provide better quality of life in this group of patients. Therefore, the aim of this trial is to evaluate the effectiveness of cerebrolysin (CBL) on gross motor function of children with CP who are undergoing treatment. In this clinical trial study, paediatric patients aged 18-75 months with spastic diplegic or quadriplegic cerebral palsy, who were under rehabilitation therapy, were selected and randomly allocated in control and CBL groups. Patients in CBL group underwent treatment with standard rehabilitation therapy plus CBL. The latter was administrated intramuscularly as a single daily dose of 0.1 cc/kg for 10 days and then continued weekly for 4 months. Gross motor function of participants in the two studied groups, before and after trial, was evaluated and compared using the validated Persian version of gross motor function classification system-expanded and revised (GMFCS-E&R). During this trial, 108 patients with CP were evaluated for eligibility. From these, 50 patients were enrolled and randomly allocated in the CBL and control groups. Four months after trial, the mean level of GMFCS decreased significantly in the two groups (P < 0.05). However, it was significantly lower in the CBL group than in the control group (2.1 vs. 3.16, P < 0.05). The results of this trial indicated that CBL could improve gross motor function in patients with CP. This finding is consistent with neurotrophic and neuroprotective effects of CBL, which have been reported in various clinical trials in other neurological disorders. Further studies are recommended to establish the value of continued neuroprotection and to determine the pharmacokinetics/dynamics of CBL in this group of patients.

Classification effects of real and imaginary movement selective attention tasks on a P300-based brain-computer interface

NASA Astrophysics Data System (ADS)

Salvaris, Mathew; Sepulveda, Francisco

2010-10-01

Brain-computer interfaces (BCIs) rely on various electroencephalography methodologies that allow the user to convey their desired control to the machine. Common approaches include the use of event-related potentials (ERPs) such as the P300 and modulation of the beta and mu rhythms. All of these methods have their benefits and drawbacks. In this paper, three different selective attention tasks were tested in conjunction with a P300-based protocol (i.e. the standard counting of target stimuli as well as the conduction of real and imaginary movements in sync with the target stimuli). The three tasks were performed by a total of 10 participants, with the majority (7 out of 10) of the participants having never before participated in imaginary movement BCI experiments. Channels and methods used were optimized for the P300 ERP and no sensory-motor rhythms were explicitly used. The classifier used was a simple Fisher's linear discriminant. Results were encouraging, showing that on average the imaginary movement achieved a P300 versus No-P300 classification accuracy of 84.53%. In comparison, mental counting, the standard selective attention task used in previous studies, achieved 78.9% and real movement 90.3%. Furthermore, multiple trial classification results were recorded and compared, with real movement reaching 99.5% accuracy after four trials (12.8 s), imaginary movement reaching 99.5% accuracy after five trials (16 s) and counting reaching 98.2% accuracy after ten trials (32 s).

Classification effects of real and imaginary movement selective attention tasks on a P300-based brain-computer interface.

PubMed

Salvaris, Mathew; Sepulveda, Francisco

2010-10-01

Brain-computer interfaces (BCIs) rely on various electroencephalography methodologies that allow the user to convey their desired control to the machine. Common approaches include the use of event-related potentials (ERPs) such as the P300 and modulation of the beta and mu rhythms. All of these methods have their benefits and drawbacks. In this paper, three different selective attention tasks were tested in conjunction with a P300-based protocol (i.e. the standard counting of target stimuli as well as the conduction of real and imaginary movements in sync with the target stimuli). The three tasks were performed by a total of 10 participants, with the majority (7 out of 10) of the participants having never before participated in imaginary movement BCI experiments. Channels and methods used were optimized for the P300 ERP and no sensory-motor rhythms were explicitly used. The classifier used was a simple Fisher's linear discriminant. Results were encouraging, showing that on average the imaginary movement achieved a P300 versus No-P300 classification accuracy of 84.53%. In comparison, mental counting, the standard selective attention task used in previous studies, achieved 78.9% and real movement 90.3%. Furthermore, multiple trial classification results were recorded and compared, with real movement reaching 99.5% accuracy after four trials (12.8 s), imaginary movement reaching 99.5% accuracy after five trials (16 s) and counting reaching 98.2% accuracy after ten trials (32 s).

Influence of P300 latency jitter on event related potential-based brain-computer interface performance

NASA Astrophysics Data System (ADS)

Aricò, P.; Aloise, F.; Schettini, F.; Salinari, S.; Mattia, D.; Cincotti, F.

2014-06-01

Objective. Several ERP-based brain-computer interfaces (BCIs) that can be controlled even without eye movements (covert attention) have been recently proposed. However, when compared to similar systems based on overt attention, they displayed significantly lower accuracy. In the current interpretation, this is ascribed to the absence of the contribution of short-latency visual evoked potentials (VEPs) in the tasks performed in the covert attention modality. This study aims to investigate if this decrement (i) is fully explained by the lack of VEP contribution to the classification accuracy; (ii) correlates with lower temporal stability of the single-trial P300 potentials elicited in the covert attention modality. Approach. We evaluated the latency jitter of P300 evoked potentials in three BCI interfaces exploiting either overt or covert attention modalities in 20 healthy subjects. The effect of attention modality on the P300 jitter, and the relative contribution of VEPs and P300 jitter to the classification accuracy have been analyzed. Main results. The P300 jitter is higher when the BCI is controlled in covert attention. Classification accuracy negatively correlates with jitter. Even disregarding short-latency VEPs, overt-attention BCI yields better accuracy than covert. When the latency jitter is compensated offline, the difference between accuracies is not significant. Significance. The lower temporal stability of the P300 evoked potential generated during the tasks performed in covert attention modality should be regarded as the main contributing explanation of lower accuracy of covert-attention ERP-based BCIs.

Social intuition as a form of implicit learning: Sequences of body movements are learned less explicitly than letter sequences

PubMed Central

Norman, Elisabeth; Price, Mark C.

2012-01-01

In the current paper, we first evaluate the suitability of traditional serial reaction time (SRT) and artificial grammar learning (AGL) experiments for measuring implicit learning of social signals. We then report the results of a novel sequence learning task which combines aspects of the SRT and AGL paradigms to meet our suggested criteria for how implicit learning experiments can be adapted to increase their relevance to situations of social intuition. The sequences followed standard finite-state grammars. Sequence learning and consciousness of acquired knowledge were compared between 2 groups of 24 participants viewing either sequences of individually presented letters or sequences of body-posture pictures, which were described as series of yoga movements. Participants in both conditions showed above-chance classification accuracy, indicating that sequence learning had occurred in both stimulus conditions. This shows that sequence learning can still be found when learning procedures reflect the characteristics of social intuition. Rule awareness was measured using trial-by-trial evaluation of decision strategy (Dienes & Scott, 2005; Scott & Dienes, 2008). For letters, sequence classification was best on trials where participants reported responding on the basis of explicit rules or memory, indicating some explicit learning in this condition. For body-posture, classification was not above chance on these types of trial, but instead showed a trend to be best on those trials where participants reported that their responses were based on intuition, familiarity, or random choice, suggesting that learning was more implicit. Results therefore indicate that the use of traditional stimuli in research on sequence learning might underestimate the extent to which learning is implicit in domains such as social learning, contributing to ongoing debate about levels of conscious awareness in implicit learning. PMID:22679467

Assessment of Suicidal Ideation and Behavior: Report of the International Society for CNS Clinical Trials and Methodology Consensus Meeting.

PubMed

Chappell, Phillip B; Stewart, Michelle; Alphs, Larry; DiCesare, Franco; DuBrava, Sarah; Harkavy-Friedman, Jill; Lim, Pilar; Ratcliffe, Sian; Silverman, Morton M; Targum, Steven D; Marder, Stephen R

2017-06-01

To develop consensus recommendations for assessment of suicidal ideation/suicidal behavior (SI/SB) in clinical trials. Stakeholders from academia, industry, regulatory agencies, National Institutes of Health, National Institute of Mental Health, and patient advocacy organizations participated in a consensus meeting that was sponsored by the International Society for CNS Clinical Trials and Methodology and held November 17-18, 2015. Prior to the meeting, teams of experts identified key areas of consensus and dissent related to SI/SB. The most critical issues were presented and discussed in the consensus meeting. Literature reviews and a pre-meeting survey were conducted. Findings were discussed in pre-meeting working group sessions and at the consensus meeting. Five pre-meeting working groups reviewed (1) nomenclature and classification schemes for SI/SB, (2) detection and assessment of SI/SB, (3) analysis of SI/SB data, (4) design of clinical trials for new treatments of SI/SB, and (5) public health approaches to SI/SB. A modification of the RAND/UCLA Appropriateness Method was used to combine review of scientific evidence with the collective views of experts and stakeholders to reach the final consensus statements. After discussion, all attendees voted using an electronic interactive audience response system. Areas of agreement and areas of continuing dissent were recorded. All 5 working groups agreed that a major barrier to advancement of the field of SI/SB research and the development of new treatments for SI/SB remains the lack of a universally accepted standardized nomenclature and classification system. Achieving alignment on definitions and classification of suicide-related phenomena is critical to improving the detection and assessment of SI/SB, the design of clinical trials for new treatments, and effective public health interventions. © Copyright 2017 Physicians Postgraduate Press, Inc.

Bypass versus Angioplasty in Severe Ischaemia of the Leg (BASIL) trial: A description of the severity and extent of disease using the Bollinger angiogram scoring method and the TransAtlantic Inter-Society Consensus II classification.

PubMed

Bradbury, Andrew W; Adam, Donald J; Bell, Jocelyn; Forbes, John F; Fowkes, F Gerry R; Gillespie, Ian; Ruckley, Charles Vaughan; Raab, Gillian M

2010-05-01

The Bypass versus Angioplasty in Severe Ischaemia of the Leg (BASIL) trial showed in patients with severe lower limb ischemia (rest pain, tissue loss) who survive for 2 years after intervention that initial randomization to bypass surgery, compared with balloon angioplasty, was associated with an improvement in subsequent amputation-free survival and overall survival of about 6 and 7 months, respectively. The aim of this report is to describe the angiographic severity and extent of infrainguinal arterial disease in the BASIL trial cohort so that the trial outcomes can be appropriately generalized to other patient cohorts with similar anatomic (angiographic) patterns of disease. Preintervention angiograms were scored using the Bollinger method and the TransAtlantic Inter-Society Consensus (TASC) II classification system by three consultant interventional radiologists and two consultant vascular surgeons unaware of the treatment received or patient outcomes. As was to be expected from the randomization process, patients in the two trial arms were well matched in terms of angiographic severity and extent of disease as documented by Bollinger and TASC II. In patients with the least overall disease, it tended to be concentrated in the superficial femoral and popliteal arteries, which were the commonest sites of disease overall. The below knee arteries became increasingly involved as the overall severity of disease increased, but the disease in the above knee arteries did not tend to worsen. The posterior tibial artery was the most diseased crural artery, whereas the peroneal appeared relatively spared. There was less interobserver disagreement with the Bollinger method than with the TASC II classification system, which also appears inherently less sensitive to clinically important differences in infrapopliteal disease among patients with severe leg ischemia. Anatomic (angiographic) disease description in patients with severe leg ischemia requires a reproducible scoring system that is sensitive to differences in crural artery disease. The Bollinger system appears well suited for this purpose, but the TASC II classification system less so. We hope this detailed analysis will facilitate appropriate generalization of the BASIL trial data to other groups of patients affected by similar anatomic (angiographic) patterns of disease. Crown Copyright (c) 2010. Published by Mosby, Inc. All rights reserved.

Spatial Classification of Orchards and Vineyards with High Spatial Resolution Panchromatic Imagery

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

Warner, Timothy; Steinmaus, Karen L.

2005-02-01

New high resolution single spectral band imagery offers the capability to conduct image classifications based on spatial patterns in imagery. A classification algorithm based on autocorrelation patterns was developed to automatically extract orchards and vineyards from satellite imagery. The algorithm was tested on IKONOS imagery over Granger, WA, which resulted in a classification accuracy of 95%.

Anatomical classification of breast sentinel lymph nodes using computed tomography-lymphography.

PubMed

Fujita, Tamaki; Miura, Hiroyuki; Seino, Hiroko; Ono, Shuichi; Nishi, Takashi; Nishimura, Akimasa; Hakamada, Kenichi; Aoki, Masahiko

2018-05-03

To evaluate the anatomical classification and location of breast sentinel lymph nodes, preoperative computed tomography-lymphography examinations were retrospectively reviewed for sentinel lymph nodes in 464 cases clinically diagnosed with node-negative breast cancer between July 2007 and June 2016. Anatomical classification was performed based on the numbers of lymphatic routes and sentinel lymph nodes, the flow direction of lymphatic routes, and the location of sentinel lymph nodes. Of the 464 cases reviewed, anatomical classification could be performed in 434 (93.5 %). The largest number of cases showed single route/single sentinel lymph node (n = 296, 68.2 %), followed by multiple routes/multiple sentinel lymph nodes (n = 59, 13.6 %), single route/multiple sentinel lymph nodes (n = 53, 12.2 %), and multiple routes/single sentinel lymph node (n = 26, 6.0 %). Classification based on the flow direction of lymphatic routes showed that 429 cases (98.8 %) had outward flow on the superficial fascia toward axillary lymph nodes, whereas classification based on the height of sentinel lymph nodes showed that 323 cases (74.4 %) belonged to the upper pectoral group of axillary lymph nodes. There was wide variation in the number of lymphatic routes and their branching patterns and in the number, location, and direction of flow of sentinel lymph nodes. It is clinically very important to preoperatively understand the anatomical morphology of lymphatic routes and sentinel lymph nodes for optimal treatment of breast cancer, and computed tomography-lymphography is suitable for this purpose.

Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI.

PubMed

Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R

2017-04-01

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (<35 Hz) frequency ranges. MBRLAF is computationally more demanding than RLAF, but highly effective in all EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We introduce new algorithms for reducing EEG artifacts due to simultaneous fMRI The algorithms combine a reference layer and adaptive filtering Several evaluation criteria suggest superior effectivity in terms of artifact reduction We demonstrate that physiological EEG components are preserved.

Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI

NASA Astrophysics Data System (ADS)

Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R.

2017-04-01

Objective. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. Approach. To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. Main results. The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (<35 Hz) frequency ranges. MBRLAF is computationally more demanding than RLAF, but highly effective in all EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. Significance. In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We introduce new algorithms for reducing EEG artifacts due to simultaneous fMRI The algorithms combine a reference layer and adaptive filtering Several evaluation criteria suggest superior effectivity in terms of artifact reduction We demonstrate that physiological EEG components are preserved

Nonlinear features for classification and pose estimation of machined parts from single views

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-10-01

A new nonlinear feature extraction method is presented for classification and pose estimation of objects from single views. The feature extraction method is called the maximum representation and discrimination feature (MRDF) method. The nonlinear MRDF transformations to use are obtained in closed form, and offer significant advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We consider MRDFs on image data, provide a new 2-stage nonlinear MRDF solution, and show it specializes to well-known linear and nonlinear image processing transforms under certain conditions. We show the use of MRDF in estimating the class and pose of images of rendered solid CAD models of machine parts from single views using a feature-space trajectory neural network classifier. We show new results with better classification and pose estimation accuracy than are achieved by standard principal component analysis and Fukunaga-Koontz feature extraction methods.

Detection of P300 waves in single trials by the wavelet transform (WT).

PubMed

Demiralp, T; Ademoglu, A; Schürmann, M; Başar-Eroglu, C; Başar, E

1999-01-01

The P300 response is conventionally obtained by averaging the responses to the task-relevant (target) stimuli of the oddball paradigm. However, it is well known that cognitive ERP components show a high variability due to changes of cognitive state during an experimental session. With simple tasks such changes may not be demonstrable by the conventional method of averaging the sweeps chosen according to task-relevance. Therefore, the present work employed a response-based classification procedure to choose the trials containing the P300 component from the whole set of sweeps of an auditory oddball paradigm. For this purpose, the most significant response property reflecting the P300 wave was identified by using the wavelet transform (WT). The application of a 5 octave quadratic B-spline-WT on single sweeps yielded discrete coefficients in each octave with an appropriate time resolution for each frequency range. The main feature indicating a P300 response was the positivity of the 4th delta (0.5-4 Hz) coefficient (310-430 ms) after stimulus onset. The average of selected single sweeps from the whole set of data according to this criterion yielded more enhanced P300 waves compared with the average of the target responses, and the average of the remaining sweeps showed a significantly smaller positivity in the P300 latency range compared with the average of the non-target responses. The combination of sweeps classified according to the task-based and response-based criteria differed significantly. This suggests an influence of changes in cognitive state on the presence of the P300 wave which cannot be assessed by task performance alone. Copyright 1999 Academic Press.

Multi-material classification of dry recyclables from municipal solid waste based on thermal imaging.

PubMed

Gundupalli, Sathish Paulraj; Hait, Subrata; Thakur, Atul

2017-12-01

There has been a significant rise in municipal solid waste (MSW) generation in the last few decades due to rapid urbanization and industrialization. Due to the lack of source segregation practice, a need for automated segregation of recyclables from MSW exists in the developing countries. This paper reports a thermal imaging based system for classifying useful recyclables from simulated MSW sample. Experimental results have demonstrated the possibility to use thermal imaging technique for classification and a robotic system for sorting of recyclables in a single process step. The reported classification system yields an accuracy in the range of 85-96% and is comparable with the existing single-material recyclable classification techniques. We believe that the reported thermal imaging based system can emerge as a viable and inexpensive large-scale classification-cum-sorting technology in recycling plants for processing MSW in developing countries. Copyright © 2017 Elsevier Ltd. All rights reserved.

Single-accelerometer-based daily physical activity classification.

PubMed

Long, Xi; Yin, Bin; Aarts, Ronald M

2009-01-01

In this study, a single tri-axial accelerometer placed on the waist was used to record the acceleration data for human physical activity classification. The data collection involved 24 subjects performing daily real-life activities in a naturalistic environment without researchers' intervention. For the purpose of assessing customers' daily energy expenditure, walking, running, cycling, driving, and sports were chosen as target activities for classification. This study compared a Bayesian classification with that of a Decision Tree based approach. A Bayes classifier has the advantage to be more extensible, requiring little effort in classifier retraining and software update upon further expansion or modification of the target activities. Principal components analysis was applied to remove the correlation among features and to reduce the feature vector dimension. Experiments using leave-one-subject-out and 10-fold cross validation protocols revealed a classification accuracy of approximately 80%, which was comparable with that obtained by a Decision Tree classifier.

Comparison of using single- or multi-polarimetric TerraSAR-X images for segmentation and classification of man-made maritime objects

NASA Astrophysics Data System (ADS)

Teutsch, Michael; Saur, Günter

2011-11-01

Spaceborne SAR imagery offers high capability for wide-ranging maritime surveillance especially in situations, where AIS (Automatic Identification System) data is not available. Therefore, maritime objects have to be detected and optional information such as size, orientation, or object/ship class is desired. In recent research work, we proposed a SAR processing chain consisting of pre-processing, detection, segmentation, and classification for single-polarimetric (HH) TerraSAR-X StripMap images to finally assign detection hypotheses to class "clutter", "non-ship", "unstructured ship", or "ship structure 1" (bulk carrier appearance) respectively "ship structure 2" (oil tanker appearance). In this work, we extend the existing processing chain and are now able to handle full-polarimetric (HH, HV, VH, VV) TerraSAR-X data. With the possibility of better noise suppression using the different polarizations, we slightly improve both the segmentation and the classification process. In several experiments we demonstrate the potential benefit for segmentation and classification. Precision of size and orientation estimation as well as correct classification rates are calculated individually for single- and quad-polarization and compared to each other.

From comparison to classification: a cortical tool for boosting perception.

PubMed

Nahum, Mor; Daikhin, Luba; Lubin, Yedida; Cohen, Yamit; Ahissar, Merav

2010-01-20

Humans are much better in relative than in absolute judgments. This common assertion is based on findings that discrimination thresholds are much lower when measured with methods that allow interstimuli comparisons than when measured with methods that require classification of one stimulus at a time and are hence sensitive to memory load. We now challenged this notion by measuring discrimination thresholds and evoked potentials while listeners performed a two-tone frequency discrimination task. We tested various protocols that differed in the pattern of cross-trial tone repetition. We found that best performance was achieved only when listeners effectively used cross-trial repetition to avoid interstimulus comparisons with the repeated reference tone. Instead, they classified one tone, the nonreference tone, as either high or low by comparing it with a recently formed internal reference. Listeners were not aware of the switch from interstimulus comparison to classification. Its successful use was revealed by the conjunction of improved behavioral performance and an event-related potential component (P3), indicating an implicit perceptual decision, which followed the nonreference tone in each trial. Interestingly, tone repetition itself did not suffice for the switch, implying that the bottleneck to discrimination does not reside at the lower, sensory stage. Rather, the temporal consistency of repetition was important, suggesting the involvement of higher-level mechanisms with longer time constants. These findings suggest that classification is based on more automatic and accurate mechanisms than interstimulus comparisons and that the ability to effectively use them depends on a dynamic interplay between higher- and lower-level cortical mechanisms.

Factors affecting the causality assessment of adverse events following immunisation in paediatric clinical trials: An online survey.

PubMed

Voysey, Merryn; Tavana, Rahele; Farooq, Yama; Heath, Paul T; Bonhoeffer, Jan; Snape, Matthew D

2015-12-16

Serious adverse events (SAEs) in clinical trials require reporting within 24h, including a judgment of whether the SAE was related to the investigational product(s). Such assessments are an important component of pharmacovigilance, however classification systems for assigning relatedness vary across study protocols. This on-line survey evaluated the consistency of SAE causality assessment among professionals with vaccine clinical trial experience. Members of the clinical advisory forum of experts (CAFÉ), a Brighton Collaboration online-forum, were emailed a survey containing SAEs from hypothetical vaccine trials which they were asked to classify. Participants were randomised to either two classification options (related/not related to study immunisation) or three options (possibly/probably/unrelated). The clinical scenarios, were (i) leukaemia diagnosed 5 months post-immunisation with a live RSV vaccine, (ii) juvenile idiopathic arthritis (JIA) 3 months post-immunisation with a group A streptococcal vaccine, (iii) developmental delay diagnosed at age 10 months after infant capsular group B meningococcal vaccine, (iv) developmental delay diagnosed at age 10 months after maternal immunisation with a group B streptococcal vaccine. There were 140 respondents (72 two options, 68 three options). Across all respondents, SAEs were considered related to study immunisation by 28% (leukaemia), 74% (JIA), 29% (developmental delay after infant immunisation) and 42% (developmental delay after maternal immunisation). Having only two options made respondents significantly less likely to classify the SAE as immunisation-related for two scenarios (JIA p=0.0075; and maternal immunisation p=0.045). Amongst study investigators (n=43) this phenomenon was observed for three of the four scenarios: (JIA p=0.0236; developmental delay following infant immunisation p=0.0266; and developmental delay after maternal immunisation p=0.0495). SAE causality assessment is inconsistent amongst study investigators and can be influenced by the classification systems available to them. There is a pressing need for SAE classification systems to be standardised across study protocols. Copyright © 2015 Elsevier Ltd. All rights reserved.

Oesophageal diverticula: principles of management and appraisal of classification.

PubMed Central

Borrie, J; Wilson, R L

1980-01-01

In this paper we review a consecutive series of 50 oesophageal diverticula, appraise clinical features and methods of management, and suggest an improvement on the World Health Organization classification. The link between oesophageal diverticula and motor disorders as assessed by oesophageal manometry is stressed. It is necessary to correct the functional disorder as well as the diverticulum if it is causing symptoms. A revised classification could be as follows: congenital--single or multiple; acquired--single (cricopharyngeal, mid-oesophageal, epiphrenic other) or multiple (for example, when cricopharyngeal and mid-oesophageal present together, or when there is intramural diverticulosis. Images PMID:6781091

Utilizing Retinotopic Mapping for a Multi-Target SSVEP BCI With a Single Flicker Frequency.

PubMed

Maye, Alexander; Zhang, Dan; Engel, Andreas K

2017-07-01

In brain-computer interfaces (BCIs) that use the steady-state visual evoked response (SSVEP), the user selects a control command by directing attention overtly or covertly to one out of several flicker stimuli. The different control channels are encoded in the frequency, phase, or time domain of the flicker signals. Here, we present a new type of SSVEP BCI, which uses only a single flicker stimulus and yet affords controlling multiple channels. The approach rests on the observation that the relative position between the stimulus and the foci of overt attention result in distinct topographies of the SSVEP response on the scalp. By classifying these topographies, the computer can determine at which position the user is gazing. Offline data analysis in a study on 12 healthy volunteers revealed that 9 targets can be recognized with about 95±3% accuracy, corresponding to an information transfer rate (ITR) of 40.8 ± 3.3 b/min on average. We explored how the classification accuracy is affected by the number of control channels, the trial length, and the number of EEG channels. Our findings suggest that the EEG data from five channels over parieto-occipital brain areas are sufficient for reliably classifying the topographies and that there is a large potential to improve the ITR by optimizing the trial length. The robust performance and the simple stimulation setup suggest that this approach is a prime candidate for applications on desktop and tablet computers.

Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials

PubMed Central

Miotto, Riccardo

2015-01-01

Objective To develop a cost-effective, case-based reasoning framework for clinical research eligibility screening by only reusing the electronic health records (EHRs) of minimal enrolled participants to represent the target patient for each trial under consideration. Materials and Methods The EHR data—specifically diagnosis, medications, laboratory results, and clinical notes—of known clinical trial participants were aggregated to profile the “target patient” for a trial, which was used to discover new eligible patients for that trial. The EHR data of unseen patients were matched to this “target patient” to determine their relevance to the trial; the higher the relevance, the more likely the patient was eligible. Relevance scores were a weighted linear combination of cosine similarities computed over individual EHR data types. For evaluation, we identified 262 participants of 13 diversified clinical trials conducted at Columbia University as our gold standard. We ran a 2-fold cross validation with half of the participants used for training and the other half used for testing along with other 30 000 patients selected at random from our clinical database. We performed binary classification and ranking experiments. Results The overall area under the ROC curve for classification was 0.95, enabling the highlight of eligible patients with good precision. Ranking showed satisfactory results especially at the top of the recommended list, with each trial having at least one eligible patient in the top five positions. Conclusions This relevance-based method can potentially be used to identify eligible patients for clinical trials by processing patient EHR data alone without parsing free-text eligibility criteria, and shows promise of efficient “case-based reasoning” modeled only on minimal trial participants. PMID:25769682

Single-trial dynamics of motor cortex and their applications to brain-machine interfaces

PubMed Central

Kao, Jonathan C.; Nuyujukian, Paul; Ryu, Stephen I.; Churchland, Mark M.; Cunningham, John P.; Shenoy, Krishna V.

2015-01-01

Increasing evidence suggests that neural population responses have their own internal drive, or dynamics, that describe how the neural population evolves through time. An important prediction of neural dynamical models is that previously observed neural activity is informative of noisy yet-to-be-observed activity on single-trials, and may thus have a denoising effect. To investigate this prediction, we built and characterized dynamical models of single-trial motor cortical activity. We find these models capture salient dynamical features of the neural population and are informative of future neural activity on single trials. To assess how neural dynamics may beneficially denoise single-trial neural activity, we incorporate neural dynamics into a brain–machine interface (BMI). In online experiments, we find that a neural dynamical BMI achieves substantially higher performance than its non-dynamical counterpart. These results provide evidence that neural dynamics beneficially inform the temporal evolution of neural activity on single trials and may directly impact the performance of BMIs. PMID:26220660

Music in mind, a randomized controlled trial of music therapy for young people with behavioural and emotional problems: study protocol.

PubMed

Porter, Sam; Holmes, Valerie; McLaughlin, Katrina; Lynn, Fiona; Cardwell, Chris; Braiden, Hannah-Jane; Doran, Jackie; Rogan, Sheelagh

2012-10-01

This article is a report of a trial protocol to determine if improvizational music therapy leads to clinically significant improvement in communication and interaction skills for young people experiencing social, emotional or behavioural problems. Music therapy is often considered an effective intervention for young people experiencing social, emotional or behavioural difficulties. However, this assumption lacks empirical evidence. Music in mind is a multi-centred single-blind randomized controlled trial involving 200 young people (aged 8-16 years) and their parents. Eligible participants will have a working diagnosis within the ambit of international classification of disease 10 mental and behavioural disorders and will be recruited over 15 months from six centres within the Child and Adolescent Mental Health Services of a large health and social care trust in Northern Ireland. Participants will be randomly allocated in a 1:1 ratio to receive standard care alone or standard care plus 12 weekly music therapy sessions delivered by the Northern Ireland Music Therapy Trust. Baseline data will be collected from young people and their parents using standardized outcome measures for communicative and interaction skills (primary endpoint), self-esteem, social functioning, depression and family functioning. Follow-up data will be collected 1 and 13 weeks after the final music therapy session. A cost-effectiveness analysis will also be carried out. This study will be the largest trial to date examining the effect of music therapy on young people experiencing social, emotional or behavioural difficulties and will provide empirical evidence for the use of music therapy among this population. Trial registration. This study is registered in the ISRCTN Register, ISRCTN96352204. Ethical approval was gained in October 2010. © 2012 Blackwell Publishing Ltd.

A prospective picture collection study for a grading atlas of radiation dermatitis for clinical trials in head-and-neck cancer patients

PubMed Central

Zenda, Sadamoto; Ota, Yosuke; Tachibana, Hiroyuki; Ogawa, Hirofumi; Ishii, Shinobu; Hashiguchi, Chikako; Akimoto, Tetsuo; Ohe, Yuichiro; Uchitomi, Yosuke

2016-01-01

Radiation dermatitis is one of the most common acute toxicities of both radiotherapy and chemoradiotherapy. Many clinical trials have evaluated the level of toxicity using the Common Terminology Criteria for Adverse Events ver. 4.03. This criterion accounts for severity in a single sentence only, and no visual classification guide has been available. Thus, there is a risk of subjective interpretation by the individual investigator. This contrasts with the situation with hematologic toxicities, which can be interpreted objectively. The aim of this prospective picture collection study was to develop a grading tool for use in establishing the severity of radiation dermatitis in clinical trials. A total of 118 patients who were scheduled to receive definitive or postoperative radiotherapy or chemoradiotherapy were enrolled from the four participating cancer centers. All researchers in our group used the same model of camera under the same shooting conditions to maintain consistent photographic quality. In all, 1600 photographs were collected. Of these, 100 photographs qualified for the first round of selection and were then graded by six experts, basically in accordance with the CTCAE ver. 4.03 (JCOG ver. in Japanese). After further study, 38 photographs were selected as representing typical models for Grade 1–4 radiation dermatitis; the radiation dermatitis grading atlas was produced from these photographs. The atlas will play a major role in ensuring that the dermatitis rating system is consistent between the institutions participating in trials. We hope that this will contribute to improving the quality of clinical trials, and also to improving the level of routine clinical practice. PMID:26850926

Single-arm phase II trial design under parametric cure models.

PubMed

Wu, Jianrong

2015-01-01

The current practice of designing single-arm phase II survival trials is limited under the exponential model. Trial design under the exponential model may not be appropriate when a portion of patients are cured. There is no literature available for designing single-arm phase II trials under the parametric cure model. In this paper, a test statistic is proposed, and a sample size formula is derived for designing single-arm phase II trials under a class of parametric cure models. Extensive simulations showed that the proposed test and sample size formula perform very well under different scenarios. Copyright © 2015 John Wiley & Sons, Ltd.

American College of Cardiology/American Heart Association/European Society of Cardiology/World Heart Federation universal definition of myocardial infarction classification system and the risk of cardiovascular death: observations from the TRITON-TIMI 38 trial (Trial to Assess Improvement in Therapeutic Outcomes by Optimizing Platelet Inhibition With Prasugrel-Thrombolysis in Myocardial Infarction 38).

PubMed

Bonaca, Marc P; Wiviott, Stephen D; Braunwald, Eugene; Murphy, Sabina A; Ruff, Christian T; Antman, Elliott M; Morrow, David A

2012-01-31

The availability of more sensitive biomarkers of myonecrosis and a new classification system from the universal definition of myocardial infarction (MI) have led to evolution of the classification of MI. The prognostic implications of MI defined in the current era have not been well described. We investigated the association between new or recurrent MI by subtype according to the European Society of Cardiology/American College of Cardiology/American Heart Association/World Health Federation Task Force for the Redefinition of MI Classification System and the risk of cardiovascular death among 13 608 patients with acute coronary syndrome in the Trial to Assess Improvement in Therapeutic Outcomes by Optimizing Platelet Inhibition with Prasugrel-Thrombolysis in Myocardial Infarction 38 (TRITON-TIMI 38). The adjusted risk of cardiovascular death was evaluated by landmark analysis starting at the time of the MI through 180 days after the event. Patients who experienced an MI during follow-up had a higher risk of cardiovascular death at 6 months than patients without an MI (6.5% versus 1.3%, P<0.001). This higher risk was present across all subtypes of MI, including type 4a (peri-percutaneous coronary intervention, 3.2%; P<0.001) and type 4b (stent thrombosis, 15.4%; P<0.001). After adjustment for important clinical covariates, the occurrence of any MI was associated with a 5-fold higher risk of death at 6 months (95% confidence interval 3.8-7.1), with similarly increased risk across subtypes. MI is associated with a significantly increased risk of cardiovascular death, with a consistent relationship across all types as defined by the universal classification system. These findings underscore the clinical relevance of these events and the importance of therapies aimed at preventing MI.

Comparison of accelerometer cut points for predicting activity intensity in youth.

PubMed

Trost, Stewart G; Loprinzi, Paul D; Moore, Rebecca; Pfeiffer, Karin A

2011-07-01

The absence of comparative validity studies has prevented researchers from reaching consensus regarding the application of intensity-related accelerometer cut points for children and adolescents. This study aimed to evaluate the classification accuracy of five sets of independently developed ActiGraph cut points using energy expenditure, measured by indirect calorimetry, as a criterion reference standard. A total of 206 participants between the ages of 5 and 15 yr completed 12 standardized activity trials. Trials consisted of sedentary activities (lying down, writing, computer game), lifestyle activities (sweeping, laundry, throw and catch, aerobics, basketball), and ambulatory activities (comfortable walk, brisk walk, brisk treadmill walk, running). During each trial, participants wore an ActiGraph GT1M, and V˙O2 was measured breath-by-breath using the Oxycon Mobile portable metabolic system. Physical activity intensity was estimated using five independently developed cut points: Freedson/Trost (FT), Puyau (PU), Treuth (TR), Mattocks (MT), and Evenson (EV). Classification accuracy was evaluated via weighted κ statistics and area under the receiver operating characteristic curve (ROC-AUC). Across all four intensity levels, the EV (κ=0.68) and FT (κ=0.66) cut points exhibited significantly better agreement than TR (κ=0.62), MT (κ=0.54), and PU (κ=0.36). The EV and FT cut points exhibited significantly better classification accuracy for moderate- to vigorous-intensity physical activity (ROC-AUC=0.90) than TR, PU, or MT cut points (ROC-AUC=0.77-0.85). Only the EV cut points provided acceptable classification accuracy for all four levels of physical activity intensity and performed well among children of all ages. The widely applied sedentary cut point of 100 counts per minute exhibited excellent classification accuracy (ROC-AUC=0.90). On the basis of these findings, we recommend that researchers use the EV ActiGraph cut points to estimate time spent in sedentary, light-, moderate-, and vigorous-intensity activity in children and adolescents.

Columbia Classification Algorithm of Suicide Assessment (C-CASA): classification of suicidal events in the FDA's pediatric suicidal risk analysis of antidepressants.

PubMed

Posner, Kelly; Oquendo, Maria A; Gould, Madelyn; Stanley, Barbara; Davies, Mark

2007-07-01

To evaluate the link between antidepressants and suicidal behavior and ideation (suicidality) in youth, adverse events from pediatric clinical trials were classified in order to identify suicidal events. The authors describe the Columbia Classification Algorithm for Suicide Assessment (C-CASA), a standardized suicidal rating system that provided data for the pediatric suicidal risk analysis of antidepressants conducted by the Food and Drug Administration (FDA). Adverse events (N=427) from 25 pediatric antidepressant clinical trials were systematically identified by pharmaceutical companies. Randomly assigned adverse events were evaluated by three of nine independent expert suicidologists using the Columbia classification algorithm. Reliability of the C-CASA ratings and agreement with pharmaceutical company classification were estimated. Twenty-six new, possibly suicidal events (behavior and ideation) that were not originally identified by pharmaceutical companies were identified in the C-CASA, and 12 events originally labeled as suicidal by pharmaceutical companies were eliminated, which resulted in a total of 38 discrepant ratings. For the specific label of "suicide attempt," a relatively low level of agreement was observed between the C-CASA and pharmaceutical company ratings, with the C-CASA reporting a 50% reduction in ratings. Thus, although the C-CASA resulted in the identification of more suicidal events overall, fewer events were classified as suicide attempts. Additionally, the C-CASA ratings were highly reliable (intraclass correlation coefficient [ICC]=0.89). Utilizing a methodical, anchored approach to categorizing suicidality provides an accurate and comprehensive identification of suicidal events. The FDA's audit of the C-CASA demonstrated excellent transportability of this approach. The Columbia algorithm was used to classify suicidal adverse events in the recent FDA adult antidepressant safety analyses and has also been mandated to be applied to all anticonvulsant trials and other centrally acting agents and nonpsychotropic drugs.

Columbia Classification Algorithm of Suicide Assessment (C-CASA): Classification of Suicidal Events in the FDA’s Pediatric Suicidal Risk Analysis of Antidepressants

PubMed Central

Posner, Kelly; Oquendo, Maria A.; Gould, Madelyn; Stanley, Barbara; Davies, Mark

2013-01-01

Objective To evaluate the link between antidepressants and suicidal behavior and ideation (suicidality) in youth, adverse events from pediatric clinical trials were classified in order to identify suicidal events. The authors describe the Columbia Classification Algorithm for Suicide Assessment (C-CASA), a standardized suicidal rating system that provided data for the pediatric suicidal risk analysis of antide-pressants conducted by the Food and Drug Administration (FDA). Method Adverse events (N=427) from 25 pediatric antidepressant clinical trials were systematically identified by pharmaceutical companies. Randomly assigned adverse events were evaluated by three of nine independent expert suicidologists using the Columbia classification algorithm. Reliability of the C-CASA ratings and agreement with pharmaceutical company classification were estimated. Results Twenty-six new, possibly suicidal events (behavior and ideation) that were not originally identified by pharmaceutical companies were identified in the C-CASA, and 12 events originally labeled as suicidal by pharmaceutical companies were eliminated, which resulted in a total of 38 discrepant ratings. For the specific label of “suicide attempt,” a relatively low level of agreement was observed between the C-CASA and pharmaceutical company ratings, with the C-CASA reporting a 50% reduction in ratings. Thus, although the C-CASA resulted in the identification of more suicidal events overall, fewer events were classified as suicide attempts. Additionally, the C-CASA ratings were highly reliable (intraclass correlation coefficient [ICC]=0.89). Conclusions Utilizing a methodical, anchored approach to categorizing suicidality provides an accurate and comprehensive identification of suicidal events. The FDA’s audit of the C-CASA demonstrated excellent transportability of this approach. The Columbia algorithm was used to classify suicidal adverse events in the recent FDA adult antidepressant safety analyses and has also been mandated to be applied to all anticonvulsant trials and other centrally acting agents and nonpsychotropic drugs. PMID:17606655

Surgical manual of the Korean Gynecologic Oncology Group: classification of hysterectomy and lymphadenectomy

PubMed Central

Choi, Chel Hun; Chun, Yi Kyeong

2017-01-01

The Surgery Treatment Modality Committee of the Korean Gynecologic Oncologic Group (KGOG) has determined to develop a surgical manual to facilitate clinical trials and to improve communication between investigators by standardizing and precisely describing operating procedures. The literature on anatomic terminology, identification of surgical components, and surgical techniques were reviewed and discussed in depth to develop a surgical manual for gynecologic oncology. The surgical procedures provided here represent the minimum requirements for participating in a clinical trial. These procedures should be described in the operation record form, and the pathologic findings obtained from the procedures should be recorded in the pathologic report form. Here, we focused on radical hysterectomy and lymphadenectomy, and we developed a KGOG classification for those conditions. PMID:27670259

Surgical manual of the Korean Gynecologic Oncology Group: classification of hysterectomy and lymphadenectomy.

PubMed

Lee, Maria; Choi, Chel Hun; Chun, Yi Kyeong; Kim, Yun Hwan; Lee, Kwang Beom; Lee, Shin Wha; Shim, Seung Hyuk; Song, Yong Jung; Roh, Ju Won; Chang, Suk Joon; Lee, Jong Min

2017-01-01

The Surgery Treatment Modality Committee of the Korean Gynecologic Oncologic Group (KGOG) has determined to develop a surgical manual to facilitate clinical trials and to improve communication between investigators by standardizing and precisely describing operating procedures. The literature on anatomic terminology, identification of surgical components, and surgical techniques were reviewed and discussed in depth to develop a surgical manual for gynecologic oncology. The surgical procedures provided here represent the minimum requirements for participating in a clinical trial. These procedures should be described in the operation record form, and the pathologic findings obtained from the procedures should be recorded in the pathologic report form. Here, we focused on radical hysterectomy and lymphadenectomy, and we developed a KGOG classification for those conditions.

A hybrid sensing approach for pure and adulterated honey classification.

PubMed

Subari, Norazian; Mohamad Saleh, Junita; Md Shakaff, Ali Yeon; Zakaria, Ammar

2012-10-17

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.

Activity classification using the GENEA: optimum sampling frequency and number of axes.

PubMed

Zhang, Shaoyan; Murray, Peter; Zillmer, Ruediger; Eston, Roger G; Catt, Michael; Rowlands, Alex V

2012-11-01

The GENEA shows high accuracy for classification of sedentary, household, walking, and running activities when sampling at 80 Hz on three axes. It is not known whether it is possible to decrease this sampling frequency and/or the number of axes without detriment to classification accuracy. The purpose of this study was to compare the classification rate of activities on the basis of data from a single axis, two axes, and three axes, with sampling rates ranging from 5 to 80 Hz. Sixty participants (age, 49.4 yr (6.5 yr); BMI, 24.6 kg·m (3.4 kg·m)) completed 10-12 semistructured activities in the laboratory and outdoor environment while wearing a GENEA accelerometer on the right wrist. We analyzed data from single axis, dual axes, and three axes at sampling rates of 5, 10, 20, 40, and 80 Hz. Mathematical models based on features extracted from mean, SD, fast Fourier transform, and wavelet decomposition were built, which combined one of the numbers of axes with one of the sampling rates to classify activities into sedentary, household, walking, and running. Classification accuracy was high irrespective of the number of axes for data collected at 80 Hz (96.93% ± 0.97%), 40 Hz (97.4% ± 0.73%), 20 Hz (96.86% ± 1.12%), and 10 Hz (97.01% ± 1.01%) but dropped for data collected at 5 Hz (94.98% ± 1.36%). Sampling frequencies >10 Hz and/or more than one axis of measurement were not associated with greater classification accuracy. Lower sampling rates and measurement of a single axis would result in a lower data load, longer battery life, and higher efficiency of data processing. Further research should investigate whether a lower sampling rate and a single axis affects classification accuracy when considering a wider range of activities.

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

PubMed

Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan

2016-01-01

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ Imagery

EPA Science Inventory

Segmentation and object-oriented processing of single-season and multi-season Landsat-7 ETM+ data was utilized for the classification of wetlands in a 1560 km2 study area of north central Florida. This segmentation and object-oriented classification outperformed the traditional ...

[Topiramate in substance-related and addictive disorders].

PubMed

Cohen, Johan; Dervaux, Alain; Laqueille, Xavier

2014-09-01

Drug treatments used in substance use disorders are not effective in all patients. To assess the effectiveness of topiramate use in the treatment of substance use disorders. Medline database from January 1966 to December 2013, Cochrane database and clinicaltrials.gov. We used keywords topiramate, addiction, substance abuse, alcohol, tobacco, nicotine, cocaine, methamphetamine, opiate, heroin, benzodiazepine, cannabis, bulimia nervosa, binge eating disorder, gambling. All clinical trials were included. Animal trials, laboratory tests, reviews, answers to writers, case-reports, case series and publications unrelated to the topic were excluded. Twenty-eight articles investigating the efficacy of topiramate in substance use were included. In alcohol-related disorder, several trials and a meta-analysis showed a reduction of days of consumption. In a single-center trial on tobacco-related disorder, topiramate was not found effective in reducing the carbon monoxide expired. In cocaine-related disorder, one single-center trial showed a reduction of days of consumption and two single-center trials have found a trend in favour of topiramate. In alcohol and cocaine co-dependency, a single-center trial found a trend in favour of topiramate. In methamphetamine-related disorder, a multicenter trial found a trend in favour of topiramate. In bulimia nervosa, two single-center trials showed a reduction in binge eating and compensatory behaviours. In binge eating disorder, several trials showed a reduction of binge eating and weight. In gambling, one single-center trial did not show any significant results. There were no randomized controlled trials found in opioid-related disorder, benzodiazepines-related disorder, and cannabis-related disorder. Definition of abstinence and methods to assess the efficacy of topiramate differed between trials. The methodological quality of included trials was variable, especially with no double-blind procedure in eight trials. Topiramate showed interest mainly in alcoholism, binge eating disorder and bulimia nervosa. No definitive conclusions can be reached for other substance use disorders such as nicotine dependence, cocaine dependence, amphetamine dependence or cannabis dependence and for gambling. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Thymectomy in Myasthenia Gravis

PubMed Central

Aydin, Yener; Ulas, Ali Bilal; Mutlu, Vahit; Colak, Abdurrahim; Eroglu, Atilla

2017-01-01

In recent years, thymectomy has become a widespread procedure in the treatment of myasthenia gravis (MG). Likelihood of remission was highest in preoperative mild disease classification (Osserman classification 1, 2A). In absence of thymoma or hyperplasia, there was no relationship between age and gender in remission with thymectomy. In MG treatment, randomized trials that compare conservative treatment with thymectomy have started, recently. As with non-randomized trials, remission with thymectomy in MG treatment was better than conservative treatment with only medication. There are four major methods for the surgical approach: transcervical, minimally invasive, transsternal, and combined transcervical transsternal thymectomy. Transsternal approach with thymectomy is the accepted standard surgical approach for many years. In recent years, the incidence of thymectomy has been increasing with minimally invasive techniques using thoracoscopic and robotic methods. There are not any randomized, controlled studies which are comparing surgical techniques. However, when comparing non-randomized trials, it is seen that minimally invasive thymectomy approaches give similar results to more aggressive approaches. PMID:28416933

Single-Rooted Extraction Sockets: Classification and Treatment Protocol.

PubMed

El Chaar, Edgar; Oshman, Sarah; Fallah Abed, Pooria

2016-09-01

Clinicians have many treatment techniques from which to choose when extracting a failing tooth and replacing it with an implant-supported restoration and when successful management of an extraction socket during the course of tooth replacement is necessary to achieve predictable and esthetic outcomes. This article presents a straightforward, yet thorough, classification for extraction sockets of single-rooted teeth and provides guidance to clinicians in the selection of appropriate and predictable treatment. The presented classification of extraction sockets for single-rooted teeth focuses on the topography of the extraction socket, while the protocol for treatment of each socket type factors in the shape of the remaining bone, the biotype, and the location of the socket whether it be in the mandible or maxilla. This system is based on the biologic foundations of wound healing and can help guide clinicians to successful treatment outcomes.

Neural network classifications and correlation analysis of EEG and MEG activity accompanying spontaneous reversals of the Necker cube.

PubMed

Gaetz, M; Weinberg, H; Rzempoluck, E; Jantzen, K J

1998-04-01

It has recently been suggested that reentrant connections are essential in systems that process complex information [A. Damasio, H. Damasio, Cortical systems for the retrieval of concrete knowledge: the convergence zone framework, in: C. Koch, J.L. Davis (Eds.), Large Scale Neuronal Theories of the Brain, The MIT Press, Cambridge, 1995, pp. 61-74; G. Edelman, The Remembered Present, Basic Books, New York, 1989; M.I. Posner, M. Rothbart, Constructing neuronal theories of mind, in: C. Koch, J.L. Davis (Eds.), Large Scale Neuronal Theories of the Brain, The MIT Press, Cambridge, 1995, pp. 183-199; C. von der Malsburg, W. Schneider, A neuronal cocktail party processor, Biol. Cybem., 54 (1986) 29-40]. Reentry is not feedback, but parallel signalling in the time domain between spatially distributed maps, similar to a process of correlation between distributed systems. Accordingly, it was expected that during spontaneous reversals of the Necker cube, complex patterns of correlations between distributed systems would be present in the cortex. The present study included EEG (n=4) and MEG recordings (n=5). Two experimental questions were posed: (1) Can distributed cortical patterns present during perceptual reversals be classified differently using a generalised regression neural network (GRNN) compared to processing of a two-dimensional figure? (2) Does correlated cortical activity increase significantly during perception of a Necker cube reversal? One-second duration single trials of EEG and MEG data were analysed using the GRNN. Electrode/sensor pairings based on cortico-cortical connections were selected to assess correlated activity in each condition. The GRNN significantly classified single trials recorded during Necker cube reversals as different from single trials recorded during perception of a two-dimensional figure for both EEG and MEG. In addition, correlated cortical activity increased significantly in the Necker cube reversal condition for EEG and MEG compared to the perception of a non-reversing stimulus. Coherent MEG activity observed over occipital, parietal and temporal regions is believed to represent neural systems related to the perception of Necker cube reversals. Copyright 1998 Elsevier Science B.V.

Automatic detection and classification of artifacts in single-channel EEG.

PubMed

Olund, Thomas; Duun-Henriksen, Jonas; Kjaer, Troels W; Sorensen, Helge B D

2014-01-01

Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated. The detection algorithm yield an average sensitivity and specificity above 95% for both the subject-specific and generic models. The classification algorithm show a mean accuracy of 78 and 64% for the subject-specific and generic model, respectively. The classification model was additionally validated on a reference dataset with similar results.

Evaluation of space SAR as a land-cover classification

NASA Technical Reports Server (NTRS)

Brisco, B.; Ulaby, F. T.; Williams, T. H. L.

1985-01-01

The multidimensional approach to the mapping of land cover, crops, and forests is reported. Dimensionality is achieved by using data from sensors such as LANDSAT to augment Seasat and Shuttle Image Radar (SIR) data, using different image features such as tone and texture, and acquiring multidate data. Seasat, Shuttle Imaging Radar (SIR-A), and LANDSAT data are used both individually and in combination to map land cover in Oklahoma. The results indicates that radar is the best single sensor (72% accuracy) and produces the best sensor combination (97.5% accuracy) for discriminating among five land cover categories. Multidate Seasat data and a single data of LANDSAT coverage are then used in a crop classification study of western Kansas. The highest accuracy for a single channel is achieved using a Seasat scene, which produces a classification accuracy of 67%. Classification accuracy increases to approximately 75% when either a multidate Seasat combination or LANDSAT data in a multisensor combination is used. The tonal and textural elements of SIR-A data are then used both alone and in combination to classify forests into five categories.

Multi-template tensor-based morphometry: Application to analysis of Alzheimer's disease

PubMed Central

Koikkalainen, Juha; Lötjönen, Jyrki; Thurfjell, Lennart; Rueckert, Daniel; Waldemar, Gunhild; Soininen, Hilkka

2012-01-01

In this paper methods for using multiple templates in tensor-based morphometry (TBM) are presented and comparedtothe conventional single-template approach. TBM analysis requires non-rigid registrations which are often subject to registration errors. When using multiple templates and, therefore, multiple registrations, it can be assumed that the registration errors are averaged and eventually compensated. Four different methods are proposed for multi-template TBM. The methods were evaluated using magnetic resonance (MR) images of healthy controls, patients with stable or progressive mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD) from the ADNI database (N=772). The performance of TBM features in classifying images was evaluated both quantitatively and qualitatively. Classification results show that the multi-template methods are statistically significantly better than the single-template method. The overall classification accuracy was 86.0% for the classification of control and AD subjects, and 72.1%for the classification of stable and progressive MCI subjects. The statistical group-level difference maps produced using multi-template TBM were smoother, formed larger continuous regions, and had larger t-values than the maps obtained with single-template TBM. PMID:21419228

An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain computer interface

NASA Astrophysics Data System (ADS)

Wang, Tao; He, Bin

2004-03-01

The recognition of mental states during motor imagery tasks is crucial for EEG-based brain computer interface research. We have developed a new algorithm by means of frequency decomposition and weighting synthesis strategy for recognizing imagined right- and left-hand movements. A frequency range from 5 to 25 Hz was divided into 20 band bins for each trial, and the corresponding envelopes of filtered EEG signals for each trial were extracted as a measure of instantaneous power at each frequency band. The dimensionality of the feature space was reduced from 200 (corresponding to 2 s) to 3 by down-sampling of envelopes of the feature signals, and subsequently applying principal component analysis. The linear discriminate analysis algorithm was then used to classify the features, due to its generalization capability. Each frequency band bin was weighted by a function determined according to the classification accuracy during the training process. The present classification algorithm was applied to a dataset of nine human subjects, and achieved a success rate of classification of 90% in training and 77% in testing. The present promising results suggest that the present classification algorithm can be used in initiating a general-purpose mental state recognition based on motor imagery tasks.

Markers of systemic inflammation predict survival in patients with advanced renal cell cancer.

PubMed

Fox, P; Hudson, M; Brown, C; Lord, S; Gebski, V; De Souza, P; Lee, C K

2013-07-09

The host inflammatory response has a vital role in carcinogenesis and tumour progression. We examined the prognostic value of inflammatory markers (albumin, white-cell count and its components, and platelets) in pre-treated patients with advanced renal cell carcinoma (RCC). Using data from a randomised trial, multivariable proportional hazards models were generated to examine the impact of inflammatory markers and established prognostic factors (performance status, calcium, and haemoglobin) on overall survival (OS). We evaluated a new prognostic classification incorporating additional information from inflammatory markers. Of the 416 patients, 362 were included in the analysis. Elevated neutrophil counts, elevated platelet counts, and a high neutrophil-lymphocyte ratio were significant independent predictors for shorter OS in a model with established prognostic factors. The addition of inflammatory markers improves the discriminatory value of the prognostic classification as compared with established factors alone (C-statistic 0.673 vs 0.654, P=0.002 for the difference), with 25.8% (P=0.004) of patients more appropriately classified using the new classification. Markers of systemic inflammation contribute significantly to prognostic classification in addition to established factors for pre-treated patients with advanced RCC. Upon validation of these data in independent studies, stratification of patients using these markers in future clinical trials is recommended.

Ovarian hyperstimulation syndrome: review and new classification criteria for reporting in clinical trials.

PubMed

Humaidan, P; Nelson, S M; Devroey, P; Coddington, C C; Schwartz, L B; Gordon, K; Frattarelli, J L; Tarlatzis, B C; Fatemi, H M; Lutjen, P; Stegmann, B J

2016-09-01

What is an objective approach that employs measurable and reproducible physiologic changes as the basis for the classification of ovarian hyperstimulation syndrome (OHSS) in order to facilitate more accurate reporting of incidence rates within and across clinical trials? The OHSS flow diagram is an objective approach that will facilitate consistent capture, classification and reporting of OHSS within and across clinical trials. OHSS is a potentially life-threatening iatrogenic complication of the early luteal phase and/or early pregnancy after ovulation induction (OI) or ovarian stimulation (OS). The clinical picture of OHSS (the constellation of symptoms associated with each stage of the disease) is highly variable, hampering its appropriate classification in clinical trials. Although some degree of ovarian hyperstimulation is normal after stimulation, the point at which symptoms transition from those anticipated to those of a disease state is nebulous. An OHSS working group, comprised of subject matter experts and clinical researchers who have significantly contributed to the field of fertility, was convened in April and November 2014. The OHSS working group was tasked with reaching a consensus on the definition and the classification of OHSS for reporting in clinical trials. The group engaged in targeted discussion regarding the scientific background of OHSS, the criteria proposed for the definition and the rationale for universal adoption. An agreement was reached after discussion with all members. One of the following conditions must be met prior to making the diagnosis of OHSS in the context of a clinical trial: (i) the subject has undergone OS (either controlled OS or OI) AND has received a trigger shot for final oocyte maturation (e.g. hCG, GnRH agonist [GnRHa] or kisspeptin) followed by either fresh transfer or segmentation (cryopreservation of embryos) or (ii) the subject has undergone OS or OI AND has a positive pregnancy test. All study patients who develop symptoms of OHSS should undergo a thorough examination. An OHSS flow diagram was designed to be implemented for all subjects with pelvic or abdominal complaints, such as lower abdominal discomfort or distention, nausea, vomiting and diarrhea, and/or for subjects suspected of having OHSS. The diagnosis of OHSS should be based on the flow diagram. This classification system is primarily intended to address the needs of the clinical investigator undertaking clinical trials in the field of OS and may not be applicable for the use in clinical practice or with OHSS occurring under natural circumstances. The proposed OHSS classification system will enable an accurate estimate of the incidence and severity of OHSS within and across clinical trials performed in women with infertility. Financial support for the advisory group meetings was provided by Merck & Co., Inc., Kenilworth, NJ, USA. P.H. reports unrestricted research grants from MSD, Merck and Ferring, and honoraria for lectures from MSD, Merck and IBSA. S.M.N. reports that he has received fees and grant support from the following companies (in alphabetic order): Beckman Coulter, Besins, EMD Serono, Ferring Pharmaceuticals, Finox, MSD and Roche Diagnostics over the previous 5 years. P.D., C.C.C., J.L.F., H.M.F., and P.L. report no relationships that present a potential conflict of interest. B.C.T. grants and honorarium from Merck Serono; unrestricted research grants, travel grants and honorarium, and participation in a company-sponsored speaker's bureau from Merck Sharp & Dohme; grants, travel grants, honoraria and advisory board membership from IBSA; travel grants from Ferring; and advisory board membership from Ovascience. L.B.S. reports current employment with Merck & Co, Inc., Kenilworth, NJ, USA, and owns stock in the company. K.G. and B.J.S. report prior employment with Merck & Co., Inc., Kenilworth, NJ, USA, and own stock in the company. All reported that competing interests are outside the submitted work. No other relationships or activities exist that could appear to have influenced the submitted work. Not applicable. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Overview of classification systems in peripheral artery disease.

PubMed

Hardman, Rulon L; Jazaeri, Omid; Yi, J; Smith, M; Gupta, Rajan

2014-12-01

Peripheral artery disease (PAD), secondary to atherosclerotic disease, is currently the leading cause of morbidity and mortality in the western world. While PAD is common, it is estimated that the majority of patients with PAD are undiagnosed and undertreated. The challenge to the treatment of PAD is to accurately diagnose the symptoms and determine treatment for each patient. The varied presentations of peripheral vascular disease have led to numerous classification schemes throughout the literature. Consistent grading of patients leads to both objective criteria for treating patients and a baseline for clinical follow-up. Reproducible classification systems are also important in clinical trials and when comparing medical, surgical, and endovascular treatment paradigms. This article reviews the various classification systems for PAD and advantages to each system.

A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

NASA Technical Reports Server (NTRS)

Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

2012-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.

Estimating Single-Trial Responses in EEG

NASA Technical Reports Server (NTRS)

Shah, A. S.; Knuth, K. H.; Truccolo, W. A.; Mehta, A. D.; Fu, K. G.; Johnston, T. A.; Ding, M.; Bressler, S. L.; Schroeder, C. E.; Clancy, Daniel (Technical Monitor)

2002-01-01

Accurate characterization of single-trial field potential responses is critical from a number of perspectives. For example, it allows differentiation of an evoked response from ongoing EEG. We previously developed the multiple component Event Related Potential (mcERP) algorithm to improve resolution of the single-trial evoked response. The mcERP model states that multiple components, each specified by a stereotypic waveform varying in latency and amplitude from trial to trial, comprise the evoked response. Application of the mcERP algorithm to simulated data with three independent, synthetic components has shown that the model is capable of separating these components and estimating their variability. Application of the model to single trial, visual evoked potentials recorded simultaneously from all V1 laminae in an awake, fixating macaque yielded local and far-field components. Certain local components estimated by the model were distributed in both granular and supragranular laminae. This suggests a linear coupling between the responses of thalamo-recipient neuronal ensembles and subsequent responses of supragranular neuronal ensembles, as predicted by the feedforward anatomy of V1. Our results indicate that the mcERP algorithm provides a valid estimation of single-trial responses. This will enable analyses that depend on trial-to-trial variations and those that require separation of the evoked response from background EEG rhythms

A novel application of deep learning for single-lead ECG classification.

PubMed

Mathews, Sherin M; Kambhamettu, Chandra; Barner, Kenneth E

2018-06-04

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac abnormalities. In this paper, a novel approach based on deep learning methodology is proposed for the classification of single-lead electrocardiogram (ECG) signals. We demonstrate the application of the Restricted Boltzmann Machine (RBM) and deep belief networks (DBN) for ECG classification following detection of ventricular and supraventricular heartbeats using single-lead ECG. The effectiveness of this proposed algorithm is illustrated using real ECG signals from the widely-used MIT-BIH database. Simulation results demonstrate that with a suitable choice of parameters, RBM and DBN can achieve high average recognition accuracies of ventricular ectopic beats (93.63%) and of supraventricular ectopic beats (95.57%) at a low sampling rate of 114 Hz. Experimental results indicate that classifiers built into this deep learning-based framework achieved state-of-the art performance models at lower sampling rates and simple features when compared to traditional methods. Further, employing features extracted at a sampling rate of 114 Hz when combined with deep learning provided enough discriminatory power for the classification task. This performance is comparable to that of traditional methods and uses a much lower sampling rate and simpler features. Thus, our proposed deep neural network algorithm demonstrates that deep learning-based methods offer accurate ECG classification and could potentially be extended to other physiological signal classifications, such as those in arterial blood pressure (ABP), nerve conduction (EMG), and heart rate variability (HRV) studies. Copyright © 2018. Published by Elsevier Ltd.

sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

PubMed

Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

2011-10-01

In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

Semantic Classical Conditioning and Brain-Computer Interface Control: Encoding of Affirmative and Negative Thinking

PubMed Central

Ruf, Carolin A.; De Massari, Daniele; Furdea, Adrian; Matuz, Tamara; Fioravanti, Chiara; van der Heiden, Linda; Halder, Sebastian; Birbaumer, Niels

2013-01-01

The aim of the study was to investigate conditioned electroencephalography (EEG) responses to factually correct and incorrect statements in order to enable binary communication by means of a brain-computer interface (BCI). In two experiments with healthy participants true and false statements (serving as conditioned stimuli, CSs) were paired with two different tones which served as unconditioned stimuli (USs). The features of the USs were varied and tested for their effectiveness to elicit differentiable conditioned reactions (CRs). After acquisition of the CRs, these CRs to true and false statements were classified offline using a radial basis function kernel support vector machine. A mean single-trial classification accuracy of 50.5% was achieved for differentiating conditioned “yes” versus “no” thinking and mean accuracies of 65.4% for classification of “yes” and 68.8% for “no” thinking (both relative to baseline) were found using the best US. Analysis of the area under the curve of the conditioned EEG responses revealed significant differences between conditioned “yes” and “no” answers. Even though improvements are necessary, these first results indicate that the semantic conditioning paradigm could be a useful basis for further research regarding BCI communication in patients in the complete locked-in state. PMID:23471568

Sequential Probability Ratio Testing with Power Projective Base Method Improves Decision-Making for BCI

PubMed Central

Liu, Rong

2017-01-01

Obtaining a fast and reliable decision is an important issue in brain-computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this study, the EEG signals were firstly analyzed with a power projective base method. Then we were applied a decision-making model, the sequential probability ratio testing (SPRT), for single-trial classification of motor imagery movement events. The unique strength of this proposed classification method lies in its accumulative process, which increases the discriminative power as more and more evidence is observed over time. The properties of the method were illustrated on thirteen subjects' recordings from three datasets. Results showed that our proposed power projective method outperformed two benchmark methods for every subject. Moreover, with sequential classifier, the accuracies across subjects were significantly higher than that with nonsequential ones. The average maximum accuracy of the SPRT method was 84.1%, as compared with 82.3% accuracy for the sequential Bayesian (SB) method. The proposed SPRT method provides an explicit relationship between stopping time, thresholds, and error, which is important for balancing the time-accuracy trade-off. These results suggest SPRT would be useful in speeding up decision-making while trading off errors in BCI. PMID:29348781

Adaptive video-based vehicle classification technique for monitoring traffic : [executive summary].

DOT National Transportation Integrated Search

2015-08-01

Federal Highway Administration (FHWA) recommends axle-based classification standards to map : passenger vehicles, single unit trucks, and multi-unit trucks, at Automatic Traffic Recorder (ATR) stations : statewide. Many state Departments of Transport...

Exploration of Force Myography and surface Electromyography in hand gesture classification.

PubMed

Jiang, Xianta; Merhi, Lukas-Karim; Xiao, Zhen Gang; Menon, Carlo

2017-03-01

Whereas pressure sensors increasingly have received attention as a non-invasive interface for hand gesture recognition, their performance has not been comprehensively evaluated. This work examined the performance of hand gesture classification using Force Myography (FMG) and surface Electromyography (sEMG) technologies by performing 3 sets of 48 hand gestures using a prototyped FMG band and an array of commercial sEMG sensors worn both on the wrist and forearm simultaneously. The results show that the FMG band achieved classification accuracies as good as the high quality, commercially available, sEMG system on both wrist and forearm positions; specifically, by only using 8 Force Sensitive Resisters (FSRs), the FMG band achieved accuracies of 91.2% and 83.5% in classifying the 48 hand gestures in cross-validation and cross-trial evaluations, which were higher than those of sEMG (84.6% and 79.1%). By using all 16 FSRs on the band, our device achieved high accuracies of 96.7% and 89.4% in cross-validation and cross-trial evaluations. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Multiple confidence estimates as indices of eyewitness memory.

PubMed

Sauer, James D; Brewer, Neil; Weber, Nathan

2008-08-01

Eyewitness identification decisions are vulnerable to various influences on witnesses' decision criteria that contribute to false identifications of innocent suspects and failures to choose perpetrators. An alternative procedure using confidence estimates to assess the degree of match between novel and previously viewed faces was investigated. Classification algorithms were applied to participants' confidence data to determine when a confidence value or pattern of confidence values indicated a positive response. Experiment 1 compared confidence group classification accuracy with a binary decision control group's accuracy on a standard old-new face recognition task and found superior accuracy for the confidence group for target-absent trials but not for target-present trials. Experiment 2 used a face mini-lineup task and found reduced target-present accuracy offset by large gains in target-absent accuracy. Using a standard lineup paradigm, Experiments 3 and 4 also found improved classification accuracy for target-absent lineups and, with a more sophisticated algorithm, for target-present lineups. This demonstrates the accessibility of evidence for recognition memory decisions and points to a more sensitive index of memory quality than is afforded by binary decisions.

Assessment of differences between repeated pulse wave velocity measurements in terms of 'bias' in the extrapolated cardiovascular risk and the classification of aortic stiffness: is a single PWV measurement enough?

PubMed

Papaioannou, T G; Protogerou, A D; Nasothimiou, E G; Tzamouranis, D; Skliros, N; Achimastos, A; Papadogiannis, D; Stefanadis, C I

2012-10-01

Currently, there is no recommendation regarding the minimum number of pulse wave velocity (PWV) measurements to optimize individual's cardiovascular risk (CVR) stratification. The aim of this study was to examine differences between three single consecutive and averaged PWV measurements in terms of the extrapolated CVR and the classification of aortic stiffness as normal. In 60 subjects who referred for CVR assessment, three repeated measurements of blood pressure (BP), heart rate and PWV were performed. The reproducibility was evaluated by the intraclass correlation coefficient (ICC) and mean±s.d. of differences. The absolute differences between single and averaged PWV measurements were classified as: ≤0.25, 0.26-0.49, 0.50-0.99 and ≥1 m s(-1). A difference ≥0.5 m s(-1) (corresponding to 7.5% change in CVR, meta-analysis data from >12 000 subjects) was considered as clinically meaningful; PWV values (single or averaged) were classified as normal according to respective age-corrected normal values (European Network data). Kappa statistic was used to evaluate the agreement between classifications. PWV for the first, second and third measurement was 7.0±1.9, 6.9±1.9, 6.9±2.0 m s(-1), respectively (P=0.319); BP and heart rate did not vary significantly. A good reproducibility between single measurements was observed (ICC>0.94, s.d. ranged between 0.43 and 0.64 m s(-1)). A high percent with difference ≥0.5 m s(-1) was observed between: any pair of the three single PWV measurements (26.6-38.3%); the first or second single measurement and the average of the first and second (18.3%); any single measurement and the average of three measurements (10-20%). In only up to 5% a difference ≥0.5 m s(-1) was observed between the average of three and the average of any two PWV measurements. There was no significant agreement regarding PWV classification as normal between: the first or second measurement and the averaged PWV values. There was significant agreement in classification made by the average of the first two and the average of three PWV measurements (κ=0.85, P<0.001). Even when high reproducibility in PWV measurement is succeeded single measurements provide quite variable results in terms of the extrapolated CVR and the classification of aortic stiffness as normal. The average of two PWV measurements provides similar results with the average of three.

Toward an endovascular internal carotid artery classification system.

PubMed

Shapiro, M; Becske, T; Riina, H A; Raz, E; Zumofen, D; Jafar, J J; Huang, P P; Nelson, P K

2014-02-01

Does the world need another ICA classification scheme? We believe so. The purpose of proposed angiography-driven classification is to optimize description of the carotid artery from the endovascular perspective. A review of existing, predominantly surgically-driven classifications is performed, and a new scheme, based on the study of NYU aneurysm angiographic and cross-sectional databases is proposed. Seven segments - cervical, petrous, cavernous, paraophthlamic, posterior communicating, choroidal, and terminus - are named. This nomenclature recognizes intrinsic uncertainty in precise angiographic and cross-sectional localization of aneurysms adjacent to the dural rings, regarding all lesions distal to the cavernous segment as potentially intradural. Rather than subdividing various transitional, ophthalmic, and hypophyseal aneurysm subtypes, as necessitated by their varied surgical approaches and risks, the proposed classification emphasizes their common endovascular treatment features, while recognizing that many complex, trans-segmental, and fusiform aneurysms not readily classifiable into presently available, saccular aneurysm-driven schemes, are being increasingly addressed by endovascular means. We believe this classification may find utility in standardizing nomenclature for outcome tracking, treatment trials and physician communication.

A Hybrid Sensing Approach for Pure and Adulterated Honey Classification

PubMed Central

Subari, Norazian; Saleh, Junita Mohamad; Shakaff, Ali Yeon Md; Zakaria, Ammar

2012-01-01

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data. PMID:23202033

Metric learning for automatic sleep stage classification.

PubMed

Phan, Huy; Do, Quan; Do, The-Luan; Vu, Duc-Lung

2013-01-01

We introduce in this paper a metric learning approach for automatic sleep stage classification based on single-channel EEG data. We show that learning a global metric from training data instead of using the default Euclidean metric, the k-nearest neighbor classification rule outperforms state-of-the-art methods on Sleep-EDF dataset with various classification settings. The overall accuracy for Awake/Sleep and 4-class classification setting are 98.32% and 94.49% respectively. Furthermore, the superior accuracy is achieved by performing classification on a low-dimensional feature space derived from time and frequency domains and without the need for artifact removal as a preprocessing step.

Field evaluation of a random forest activity classifier for wrist-worn accelerometer data.

PubMed

Pavey, Toby G; Gilson, Nicholas D; Gomersall, Sjaan R; Clark, Bronwyn; Trost, Stewart G

2017-01-01

Wrist-worn accelerometers are convenient to wear and associated with greater wear-time compliance. Previous work has generally relied on choreographed activity trials to train and test classification models. However, validity in free-living contexts is starting to emerge. Study aims were: (1) train and test a random forest activity classifier for wrist accelerometer data; and (2) determine if models trained on laboratory data perform well under free-living conditions. Twenty-one participants (mean age=27.6±6.2) completed seven lab-based activity trials and a 24h free-living trial (N=16). Participants wore a GENEActiv monitor on the non-dominant wrist. Classification models recognising four activity classes (sedentary, stationary+, walking, and running) were trained using time and frequency domain features extracted from 10-s non-overlapping windows. Model performance was evaluated using leave-one-out-cross-validation. Models were implemented using the randomForest package within R. Classifier accuracy during the 24h free living trial was evaluated by calculating agreement with concurrently worn activPAL monitors. Overall classification accuracy for the random forest algorithm was 92.7%. Recognition accuracy for sedentary, stationary+, walking, and running was 80.1%, 95.7%, 91.7%, and 93.7%, respectively for the laboratory protocol. Agreement with the activPAL data (stepping vs. non-stepping) during the 24h free-living trial was excellent and, on average, exceeded 90%. The ICC for stepping time was 0.92 (95% CI=0.75-0.97). However, sensitivity and positive predictive values were modest. Mean bias was 10.3min/d (95% LOA=-46.0 to 25.4min/d). The random forest classifier for wrist accelerometer data yielded accurate group-level predictions under controlled conditions, but was less accurate at identifying stepping verse non-stepping behaviour in free living conditions Future studies should conduct more rigorous field-based evaluations using observation as a criterion measure. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Warp-averaging event-related potentials.

PubMed

Wang, K; Begleiter, H; Porjesz, B

2001-10-01

To align the repeated single trials of the event-related potential (ERP) in order to get an improved estimate of the ERP. A new implementation of the dynamic time warping is applied to compute a warp-average of the single trials. The trilinear modeling method is applied to filter the single trials prior to alignment. Alignment is based on normalized signals and their estimated derivatives. These features reduce the misalignment due to aligning the random alpha waves, explaining amplitude differences in latency differences, or the seemingly small amplitudes of some components. Simulations and applications to visually evoked potentials show significant improvement over some commonly used methods. The new implementation of the dynamic time warping can be used to align the major components (P1, N1, P2, N2, P3) of the repeated single trials. The average of the aligned single trials is an improved estimate of the ERP. This could lead to more accurate results in subsequent analysis.

Deconvolution single shot multibox detector for supermarket commodity detection and classification

NASA Astrophysics Data System (ADS)

Li, Dejian; Li, Jian; Nie, Binling; Sun, Shouqian

2017-07-01

This paper proposes an image detection model to detect and classify supermarkets shelves' commodity. Based on the principle of the features directly affects the accuracy of the final classification, feature maps are performed to combine high level features with bottom level features. Then set some fixed anchors on those feature maps, finally the label and the position of commodity is generated by doing a box regression and classification. In this work, we proposed a model named Deconvolutiuon Single Shot MultiBox Detector, we evaluated the model using 300 images photographed from real supermarket shelves. Followed the same protocol in other recent methods, the results showed that our model outperformed other baseline methods.

Layered classification techniques for remote sensing applications

NASA Technical Reports Server (NTRS)

Swain, P. H.; Wu, C. L.; Landgrebe, D. A.; Hauska, H.

1975-01-01

The single-stage method of pattern classification utilizes all available features in a single test which assigns the unknown to a category according to a specific decision strategy (such as the maximum likelihood strategy). The layered classifier classifies the unknown through a sequence of tests, each of which may be dependent on the outcome of previous tests. Although the layered classifier was originally investigated as a means of improving classification accuracy and efficiency, it was found that in the context of remote sensing data analysis, other advantages also accrue due to many of the special characteristics of both the data and the applications pursued. The layered classifier method and several of the diverse applications of this approach are discussed.

Multimodal Intervention Trial for Cognitive Deficits in Neurofibromatosis Type 1: Efficacy of Computerized Cognitive Training and Stimulant Medication

DTIC Science & Technology

2017-10-01

AWARD NUMBER: W81XWH-15-1-0508 TITLE: Multimodal Intervention Trial for Cognitive Deficits in Neurofibromatosis Type 1: Efficacy of...Computerized Cognitive Training and Stimulant Medication PRINCIPAL INVESTIGATOR: Maria T. Acosta, M.D. CONTRACTING ORGANIZATION: Children’s National Health...database. 15. SUBJECT TERMS Neurofibromatosis, cognition , pediatric, computerized training programs, working memory 16. SECURITY CLASSIFICATION OF: 17

Application of Sensor Fusion to Improve Uav Image Classification

NASA Astrophysics Data System (ADS)

Jabari, S.; Fathollahi, F.; Zhang, Y.

2017-08-01

Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan) camera along with either a colour camera or a four-band multi-spectral (MS) camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC). We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.

Feature Selection for Ridge Regression with Provable Guarantees.

PubMed

Paul, Saurabh; Drineas, Petros

2016-04-01

We introduce single-set spectral sparsification as a deterministic sampling-based feature selection technique for regularized least-squares classification, which is the classification analog to ridge regression. The method is unsupervised and gives worst-case guarantees of the generalization power of the classification function after feature selection with respect to the classification function obtained using all features. We also introduce leverage-score sampling as an unsupervised randomized feature selection method for ridge regression. We provide risk bounds for both single-set spectral sparsification and leverage-score sampling on ridge regression in the fixed design setting and show that the risk in the sampled space is comparable to the risk in the full-feature space. We perform experiments on synthetic and real-world data sets; a subset of TechTC-300 data sets, to support our theory. Experimental results indicate that the proposed methods perform better than the existing feature selection methods.

Distinguishing rational from irrational applications of pharmacogenetic synergies from the bench to clinical trials.

PubMed

Hucl, Tomas; Gallmeier, Eike; Kern, Scott E

2007-06-01

Single therapeutic agents very often fail in unselected patients. It is therefore commonplace to combine an agent specifically with a selected patient subgroup or with another agent. To support such efforts, it is useful to clarify the distinctions between the terms and the mathematical models used in analyzing combinations. To incorporate molecular disease classifications, the familiar concept of the therapeutic window is modified to define a pharmacogenetic window, which is an unambiguous numerical measure of the magnitude of interaction produced by a combination, and to define a test of pharmacogenetic synergy. In contrast, certain common comparative methods, such as vertical windows (comparing effects at a given dose) and animal models of mutational targets may be dominated by undesirable features. Although this discussion is oriented towards cancer therapy, an extension of these concepts to other comparative biologic assays is feasible and advisable.

Classifying the auditory P300 using mobile EEG recordings without calibration phase.

PubMed

Zink, R; Hunyádi, B; Van Huffel, S; De Vos, M

2015-08-01

One of the major drawbacks in mobile EEG Brain Computer Interfaces (BCI) is the need for subject specific training data to train a classifier. By removing the need for supervised classification and calibration phase, new users could start immediate interaction with a BCI. We propose a solution to exploit the structural difference by means of canonical polyadic decomposition (CPD) for three-class auditory oddball data without the need for subject-specific information. We achieve this by adding average event-related-potential (ERP) templates to the CPD model. This constitutes a novel similarity measure between single-trial pairs and known-templates, which results in a fast and interpretable classifier. These results have similar accuracy to those of the supervised and cross-validated stepwise LDA approach but without the need for having subject-dependent data. Therefore the described CPD method has a significant practical advantage over the traditional and widely used approach.

Real-time recognition of feedback error-related potentials during a time-estimation task.

PubMed

Lopez-Larraz, Eduardo; Iturrate, Iñaki; Montesano, Luis; Minguez, Javier

2010-01-01

Feedback error-related potentials are a promising brain process in the field of rehabilitation since they are related to human learning. Due to the fact that many therapeutic strategies rely on the presentation of feedback stimuli, potentials generated by these stimuli could be used to ameliorate the patient's progress. In this paper we propose a method that can identify, in real-time, feedback evoked potentials in a time-estimation task. We have tested our system with five participants in two different days with a separation of three weeks between them, achieving a mean single-trial detection performance of 71.62% for real-time recognition, and 78.08% in offline classification. Additionally, an analysis of the stability of the signal between the two days is performed, suggesting that the feedback responses are stable enough to be used without the needing of training again the user.

Reverse inference of memory retrieval processes underlying metacognitive monitoring of learning using multivariate pattern analysis.

PubMed

Stiers, Peter; Falbo, Luciana; Goulas, Alexandros; van Gog, Tamara; de Bruin, Anique

2016-05-15

Monitoring of learning is only accurate at some time after learning. It is thought that immediate monitoring is based on working memory, whereas later monitoring requires re-activation of stored items, yielding accurate judgements. Such interpretations are difficult to test because they require reverse inference, which presupposes specificity of brain activity for the hidden cognitive processes. We investigated whether multivariate pattern classification can provide this specificity. We used a word recall task to create single trial examples of immediate and long term retrieval and trained a learning algorithm to discriminate them. Next, participants performed a similar task involving monitoring instead of recall. The recall-trained classifier recognized the retrieval patterns underlying immediate and long term monitoring and classified delayed monitoring examples as long-term retrieval. This result demonstrates the feasibility of decoding cognitive processes, instead of their content. Copyright © 2016 Elsevier Inc. All rights reserved.

A systematic review and development of a classification framework for factors associated with missing patient-reported outcome data.

PubMed

Palmer, Michael J; Mercieca-Bebber, Rebecca; King, Madeleine; Calvert, Melanie; Richardson, Harriet; Brundage, Michael

2018-02-01

Missing patient-reported outcome data can lead to biased results, to loss of power to detect between-treatment differences, and to research waste. Awareness of factors may help researchers reduce missing patient-reported outcome data through study design and trial processes. The aim was to construct a Classification Framework of factors associated with missing patient-reported outcome data in the context of comparative studies. The first step in this process was informed by a systematic review. Two databases (MEDLINE and CINAHL) were searched from inception to March 2015 for English articles. Inclusion criteria were (a) relevant to patient-reported outcomes, (b) discussed missing data or compliance in prospective medical studies, and (c) examined predictors or causes of missing data, including reasons identified in actual trial datasets and reported on cover sheets. Two reviewers independently screened titles and abstracts. Discrepancies were discussed with the research team prior to finalizing the list of eligible papers. In completing the systematic review, four particular challenges to synthesizing the extracted information were identified. To address these challenges, operational principles were established by consensus to guide the development of the Classification Framework. A total of 6027 records were screened. In all, 100 papers were eligible and included in the review. Of these, 57% focused on cancer, 23% did not specify disease, and 20% reported for patients with a variety of non-cancer conditions. In total, 40% of the papers offered a descriptive analysis of possible factors associated with missing data, but some papers used other methods. In total, 663 excerpts of text (units), each describing a factor associated with missing patient-reported outcome data, were extracted verbatim. Redundant units were identified and sequestered. Similar units were grouped, and an iterative process of consensus among the investigators was used to reduce these units to a list of factors that met the guiding principles. The list was organized on a framework, using an iterative consensus-based process. The resultant Classification Framework is a summary of the factors associated with missing patient-reported outcome data described in the literature. It consists of 5 components (instrument, participant, centre, staff, and study) and 46 categories, each with one or more sub-categories or examples. A systematic review of the literature revealed 46 unique categories of factors associated with missing patient-reported outcome data, organized into 5 main component groups. The Classification Framework may assist researchers to improve the design of new randomized clinical trials and to implement procedures to reduce missing patient-reported outcome data. Further research using the Classification Framework to inform quantitative analyses of missing patient-reported outcome data in existing clinical trials and to inform qualitative inquiry of research staff is planned.

Clinical Variant Classification: A Comparison of Public Databases and a Commercial Testing Laboratory.

PubMed

Gradishar, William; Johnson, KariAnne; Brown, Krystal; Mundt, Erin; Manley, Susan

2017-07-01

There is a growing move to consult public databases following receipt of a genetic test result from a clinical laboratory; however, the well-documented limitations of these databases call into question how often clinicians will encounter discordant variant classifications that may introduce uncertainty into patient management. Here, we evaluate discordance in BRCA1 and BRCA2 variant classifications between a single commercial testing laboratory and a public database commonly consulted in clinical practice. BRCA1 and BRCA2 variant classifications were obtained from ClinVar and compared with the classifications from a reference laboratory. Full concordance and discordance were determined for variants whose ClinVar entries were of the same pathogenicity (pathogenic, benign, or uncertain). Variants with conflicting ClinVar classifications were considered partially concordant if ≥1 of the listed classifications agreed with the reference laboratory classification. Four thousand two hundred and fifty unique BRCA1 and BRCA2 variants were available for analysis. Overall, 73.2% of classifications were fully concordant and 12.3% were partially concordant. The remaining 14.5% of variants had discordant classifications, most of which had a definitive classification (pathogenic or benign) from the reference laboratory compared with an uncertain classification in ClinVar (14.0%). Here, we show that discrepant classifications between a public database and single reference laboratory potentially account for 26.7% of variants in BRCA1 and BRCA2 . The time and expertise required of clinicians to research these discordant classifications call into question the practicality of checking all test results against a database and suggest that discordant classifications should be interpreted with these limitations in mind. With the increasing use of clinical genetic testing for hereditary cancer risk, accurate variant classification is vital to ensuring appropriate medical management. There is a growing move to consult public databases following receipt of a genetic test result from a clinical laboratory; however, we show that up to 26.7% of variants in BRCA1 and BRCA2 have discordant classifications between ClinVar and a reference laboratory. The findings presented in this paper serve as a note of caution regarding the utility of database consultation. © AlphaMed Press 2017.

Nutritional supplements for people being treated for active tuberculosis: A technical summary.

PubMed

Grobler, L; Durao, S; Van der Merwe, S M; Wessels, J; Naude, C E

2017-12-13

Tuberculosis and nutrition are intrinsically linked in a complex relationship. Altered metabolism and loss of appetite associated with tuberculosis may result in undernutrition, which in turn may worsen the disease or delay recovery. We highlight an updated Cochrane review assessing the effects of oral nutritional supplements in people with active tuberculosis who are receiving antituberculosis drug therapy. The review authors conducted a comprehensive search (February 2016) for all randomised controlled trials comparing any oral nutritional supplement, given for at least 4 weeks, with no nutritional intervention, placebo or dietary advice only in people receiving antituberculosis treatment. Of the 35 trials (N=8 283 participants) included, seven assessed the provision of free food or high-energy supplements, six assessed multi-micronutrient supplementation, and 21 assessed single- or dual-micronutrient supplementation. There is currently insufficient evidence to indicate whether routinely providing free food or high-energy supplements improves antituberculosis treatment outcomes (i.e. reduced death and increased cure rates at 6 and 12 months), but it probably improves weight gain in some settings. Plasma levels of zinc, vitamin D, vitamin E and selenium probably improve with supplementation, but currently no reliable evidence demonstrates that routine supplementation with multi-, single or dual micronutrients above the recommended daily intake has clinical benefits (i.e. reduced death, increased cure rate at 6 and 12 months, improved nutritional status) in patients receiving antituberculosis treatment. In South Africa, most provinces implement a supplementation protocol based on nutritional assessment and classification of individuals rather than on disease diagnosis or treatment status.

21 CFR 886.1400 - Maddox lens.

Code of Federal Regulations, 2014 CFR

2014-04-01

... intended to be handheld or placed in a trial frame to evaluate eye muscle dysfunction. (b) Classification... the current good manufacturing practice requirements of the quality system regulation in part 820 of...

21 CFR 886.1400 - Maddox lens.

Code of Federal Regulations, 2010 CFR

2010-04-01

... intended to be handheld or placed in a trial frame to evaluate eye muscle dysfunction. (b) Classification... the current good manufacturing practice requirements of the quality system regulation in part 820 of...

21 CFR 886.1400 - Maddox lens.

Code of Federal Regulations, 2011 CFR

2011-04-01

... intended to be handheld or placed in a trial frame to evaluate eye muscle dysfunction. (b) Classification... the current good manufacturing practice requirements of the quality system regulation in part 820 of...

21 CFR 886.1400 - Maddox lens.

Code of Federal Regulations, 2013 CFR

2013-04-01

... intended to be handheld or placed in a trial frame to evaluate eye muscle dysfunction. (b) Classification... the current good manufacturing practice requirements of the quality system regulation in part 820 of...

21 CFR 886.1400 - Maddox lens.

Code of Federal Regulations, 2012 CFR

2012-04-01

... intended to be handheld or placed in a trial frame to evaluate eye muscle dysfunction. (b) Classification... the current good manufacturing practice requirements of the quality system regulation in part 820 of...

Comparing performance of mothers using simplified mid-upper arm circumference (MUAC) classification devices with an improved MUAC insertion tape in Isiolo County, Kenya.

PubMed

Grant, Angeline; Njiru, James; Okoth, Edgar; Awino, Imelda; Briend, André; Murage, Samuel; Abdirahman, Saida; Myatt, Mark

2018-01-01

A novel approach for improving community case-detection of acute malnutrition involves mothers/caregivers screening their children for acute malnutrition using a mid-upper arm circumference (MUAC) insertion tape. The objective of this study was to test three simple MUAC classification devices to determine whether they improved the sensitivity of mothers/caregivers at detecting acute malnutrition. Prospective, non-randomised, partially-blinded, clinical diagnostic trial describing and comparing the performance of three "Click-MUAC" devices and a MUAC insertion tape. The study took place in twenty-one health facilities providing integrated management of acute malnutrition (IMAM) services in Isiolo County, Kenya. Mothers/caregivers classified their child ( n =1040), aged 6-59 months, using the "Click-MUAC" devices and a MUAC insertion tape. These classifications were compared to a "gold standard" classification (the mean of three measurements taken by a research assistant using the MUAC insertion tape). The sensitivity of mother/caregiver classifications was high for all devices (>93% for severe acute malnutrition (SAM), defined by MUAC < 115 mm, and > 90% for global acute malnutrition (GAM), defined by MUAC < 125 mm). Mother/caregiver sensitivity for SAM and GAM classification was higher using the MUAC insertion tape (100% sensitivity for SAM and 99% sensitivity for GAM) than using "Click-MUAC" devices. Younden's J for SAM classification, and sensitivity for GAM classification, were significantly higher for the MUAC insertion tape (99% and 99% respectively). Specificity was high for all devices (>96%) with no significant difference between the "Click-MUAC" devices and the MUAC insertion tape. The results of this study indicate that, although the "Click-MUAC" devices performed well, the MUAC insertion tape performed best. The results for sensitivity are higher than found in previous studies. The high sensitivity for both SAM and GAM classification by mothers/caregivers with the MUAC insertion tape could be due to the use of an improved MUAC tape design which has a number of new design features. The one-on-one demonstration provided to mothers/caregivers on the use of the devices may also have helped improve sensitivity. The results of this study provide evidence that mothers/caregivers can perform sensitive and specific classifications of their child's nutritional status using MUAC. Clinical trials registration number: NCT02833740.

Reliability of Single-Leg Balance and Landing Tests in Rugby Union; Prospect of Using Postural Control to Monitor Fatigue

PubMed Central

Troester, Jordan C.; Jasmin, Jason G.; Duffield, Rob

2018-01-01

The present study examined the inter-trial (within test) and inter-test (between test) reliability of single-leg balance and single-leg landing measures performed on a force plate in professional rugby union players using commercially available software (SpartaMARS, Menlo Park, USA). Twenty-four players undertook test – re-test measures on two occasions (7 days apart) on the first training day of two respective pre-season weeks following 48h rest and similar weekly training loads. Two 20s single-leg balance trials were performed on a force plate with eyes closed. Three single-leg landing trials were performed by jumping off two feet and landing on one foot in the middle of a force plate 1m from the starting position. Single-leg balance results demonstrated acceptable inter-trial reliability (ICC = 0.60-0.81, CV = 11-13%) for sway velocity, anterior-posterior sway velocity, and mediolateral sway velocity variables. Acceptable inter-test reliability (ICC = 0.61-0.89, CV = 7-13%) was evident for all variables except mediolateral sway velocity on the dominant leg (ICC = 0.41, CV = 15%). Single-leg landing results only demonstrated acceptable inter-trial reliability for force based measures of relative peak landing force and impulse (ICC = 0.54-0.72, CV = 9-15%). Inter-test results indicate improved reliability through the averaging of three trials with force based measures again demonstrating acceptable reliability (ICC = 0.58-0.71, CV = 7-14%). Of the variables investigated here, total sway velocity and relative landing impulse are the most reliable measures of single-leg balance and landing performance, respectively. These measures should be considered for monitoring potential changes in postural control in professional rugby union. Key points Single-leg balance demonstrated acceptable inter-trial and inter-test reliability. Single-leg landing demonstrated good inter-trial and inter-test reliability for measures of relative peak landing force and relative impulse, but not time to stabilization. Of the variables investigated, sway velocity and relative landing impulse are the most reliable measures of single-leg balance and landing respectively, and should considered for monitoring changes in postural control. PMID:29769817

Use of feature extraction techniques for the texture and context information in ERTS imagery: Spectral and textural processing of ERTS imagery. [classification of Kansas land use

NASA Technical Reports Server (NTRS)

Haralick, R. H. (Principal Investigator); Bosley, R. J.

1974-01-01

The author has identified the following significant results. A procedure was developed to extract cross-band textural features from ERTS MSS imagery. Evolving from a single image texture extraction procedure which uses spatial dependence matrices to measure relative co-occurrence of nearest neighbor grey tones, the cross-band texture procedure uses the distribution of neighboring grey tone N-tuple differences to measure the spatial interrelationships, or co-occurrences, of the grey tone N-tuples present in a texture pattern. In both procedures, texture is characterized in such a way as to be invariant under linear grey tone transformations. However, the cross-band procedure complements the single image procedure by extracting texture information and spectral information contained in ERTS multi-images. Classification experiments show that when used alone, without spectral processing, the cross-band texture procedure extracts more information than the single image texture analysis. Results show an improvement in average correct classification from 86.2% to 88.8% for ERTS image no. 1021-16333 with the cross-band texture procedure. However, when used together with spectral features, the single image texture plus spectral features perform better than the cross-band texture plus spectral features, with an average correct classification of 93.8% and 91.6%, respectively.

Classification of hydrocephalus: critical analysis of classification categories and advantages of "Multi-categorical Hydrocephalus Classification" (Mc HC).

PubMed

Oi, Shizuo

2011-10-01

Hydrocephalus is a complex pathophysiology with disturbed cerebrospinal fluid (CSF) circulation. There are numerous numbers of classification trials published focusing on various criteria, such as associated anomalies/underlying lesions, CSF circulation/intracranial pressure patterns, clinical features, and other categories. However, no definitive classification exists comprehensively to cover the variety of these aspects. The new classification of hydrocephalus, "Multi-categorical Hydrocephalus Classification" (Mc HC), was invented and developed to cover the entire aspects of hydrocephalus with all considerable classification items and categories. Ten categories include "Mc HC" category I: onset (age, phase), II: cause, III: underlying lesion, IV: symptomatology, V: pathophysiology 1-CSF circulation, VI: pathophysiology 2-ICP dynamics, VII: chronology, VII: post-shunt, VIII: post-endoscopic third ventriculostomy, and X: others. From a 100-year search of publication related to the classification of hydrocephalus, 14 representative publications were reviewed and divided into the 10 categories. The Baumkuchen classification graph made from the round o'clock classification demonstrated the historical tendency of deviation to the categories in pathophysiology, either CSF or ICP dynamics. In the preliminary clinical application, it was concluded that "Mc HC" is extremely effective in expressing the individual state with various categories in the past and present condition or among the compatible cases of hydrocephalus along with the possible chronological change in the future.

An online hybrid BCI system based on SSVEP and EMG

NASA Astrophysics Data System (ADS)

Lin, Ke; Cinetto, Andrea; Wang, Yijun; Chen, Xiaogang; Gao, Shangkai; Gao, Xiaorong

2016-04-01

Objective. A hybrid brain-computer interface (BCI) is a device combined with at least one other communication system that takes advantage of both parts to build a link between humans and machines. To increase the number of targets and the information transfer rate (ITR), electromyogram (EMG) and steady-state visual evoked potential (SSVEP) were combined to implement a hybrid BCI. A multi-choice selection method based on EMG was developed to enhance the system performance. Approach. A 60-target hybrid BCI speller was built in this study. A single trial was divided into two stages: a stimulation stage and an output selection stage. In the stimulation stage, SSVEP and EMG were used together. Every stimulus flickered at its given frequency to elicit SSVEP. All of the stimuli were divided equally into four sections with the same frequency set. The frequency of each stimulus in a section was different. SSVEPs were used to discriminate targets in the same section. Different sections were classified using EMG signals from the forearm. Subjects were asked to make different number of fists according to the target section. Canonical Correlation Analysis (CCA) and mean filtering was used to classify SSVEP and EMG separately. In the output selection stage, the top two optimal choices were given. The first choice with the highest probability of an accurate classification was the default output of the system. Subjects were required to make a fist to select the second choice only if the second choice was correct. Main results. The online results obtained from ten subjects showed that the mean accurate classification rate and ITR were 81.0% and 83.6 bits min-1 respectively only using the first choice selection. The ITR of the hybrid system was significantly higher than the ITR of any of the two single modalities (EMG: 30.7 bits min-1, SSVEP: 60.2 bits min-1). After the addition of the second choice selection and the correction task, the accurate classification rate and ITR was enhanced to 85.8% and 90.9 bit min-1. Significance. These results suggest that the hybrid system proposed here is suitable for practical use.

Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram.

PubMed

Urtnasan, Erdenebayar; Park, Jong-Uk; Lee, Kyoung-Joung

2018-05-24

In this paper, we propose a convolutional neural network (CNN)-based deep learning architecture for multiclass classification of obstructive sleep apnea and hypopnea (OSAH) using single-lead electrocardiogram (ECG) recordings. OSAH is the most common sleep-related breathing disorder. Many subjects who suffer from OSAH remain undiagnosed; thus, early detection of OSAH is important. In this study, automatic classification of three classes-normal, hypopnea, and apnea-based on a CNN is performed. An optimal six-layer CNN model is trained on a training dataset (45,096 events) and evaluated on a test dataset (11,274 events). The training set (69 subjects) and test set (17 subjects) were collected from 86 subjects with length of approximately 6 h and segmented into 10 s durations. The proposed CNN model reaches a mean -score of 93.0 for the training dataset and 87.0 for the test dataset. Thus, proposed deep learning architecture achieved a high performance for multiclass classification of OSAH using single-lead ECG recordings. The proposed method can be employed in screening of patients suspected of having OSAH. © 2018 Institute of Physics and Engineering in Medicine.

Systems Biology Methods for Alzheimer's Disease Research Toward Molecular Signatures, Subtypes, and Stages and Precision Medicine: Application in Cohort Studies and Trials.

PubMed

Castrillo, Juan I; Lista, Simone; Hampel, Harald; Ritchie, Craig W

2018-01-01

Alzheimer's disease (AD) is a complex multifactorial disease, involving a combination of genomic, interactome, and environmental factors, with essential participation of (a) intrinsic genomic susceptibility and (b) a constant dynamic interplay between impaired pathways and central homeostatic networks of nerve cells. The proper investigation of the complexity of AD requires new holistic systems-level approaches, at both the experimental and computational level. Systems biology methods offer the potential to unveil new fundamental insights, basic mechanisms, and networks and their interplay. These may lead to the characterization of mechanism-based molecular signatures, and AD hallmarks at the earliest molecular and cellular levels (and beyond), for characterization of AD subtypes and stages, toward targeted interventions according to the evolving precision medicine paradigm. In this work, an update on advanced systems biology methods and strategies for holistic studies of multifactorial diseases-particularly AD-is presented. This includes next-generation genomics, neuroimaging and multi-omics methods, experimental and computational approaches, relevant disease models, and latest genome editing and single-cell technologies. Their progressive incorporation into basic research, cohort studies, and trials is beginning to provide novel insights into AD essential mechanisms, molecular signatures, and markers toward mechanism-based classification and staging, and tailored interventions. Selected methods which can be applied in cohort studies and trials, with the European Prevention of Alzheimer's Dementia (EPAD) project as a reference example, are presented and discussed.

Single-agent Taxane Versus Taxane-containing Combination Chemotherapy as Salvage Therapy for Advanced Urothelial Carcinoma.

PubMed

Sonpavde, Guru; Pond, Gregory R; Choueiri, Toni K; Mullane, Stephanie; Niegisch, Guenter; Albers, Peter; Necchi, Andrea; Di Lorenzo, Giuseppe; Buonerba, Carlo; Rozzi, Antonio; Matsumoto, Kazumasa; Lee, Jae-Lyun; Kitamura, Hiroshi; Kume, Haruki; Bellmunt, Joaquim

2016-04-01

Single-agent taxanes are commonly used as salvage systemic therapy for patients with advanced urothelial carcinoma (UC). To study the impact of combination chemotherapy delivering a taxane plus other chemotherapeutic agents compared with single-agent taxane as salvage therapy. Individual patient-level data from phase 2 trials of salvage systemic therapy were used. Trials evaluating either single agents (paclitaxel or docetaxel) or combination chemotherapy (taxane plus one other chemotherapeutic agent or more) following prior platinum-based therapy were used. Information regarding the known major baseline prognostic factors was required: time from prior chemotherapy, hemoglobin, performance status, albumin, and liver metastasis status. Cox proportional hazards regression was used to evaluate the association of prognostic factors and combination versus single-agent chemotherapy with overall survival (OS). Data were available from eight trials including 370 patients; two trials (n=109) evaluated single-agent chemotherapy with docetaxel (n=72) and cremophor-free paclitaxel (n=37), and six trials (n=261) evaluated combination chemotherapy with gemcitabine-paclitaxel (two trials, with n=99 and n=24), paclitaxel-cyclophosphamide (n=32), paclitaxel-ifosfamide-nedaplatin (n=45), docetaxel-ifosfamide-cisplatin (n=26), and paclitaxel-epirubicin (n=35). On multivariable analysis after adjustment for baseline prognostic factors, combination chemotherapy was independently and significantly associated with improved OS (hazard ratio: 0.60; 95% confidence interval, 0.45-0.82; p=0.001). The retrospective design of this analysis and the trial-eligible population were inherent limitations. Patients enrolled in trials of combination chemotherapy exhibited improved OS compared with patients enrolled in trials of single-agent chemotherapy as salvage therapy for advanced UC. Prospective randomized trials are required to validate a potential role for rational and tolerable combination chemotherapeutic regimens for the salvage therapy of advanced UC. This retrospective study suggests that a combination of chemotherapy agents may extend survival compared with single-agent chemotherapy in selected patients with metastatic urothelial cancer progressing after prior chemotherapy. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

[Clinical forms of major depressive states observed in the Ivory Coast. Classification trial].

PubMed

Megglé, D; Série, E; Veillon, F; Delafosse, J; Hazera, M

1989-12-01

After a previous analysis of African depressions in studies based on the use of D.S.M. III as a preliminary tool, the authors are now seeking to understand more directly the different ways for depressed Ivorians to express the lowering of self-esteem, as well as the various meanings of agitation observed among them. An attempt of nosographic classification, closely linked with local reality, has been extracted from this material by the authors.

Interobserver agreement in analysis of cardiotocograms recorded during trial of labor after cesarean.

PubMed

Caning, M M; Thisted, D L A; Amer-Wählin, I; Laier, G H; Krebs, L

2018-05-17

To examine interobserver agreement in intrapartum cardiotocography (CTG) classification in women undergoing trial of labor after a cesarean section (TOLAC) at term with or without complete uterine rupture. Nineteen blinded and independent Danish obstetricians assessed CTG tracings from 47 women (174 individual pages) with a complete uterine rupture during TOLAC and 37 women (133 individual pages) with no uterine rupture during TOLAC. Individual pages with CTG tracings lasting at least 20 min were evaluated by three different assessors and counted as an individual case. The tracings were analyzed according to the modified version of the Federation of Gynaecology and Obstetrics (FIGO) guidelines elaborated for the use of STAN (ST-analysis). Occurrence of defined abnormalities was recorded and the tracings were classified as normal, suspicious, pathological, or preterminal. The interobserver agreement was evaluated using Fleiss' kappa. Agreement on classification of a preterminal CTG was almost perfect. The interobserver agreement on normal, suspicious or pathological CTG was moderate to substantial. Regarding the presence of severe variable decelerations, the agreement was moderate. No statistical difference was found in the interobserver agreement between classification of tracings from women undergoing TOLAC with and without complete uterine rupture. The interobserver agreement on classification of CTG tracings from high-risk deliveries during TOLAC is best for assessment of a preterminal CTG and the poorest for the identification of severe variable decelerations.

Adding an alcohol-related risk score to an existing categorical risk classification for older adults: sensitivity to group differences.

PubMed

Wilson, Sandra R; Fink, Arlene; Verghese, Shinu; Beck, John C; Nguyen, Khue; Lavori, Philip

2007-03-01

To evaluate a new alcohol-related risk score for research use. Using data from a previously reported trial of a screening and education system for older adults (Computerized Alcohol-Related Problems Survey), secondary analyses were conducted comparing the ability of two different measures of risk to detect post-intervention group differences: the original categorical outcome measure and a new, finely grained quantitative risk score based on the same research-based risk factors. Three primary care group practices in southern California. Six hundred sixty-five patients aged 65 and older. A previously calculated, three-level categorical classification of alcohol-related risk and a newly developed quantitative risk score. Mean post-intervention risk scores differed between the three experimental conditions: usual care, patient report, and combined report (P<.001). The difference between the combined report and usual care was significant (P<.001) and directly proportional to baseline risk. The three-level risk classification did not reveal approximately 57.3% of the intervention effect detected by the risk score. The risk score also was sufficiently sensitive to detect the intervention effect within the subset of hypertensive patients (n=112; P=.001). As an outcome measure in intervention trials, the finely grained risk score is more sensitive than the trinary risk classification. The additional clinical value of the risk score relative to the categorical measure needs to be determined.

Correspondence between EQ-5D health state classifications and EQ VAS scores.

PubMed

Whynes, David K

2008-11-07

The EQ-5D health-related quality of life instrument comprises a health state classification followed by a health evaluation using a visual analogue scale (VAS). The EQ-5D has been employed frequently in economic evaluations, yet the relationship between the two parts of the instrument remains ill-understood. In this paper, we examine the correspondence between VAS scores and health state classifications for a large sample, and identify variables which contribute to determining the VAS scores independently of the health states as classified. A UK trial of management of low-grade abnormalities detected on screening for cervical pre-cancer (TOMBOLA) provided EQ-5D data for over 3,000 women. Information on distress and multi-dimensional health locus of control had been collected using other instruments. A linear regression model was fitted, with VAS score as the dependent variable. Independent variables comprised EQ-5D health state classifications, distress, locus of control, and socio-demographic characteristics. Equivalent EQ-5D and distress data, collected at twelve months, were available for over 2,000 of the women, enabling us to predict changes in VAS score over time from changes in EQ-5D classification and distress. In addition to EQ-5D health state classification, VAS score was influenced by the subject's perceived locus of control, and by her age, educational attainment, ethnic origin and smoking behaviour. Although the EQ-5D classification includes a distress dimension, the independent measure of distress was an additional determinant of VAS score. Changes in VAS score over time were explained by changes in both EQ-5D severities and distress. Women allocated to the experimental management arm of the trial reported an increase in VAS score, independently of any changes in health state and distress. In this sample, EQ VAS scores were predictable from the EQ-5D health state classification, although there also existed other group variables which contributed systematically and independently towards determining such scores. These variables comprised psychological disposition, socio-demographic factors such as age and education, clinically-important distress, and the clinical intervention itself. ISRCTN34841617.

Comparing drug classification systems.

PubMed

Mahoney, Anne; Evans, Jonathan

2008-11-06

An essential quality of drug classification systems is the ability to assign medications to a structured hierarchy for categories such as mechanism of action, physiological effects, and therapeutic indications. No single classification system can meet all of these needs; however, there should be consistency among those that group by the same underlying principals. We discovered discrepancies in how drugs with multiple therapeutic indications are classified among four widely used schemas.

Wishart Deep Stacking Network for Fast POLSAR Image Classification.

PubMed

Jiao, Licheng; Liu, Fang

2016-05-11

Inspired by the popular deep learning architecture - Deep Stacking Network (DSN), a specific deep model for polarimetric synthetic aperture radar (POLSAR) image classification is proposed in this paper, which is named as Wishart Deep Stacking Network (W-DSN). First of all, a fast implementation of Wishart distance is achieved by a special linear transformation, which speeds up the classification of POLSAR image and makes it possible to use this polarimetric information in the following Neural Network (NN). Then a single-hidden-layer neural network based on the fast Wishart distance is defined for POLSAR image classification, which is named as Wishart Network (WN) and improves the classification accuracy. Finally, a multi-layer neural network is formed by stacking WNs, which is in fact the proposed deep learning architecture W-DSN for POLSAR image classification and improves the classification accuracy further. In addition, the structure of WN can be expanded in a straightforward way by adding hidden units if necessary, as well as the structure of the W-DSN. As a preliminary exploration on formulating specific deep learning architecture for POLSAR image classification, the proposed methods may establish a simple but clever connection between POLSAR image interpretation and deep learning. The experiment results tested on real POLSAR image show that the fast implementation of Wishart distance is very efficient (a POLSAR image with 768000 pixels can be classified in 0.53s), and both the single-hidden-layer architecture WN and the deep learning architecture W-DSN for POLSAR image classification perform well and work efficiently.

Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm.

PubMed

Al Ajmi, Eiman; Forghani, Behzad; Reinhold, Caroline; Bayat, Maryam; Forghani, Reza

2018-06-01

There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm. Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set. Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis. Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours. • We present and validate a paradigm for texture analysis of DECT scans. • Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis. • DECT texture analysis has high accura\\cy for diagnosis of benign parotid tumours. • DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.

Single embryo transfer by Day 3 time-lapse selection versus Day 5 conventional morphological selection: a randomized, open-label, non-inferiority trial.

PubMed

Yang, Lanlin; Cai, Sufen; Zhang, Shuoping; Kong, Xiangyi; Gu, Yifan; Lu, Changfu; Dai, Jing; Gong, Fei; Lu, Guangxiu; Lin, Ge

2018-05-01

Does single cleavage-stage (Day 3) embryo transfer using a time-lapse (TL) hierarchical classification model achieve comparable ongoing pregnancy rates (OPR) to single blastocyst (Day 5) transfer by conventional morphological (CM) selection? Day 3 single embryo transfer (SET) with a hierarchical classification model had a significantly lower OPR compared with Day 5 SET with CM selection. Cleavage-stage SET is an alternative to blastocyst SET. Time-lapse imaging assists better embryo selection, based on studies of pregnancy outcomes when adding time-lapse imaging to CM selection at the cleavage or blastocyst stage. This single-centre, randomized, open-label, active-controlled, non-inferiority study included 600 women between October 2015 and April 2017. Eligible patients were Chinese females, aged ≤36 years, who were undergoing their first or second fresh IVF cycle using their own oocytes, and who had FSH levels ≤12 IU/mL on Day 3 of the cycle and 10 or more oocytes retrieved. Patients who had underlying uterine conditions, oocyte donation, recurrent pregnancy loss, abnormal oocytes or <6 normally fertilized embryos (2PN) were excluded from the study participation. Patients were randomized 1:1 to either the cleavage-stage SET with a time-lapse hierarchical classification model for selection (D3 + TL) or blastocyst SET with CM selection (D5 + CM). All normally fertilized zygotes were cultured in Primo Vision. The study was conducted at a tertiary IVF centre (CITIC-Xiangya) and OPR was the primary outcome. A total of 600 patients were randomized to the two groups, among which 585 (D3 + TL = 290, D5 + CM = 295) were included in the Modified-intention-to-treat (mITT) population and 517 (D3 + TL = 261, D5 + CM = 256) were included in the PP population. In the per protocol (PP) population, OPR was significantly lower in the D3 group (59.4%, 155/261) than in the D5 group (68.4%, 175/256) (difference: -9.0%, 95% CI: -17.1%, -0.7%, P = 0.03). Analysis in mITT population showed a marginally significant difference in the OPR between the D3 + TL and D5 + CM groups (56.6 versus 64.1%, difference: -7.5%, 95% CI: -15.4%, 0.4%, P = 0.06). The D3 + TL group resulted in a markedly lower implantation rate than the D5 + CM group (64.4 versus 77.0%; P = 0.002) in the PP analysis, however, the early miscarriage rate did not significantly differ between the two groups. The study lacked a direct comparison between time-lapse and CM selections at cleavage-stage SET and was statistically underpowered to detect non-inferiority. The subject's eligibility criteria favouring women with a good prognosis for IVF weakened the generalizability of the results. The OPR from Day 3 cleavage-stage SET using hierarchical classification time-lapse selection was significantly lower compared with that from Day 5 blastocyst SET using conventional morphology, yet it appeared to be clinically acceptable in women underwent IVF. This study is supported by grants from Ferring Pharmaceuticals and the Program for New Century Excellent Talents in University, China. ChiCTR-ICR-15006600. 16 June 2015. 1 October 2015.

An Analysis of Full Scale Measurements on M/V Stewart J. Cort during the 1979 and 1980 Trial Programs. Parts I and II.

DTIC Science & Technology

1982-02-01

IKCuNITY CLASSIFICATION OF Tm4iS IMAGE (Vrhn Dot& Entered) .,.-’- . . . . . ... .. ... " . . ...... ....... .. . . . . . . . . TABLE OF CONTENTS...11-19 APPENDIX D: BASIC PROCESSING ............................... 11-21 APPENDIX E: SIMULATION OF DATA...equipment previously developed, and an on-board data processing system. These full scale ship trials were the first in history with the objective of directly

Towards a hemodynamic BCI using transcranial Doppler without user-specific training data

NASA Astrophysics Data System (ADS)

Aleem, Idris; Chau, Tom

2013-02-01

Transcranial Doppler (TCD) was recently introduced as a new brain-computer interface (BCI) modality for detecting task-induced hemispheric lateralization. To date, single-trial discrimination between a lateralized mental activity and a rest state has been demonstrated with long (45 s) activation time periods. However, the possibility of detecting successive activations in a user-independent framework (i.e. without training data from the user) remains an open question. Objective. The objective of this research was to assess TCD-based detection of lateralized mental activity with a user-independent classifier. In so doing, we also investigated the accuracy of detecting successive lateralizations. Approach. TCD data from 18 participants were collected during verbal fluency, mental rotation tasks and baseline counting tasks. Linear discriminant analysis and a set of four time-domain features were used to classify successive left and right brain activations. Main results. In a user-independent framework, accuracies up to 74.6 ± 12.6% were achieved using training data from a single participant, and lateralization task durations of 18 s. Significance. Subject-independent, algorithmic classification of TCD signals corresponding to successive brain lateralization may be a feasible paradigm for TCD-BCI design.

Effect of coenzyme Q10 supplementation on heart failure: a meta-analysis123

PubMed Central

Thompson-Paul, Angela M; Bazzano, Lydia A

2013-01-01

Background: Coenzyme Q10 (CoQ10; also called ubiquinone) is an antioxidant that has been postulated to improve functional status in congestive heart failure (CHF). Several randomized controlled trials have examined the effects of CoQ10 on CHF with inconclusive results. Objective: The objective of this meta-analysis was to evaluate the impact of CoQ10 supplementation on the ejection fraction (EF) and New York Heart Association (NYHA) functional classification in patients with CHF. Design: A systematic review of the literature was conducted by using databases including MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, and manual examination of references from selected studies. Studies included were randomized controlled trials of CoQ10 supplementation that reported the EF or NYHA functional class as a primary outcome. Information on participant characteristics, trial design and duration, treatment, dose, control, EF, and NYHA classification were extracted by using a standardized protocol. Results: Supplementation with CoQ10 resulted in a pooled mean net change of 3.67% (95% CI: 1.60%, 5.74%) in the EF and −0.30 (95% CI: −0.66, 0.06) in the NYHA functional class. Subgroup analyses showed significant improvement in EF for crossover trials, trials with treatment duration ≤12 wk in length, studies published before 1994, and studies with a dose ≤100 mg CoQ10/d and in patients with less severe CHF. These subgroup analyses should be interpreted cautiously because of the small number of studies and patients included in each subgroup. Conclusions: Pooled analyses of available randomized controlled trials suggest that CoQ10 may improve the EF in patients with CHF. Additional well-designed studies that include more diverse populations are needed. PMID:23221577

Index finger motor imagery EEG pattern recognition in BCI applications using dictionary cleaned sparse representation-based classification for healthy people

NASA Astrophysics Data System (ADS)

Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Fengkui; Liu, Feixiang

2017-09-01

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface (BCI) has shown its effectiveness for the control of rehabilitation devices designed for large body parts of the patients with neurologic impairments. In order to validate the feasibility of using EEG to decode the MI of a single index finger and constructing a BCI-enhanced finger rehabilitation system, we collected EEG data during right hand index finger MI and rest state for five healthy subjects and proposed a pattern recognition approach for classifying these two mental states. First, Fisher's linear discriminant criteria and power spectral density analysis were used to analyze the event-related desynchronization patterns. Second, both band power and approximate entropy were extracted as features. Third, aiming to eliminate the abnormal samples in the dictionary and improve the classification performance of the conventional sparse representation-based classification (SRC) method, we proposed a novel dictionary cleaned sparse representation-based classification (DCSRC) method for final classification. The experimental results show that the proposed DCSRC method gives better classification accuracies than SRC and an average classification accuracy of 81.32% is obtained for five subjects. Thus, it is demonstrated that single right hand index finger MI can be decoded from the sensorimotor rhythms, and the feature patterns of index finger MI and rest state can be well recognized for robotic exoskeleton initiation.

Development and feasibility of the misuse, abuse, and diversion drug event reporting system (MADDERS®).

PubMed

Treister, Roi; Trudeau, Jeremiah J; Van Inwegen, Richard; Jones, Judith K; Katz, Nathaniel P

2016-12-01

Inappropriate use of analgesic drugs has become increasingly pervasive over the past decade. Currently, drug abuse potential is primarily assessed post-marketing; no validated tools are available to assess this potential in phase II and III clinical trials. This paper describes the development and feasibility testing of a Misuse, Abuse, and Diversion Drug Event Reporting System (MADDERS), which aims to identify potentially abuse-related events and classify them according to a recently developed classification scheme, allowing the quantification of these events in clinical trials. The system was initially conceived and designed with input from experts and patients, followed by field-testing to assess its feasibility and content validity in both completed and ongoing clinical trials. The results suggest that MADDERS is a feasible system with initial validity. It showed higher rates of the triggering events in subjects taking medications with known abuse potential than in patients taking medications without abuse potential. Additionally, experts agreed on the classification of most abuse-related events in MADDERS. MADDERS is a new systematic approach to collect information on potentially abuse-related events in clinical trials and classify them. The system has demonstrated feasibility for implementation. Additional research is ongoing to further evaluate its validity. Currently, there are no validated tools to assess drug abuse potential during clinical trials. Because of its ease of implementation, its systematic approach, and its preliminary validation results, MADDERS could provide such a tool for clinical trials. (Am J Addict 2016;25:641-651). © 2016 American Academy of Addiction Psychiatry.

Drug-induced sedation endoscopy (DISE) classification systems: a systematic review and meta-analysis.

PubMed

Dijemeni, Esuabom; D'Amone, Gabriele; Gbati, Israel

2017-12-01

Drug-induced sedation endoscopy (DISE) classification systems have been used to assess anatomical findings on upper airway obstruction, and decide and plan surgical treatments and act as a predictor for surgical treatment outcome for obstructive sleep apnoea management. The first objective is to identify if there is a universally accepted DISE grading and classification system for analysing DISE findings. The second objective is to identify if there is one DISE grading and classification treatment planning framework for deciding appropriate surgical treatment for obstructive sleep apnoea (OSA). The third objective is to identify if there is one DISE grading and classification treatment outcome framework for determining the likelihood of success for a given OSA surgical intervention. A systematic review was performed to identify new and significantly modified DISE classification systems: concept, advantages and disadvantages. Fourteen studies proposing a new DISE classification system and three studies proposing a significantly modified DISE classification were identified. None of the studies were based on randomised control trials. DISE is an objective method for visualising upper airway obstruction. The classification and assessment of clinical findings based on DISE is highly subjective due to the increasing number of DISE classification systems. Hence, this creates a growing divergence in surgical treatment planning and treatment outcome. Further research on a universally accepted objective DISE assessment is critically needed.

Maxillectomy defects: a suggested classification scheme.

PubMed

Akinmoladun, V I; Dosumu, O O; Olusanya, A A; Ikusika, O F

2013-06-01

The term "maxillectomy" has been used to describe a variety of surgical procedures for a spectrum of diseases involving a diverse anatomical site. Hence, classifications of maxillectomy defects have often made communication difficult. This article highlights this problem, emphasises the need for a uniform system of classification and suggests a classification system which is simple and comprehensive. Articles related to this subject, especially those with specified classifications of maxillary surgical defects were sourced from the internet through Google, Scopus and PubMed using the search terms maxillectomy defects classification. A manual search through available literature was also done. The review of the materials revealed many classifications and modifications of classifications from the descriptive, reconstructive and prosthodontic perspectives. No globally acceptable classification exists among practitioners involved in the management of diseases in the mid-facial region. There were over 14 classifications of maxillary defects found in the English literature. Attempts made to address the inadequacies of previous classifications have tended to result in cumbersome and relatively complex classifications. A single classification that is based on both surgical and prosthetic considerations is most desirable and is hereby proposed.

European Marketing Authorizations Granted Based on a Single Pivotal Clinical Trial: The Rule or the Exception?

PubMed

Morant, Anne Vinther; Vestergaard, Henrik Tang

2018-07-01

A minimum of two positive, adequate, and well-controlled clinical trials has historically been the gold standard for providing substantial evidence to support regulatory approval of a new medicine. Nevertheless, the present analysis of European Marketing Authorizations granted between 2012 and 2016 showed that 45% of new active substances were approved based on a single pivotal clinical trial. For therapeutic areas such as oncology and cardiovascular diseases, approvals based on a single pivotal trial are the rule rather than the exception, whereas new medicines within the nervous system area were generally supported by two or more pivotal trials. While overall similar trends have been observed in the US, the recent US Food and Drug Administration approvals of nervous system medicines based on a single pivotal trial suggest that a case-by-case scientific evaluation of the totality of evidence is increasingly applied to facilitate faster access of new medicines to patients suffering from serious diseases. © 2017 American Society for Clinical Pharmacology and Therapeutics.

Multidate mapping of mosquito habitat. [Nebraska, South Dakota

NASA Technical Reports Server (NTRS)

Woodzick, T. L.; Maxwell, E. L.

1977-01-01

LANDSAT data from three overpasses formed the data base for a multidate classification of 15 ground cover categories in the margins of Lewis and Clark Lake, a fresh water impoundment between South Dakota and Nebraska. When scaled to match topographic maps of the area, the ground cover classification maps were used as a general indicator of potential mosquito-breeding habitat by distinguishing productive wetlands areas from nonproductive nonwetlands areas. The 12 channel multidate classification was found to have an accuracy 23% higher than the average of the three single date 4 channel classifications.

Exploring the Impact of Target Eccentricity and Task Difficulty on Covert Visual Spatial Attention and Its Implications for Brain Computer Interfacing

PubMed Central

Roijendijk, Linsey; Farquhar, Jason; van Gerven, Marcel; Jensen, Ole; Gielen, Stan

2013-01-01

Objective Covert visual spatial attention is a relatively new task used in brain computer interfaces (BCIs) and little is known about the characteristics which may affect performance in BCI tasks. We investigated whether eccentricity and task difficulty affect alpha lateralization and BCI performance. Approach We conducted a magnetoencephalography study with 14 participants who performed a covert orientation discrimination task at an easy or difficult stimulus contrast at either a near (3.5°) or far (7°) eccentricity. Task difficulty was manipulated block wise and subjects were aware of the difficulty level of each block. Main Results Grand average analyses revealed a significantly larger hemispheric lateralization of posterior alpha power in the difficult condition than in the easy condition, while surprisingly no difference was found for eccentricity. The difference between task difficulty levels was significant in the interval between 1.85 s and 2.25 s after cue onset and originated from a stronger decrease in the contralateral hemisphere. No significant effect of eccentricity was found. Additionally, single-trial classification analysis revealed a higher classification rate in the difficult (65.9%) than in the easy task condition (61.1%). No effect of eccentricity was found in classification rate. Significance Our results indicate that manipulating the difficulty of a task gives rise to variations in alpha lateralization and that using a more difficult task improves covert visual spatial attention BCI performance. The variations in the alpha lateralization could be caused by different factors such as an increased mental effort or a higher visual attentional demand. Further research is necessary to discriminate between them. We did not discover any effect of eccentricity in contrast to results of previous research. PMID:24312477

Exploring the impact of target eccentricity and task difficulty on covert visual spatial attention and its implications for brain computer interfacing.

PubMed

Roijendijk, Linsey; Farquhar, Jason; van Gerven, Marcel; Jensen, Ole; Gielen, Stan

2013-01-01

Covert visual spatial attention is a relatively new task used in brain computer interfaces (BCIs) and little is known about the characteristics which may affect performance in BCI tasks. We investigated whether eccentricity and task difficulty affect alpha lateralization and BCI performance. We conducted a magnetoencephalography study with 14 participants who performed a covert orientation discrimination task at an easy or difficult stimulus contrast at either a near (3.5°) or far (7°) eccentricity. Task difficulty was manipulated block wise and subjects were aware of the difficulty level of each block. Grand average analyses revealed a significantly larger hemispheric lateralization of posterior alpha power in the difficult condition than in the easy condition, while surprisingly no difference was found for eccentricity. The difference between task difficulty levels was significant in the interval between 1.85 s and 2.25 s after cue onset and originated from a stronger decrease in the contralateral hemisphere. No significant effect of eccentricity was found. Additionally, single-trial classification analysis revealed a higher classification rate in the difficult (65.9%) than in the easy task condition (61.1%). No effect of eccentricity was found in classification rate. Our results indicate that manipulating the difficulty of a task gives rise to variations in alpha lateralization and that using a more difficult task improves covert visual spatial attention BCI performance. The variations in the alpha lateralization could be caused by different factors such as an increased mental effort or a higher visual attentional demand. Further research is necessary to discriminate between them. We did not discover any effect of eccentricity in contrast to results of previous research.

Automated structure and flow measurement - a promising tool in nailfold capillaroscopy.

PubMed

Berks, Michael; Dinsdale, Graham; Murray, Andrea; Moore, Tonia; Manning, Joanne; Taylor, Chris; Herrick, Ariane L

2018-07-01

Despite increasing interest in nailfold capillaroscopy, objective measures of capillary structure and blood flow have been little studied. We aimed to test the hypothesis that structural measurements, capillary flow, and a combined measure have the predictive power to separate patients with systemic sclerosis (SSc) from those with primary Raynaud's phenomenon (PRP) and healthy controls (HC). 50 patients with SSc, 12 with PRP, and 50 HC were imaged using a novel capillaroscopy system that generates high-quality nailfold images and provides fully-automated measurements of capillary structure and blood flow (capillary density, mean width, maximum width, shape score, derangement and mean flow velocity). Population statistics summarise the differences between the three groups. Areas under ROC curves (A Z ) were used to measure classification accuracy when assigning individuals to SSc and HC/PRP groups. Statistically significant differences in group means were found between patients with SSc and both HC and patients with PRP, for all measurements, e.g. mean width (μm) ± SE: 15.0 ± 0.71, 12.7 ± 0.74 and 11.8 ± 0.23 for SSc, PRP and HC respectively. Combining the five structural measurements gave better classification (A Z  = 0.919 ± 0.026) than the best single measurement (mean width, A Z  = 0.874 ± 0.043), whilst adding flow further improved classification (A Z  = 0.930 ± 0.024). Structural and blood flow measurements are both able to distinguish patients with SSc from those with PRP/HC. Importantly, these hold promise as clinical trial outcome measures for treatments aimed at improving finger blood flow or microvascular remodelling. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Classification of Dynamical Diffusion States in Single Molecule Tracking Microscopy

PubMed Central

Bosch, Peter J.; Kanger, Johannes S.; Subramaniam, Vinod

2014-01-01

Single molecule tracking of membrane proteins by fluorescence microscopy is a promising method to investigate dynamic processes in live cells. Translating the trajectories of proteins to biological implications, such as protein interactions, requires the classification of protein motion within the trajectories. Spatial information of protein motion may reveal where the protein interacts with cellular structures, because binding of proteins to such structures often alters their diffusion speed. For dynamic diffusion systems, we provide an analytical framework to determine in which diffusion state a molecule is residing during the course of its trajectory. We compare different methods for the quantification of motion to utilize this framework for the classification of two diffusion states (two populations with different diffusion speed). We found that a gyration quantification method and a Bayesian statistics-based method are the most accurate in diffusion-state classification for realistic experimentally obtained datasets, of which the gyration method is much less computationally demanding. After classification of the diffusion, the lifetime of the states can be determined, and images of the diffusion states can be reconstructed at high resolution. Simulations validate these applications. We apply the classification and its applications to experimental data to demonstrate the potential of this approach to obtain further insights into the dynamics of cell membrane proteins. PMID:25099798

Multi-class biological tissue classification based on a multi-classifier: Preliminary study of an automatic output power control for ultrasonic surgical units.

PubMed

Youn, Su Hyun; Sim, Taeyong; Choi, Ahnryul; Song, Jinsung; Shin, Ki Young; Lee, Il Kwon; Heo, Hyun Mu; Lee, Daeweon; Mun, Joung Hwan

2015-06-01

Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. For this purpose, a multi-classifier was built based on single classifiers with high classification rates, and the classification accuracy of the proposed model was compared with that of single classifiers for various electrode types (Type-I: 6 mm invasive; Type-II: 3 mm invasive; Type-III: surface). The sensitivity and positive predictive value (PPV) of the multi-classifier by cross checks were determined. According to the 10-fold cross validation results, the classification accuracy of the proposed model was significantly higher (p<0.05 or <0.01) than that of existing single classifiers for all electrode types. In particular, the classification accuracy of the proposed model was highest when the 3mm invasive electrode (Type-II) was used (sensitivity=97.33-100.00%; PPV=96.71-100.00%). The results of this study are an important contribution to achieving automatic optimal output power adjustment of USUs according to the properties of individual tissues. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of machine learning on brain cancer multiclass classification

NASA Astrophysics Data System (ADS)

Panca, V.; Rustam, Z.

2017-07-01

Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

Safety of intrathecal autologous adipose-derived mesenchymal stromal cells in patients with ALS

PubMed Central

Madigan, Nicolas N.; Morris, Jonathan; Jentoft, Mark; Sorenson, Eric J.; Butler, Greg; Gastineau, Dennis; Dietz, Allan; Windebank, Anthony J.

2016-01-01

Objective: To determine the safety of intrathecal autologous adipose-derived mesenchymal stromal cell treatment for amyotrophic lateral sclerosis (ALS). Methods: Participants with ALS were enrolled and treated in this phase I dose-escalation safety trial, ranging from 1 × 107 (single dose) to 1 × 108 cells (2 monthly doses). After intrathecal treatments, participants underwent standardized follow-up, which included clinical examinations, revised ALS Functional Rating Scale (ALSFRS-R) questionnaire, blood and CSF sampling, and MRI of the neuroaxis. Results: Twenty-seven patients with ALS were enrolled and treated in this study. The safety profile was positive, with the most common side effects reported being temporary low back and radicular leg pain at the highest dose level. These clinical findings were associated with elevated CSF protein and nucleated cells with MRI of thickened lumbosacral nerve roots. Autopsies from 4 treated patients did not show evidence of tumor formation. Longitudinal ALSFRS-R questionnaires confirmed continued progression of disease in all treated patients. Conclusions: Intrathecal treatment of autologous adipose-derived mesenchymal stromal cells appears safe at the tested doses in ALS. These results warrant further exploration of efficacy in phase II trials. Classification of evidence: This phase I study provides Class IV evidence that in patient with ALS, intrathecal autologous adipose-derived mesenchymal stromal cell therapy is safe. PMID:27784774

Ensemble of classifiers for confidence-rated classification of NDE signal

NASA Astrophysics Data System (ADS)

Banerjee, Portia; Safdarnejad, Seyed; Udpa, Lalita; Udpa, Satish

2016-02-01

Ensemble of classifiers in general, aims to improve classification accuracy by combining results from multiple weak hypotheses into a single strong classifier through weighted majority voting. Improved versions of ensemble of classifiers generate self-rated confidence scores which estimate the reliability of each of its prediction and boost the classifier using these confidence-rated predictions. However, such a confidence metric is based only on the rate of correct classification. In existing works, although ensemble of classifiers has been widely used in computational intelligence, the effect of all factors of unreliability on the confidence of classification is highly overlooked. With relevance to NDE, classification results are affected by inherent ambiguity of classifica-tion, non-discriminative features, inadequate training samples and noise due to measurement. In this paper, we extend the existing ensemble classification by maximizing confidence of every classification decision in addition to minimizing the classification error. Initial results of the approach on data from eddy current inspection show improvement in classification performance of defect and non-defect indications.

The footprints of visual attention in the Posner cueing paradigm revealed by classification images

NASA Technical Reports Server (NTRS)

Eckstein, Miguel P.; Shimozaki, Steven S.; Abbey, Craig K.

2002-01-01

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Musculoskeletal manifestations of systemic lupus erythmatosus.

PubMed

Mahmoud, Khaled; Zayat, Ahmed; Vital, Edward M

2017-09-01

Imaging studies suggest potential changes to the classification and assessment of inflammatory musculoskeletal lupus. This is important because of the burden of disease but the potential for new targeted therapies. Using our current classification and treatment, musculoskeletal symptoms continue to impact significantly on quality of life and work disability. Ultrasound and MRI studies suggested that new approaches to the diagnosis, classification, and evaluation of these symptoms are needed. Many patients with pain but no synovitis have ultrasound-proven joint and tendon inflammation but would not qualify for clinical trials or score highly on disease activity instruments. MRI studies show that erosions are more common than previously thought and may have a different pathogenesis than RA. Immunology studies suggest differences from other autoimmune synovitis, with a complex role for type I interferons. A wide range of biologic therapies appear more consistently effective for arthritis than some other manifestations. Changes to the selection of patients for therapy and stratification using musculoskeletal imaging may offer new approaches to clinical trials and the routine care of systemic lupus erythematosus patients with inflammatory musculoskeletal symptoms. Outcomes may thereby be improved using existing therapies. There are significant knowledge gaps that must be addressed to achieve these potential improved outcomes.

Informative value of Patient Reported Outcomes (PRO) in Health Technology Assessment (HTA)

PubMed Central

Brettschneider, Christian; Lühmann, Dagmar; Raspe, Heiner

2011-01-01

Background “Patient-Reported Outcome” (PRO) is used as an umbrella term for different concepts for measuring subjectively perceived health status e. g. as treatment effects. Their common characteristic is, that the appraisal of the health status is reported by the patient himself. In order to describe the informative value of PRO in Health Technology Assessment (HTA) first an overview of concepts, classifications and methods of measurement is given. The overview is complemented by an empirical analysis of clinical trials and HTA-reports on rheumatoid arthritis and breast cancer in order to report on type, frequency and consequences of PRO used in these documents. Methods For both issues systematic reviews of the literature have been performed. The search for methodological literature covers the publication period from 1990 to 2009, the search for clinical trials of rheumatoid arthritis and breast cancer covers the period 2005 to 2009. Both searches were performed in the medical databases of the German Institute of Medical Documentation and Information (DIMDI). The search for HTA-reports and methodological papers of HTA-agencies was performed in the CRD-Databases (CRD = Centre for Reviews and Dissemination) and by handsearching the websites of INAHTA member agencies (INAHTA = International Network of Agencies for Health Technology Assessment). For all issues specific inclusion and exclusion criteria were defined. The methodological quality of randomized controlled trials (RCT) was assessed by a modified version of the Cochrane Risk of Bias Tool. For the methodological part information extraction from the literature is structured by the report’s chapters, for the empirical part data extraction sheets were constructed. All information is summarized in a qualitative manner. Results Concerning the methodological issues the literature search retrieved 158 documents (87 documents related to definition or classification, 125 documents related to operationalisation of PRO). For the empirical analyses 225 RCT (rheumatoid arthritis: 77; breast cancer: 148) and 40 HTA-reports and method papers were found. The analysis of the methodological literature confirms the role of PRO as an umbrella term for a variety of different concepts. The newest classification system facilitates the description of PRO measures by construct, target population and the method of measurement. Steps of operationalisation involve defining a conceptual framework, instrument development, exploration of measurement properties or, possibly, the modification of existing instruments. Seven out of 59 RCT analysing the effects of antibody therapy for rheumatoid arthritis define PRO as the primary endpoint, 38 trials utilize composite measures (ACR, DAS) and ten trials report clinical or radiological parameters as the primary endpoint. Six out of 123 chemotherapy trials for breast cancer define PRO as the primary endpoint, while 98 trials report clinical endpoints (survival, tumour response, progression) in their primary analyses. Discrepancies in the number of trials result from inaccurate specifications of endpoints in the publications. This distribution is reflected in the HTA-reports: while almost all reports on rheumatoid arthritis refer to PRO, this is only the case in about half of the reports on breast cancer. Conclusions As definition and classification of PRO are concerned, coherent concepts are found in the literature. Their operationalisation and implementation must be guided by scientific principles. The type and frequency of PRO used in clinical trials largely depend on the disease analysed. The HTA-community seems to pursue the utilization of PRO proactively – in case of missing data the need for further research is stated. PMID:21468289

Informative value of Patient Reported Outcomes (PRO) in Health Technology Assessment (HTA).

PubMed

Brettschneider, Christian; Lühmann, Dagmar; Raspe, Heiner

2011-02-02

"Patient-Reported Outcome" (PRO) is used as an umbrella term for different concepts for measuring subjectively perceived health status e. g. as treatment effects. Their common characteristic is, that the appraisal of the health status is reported by the patient himself. In order to describe the informative value of PRO in Health Technology Assessment (HTA) first an overview of concepts, classifications and methods of measurement is given. The overview is complemented by an empirical analysis of clinical trials and HTA-reports on rheumatoid arthritis and breast cancer in order to report on type, frequency and consequences of PRO used in these documents. For both issues systematic reviews of the literature have been performed. The search for methodological literature covers the publication period from 1990 to 2009, the search for clinical trials of rheumatoid arthritis and breast cancer covers the period 2005 to 2009. Both searches were performed in the medical databases of the German Institute of Medical Documentation and Information (DIMDI). The search for HTA-reports and methodological papers of HTA-agencies was performed in the CRD-Databases (CRD = Centre for Reviews and Dissemination) and by handsearching the websites of INAHTA member agencies (INAHTA = International Network of Agencies for Health Technology Assessment). For all issues specific inclusion and exclusion criteria were defined. The methodological quality of randomized controlled trials (RCT) was assessed by a modified version of the Cochrane Risk of Bias Tool. For the methodological part information extraction from the literature is structured by the report's chapters, for the empirical part data extraction sheets were constructed. All information is summarized in a qualitative manner. Concerning the methodological issues the literature search retrieved 158 documents (87 documents related to definition or classification, 125 documents related to operationalisation of PRO). For the empirical analyses 225 RCT (rheumatoid arthritis: 77; breast cancer: 148) and 40 HTA-reports and method papers were found. The analysis of the methodological literature confirms the role of PRO as an umbrella term for a variety of different concepts. The newest classification system facilitates the description of PRO measures by construct, target population and the method of measurement. Steps of operationalisation involve defining a conceptual framework, instrument development, exploration of measurement properties or, possibly, the modification of existing instruments. Seven out of 59 RCT analysing the effects of antibody therapy for rheumatoid arthritis define PRO as the primary endpoint, 38 trials utilize composite measures (ACR, DAS) and ten trials report clinical or radiological parameters as the primary endpoint. Six out of 123 chemotherapy trials for breast cancer define PRO as the primary endpoint, while 98 trials report clinical endpoints (survival, tumour response, progression) in their primary analyses. Discrepancies in the number of trials result from inaccurate specifications of endpoints in the publications. This distribution is reflected in the HTA-reports: while almost all reports on rheumatoid arthritis refer to PRO, this is only the case in about half of the reports on breast cancer. As definition and classification of PRO are concerned, coherent concepts are found in the literature. Their operationalisation and implementation must be guided by scientific principles. The type and frequency of PRO used in clinical trials largely depend on the disease analysed. The HTA-community seems to pursue the utilization of PRO proactively - in case of missing data the need for further research is stated.

Retinopathy of Prematurity: Clinical Features, Classification, Natural History, Management and Outcome.

PubMed

Shah, Parag K; Prabhu, Vishma; Ranjan, Ratnesh; Narendran, Venkatapathy; Kalpana, Narendran

2016-11-07

Retinopathy of prematurity is an avoidable cause of childhood blindness. Proper understanding of the classification and treatment methods is a must in tackling this disease. Literature search with PubMed was conducted covering the period 1940-2015 with regards to retinopathy of prematurity, retrolental fibroplasia, its natural history, classification and treatment. The clinical features, screening and staging of retinopathy of prematurity according to International classification of retinopathy of prematurity (ICROP) has been included with illustrations. The standard current treatment indications, modalities and outcomes from landmark randomized controlled trials on retinopathy of prematurity have been mentioned. This review would help pediatricians to update their current knowledge on classification and treatment of retinopathy of prematurity. Screening for retinopathy of prematurity, in India, should be performed in all preterm neonates who are born <34 weeks gestation and/or <1750 grams birthweight; as well as in babies 34-36 weeks gestation or 1750-2000 grams birthweight if they have risk factors for ROP. Screening should start by one month after birth.

Sentiment classification technology based on Markov logic networks

NASA Astrophysics Data System (ADS)

He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

2016-07-01

With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

Randomized Trial of Asprin as Adjuvant Therapy for Node-Positive Breast Cancer

DTIC Science & Technology

2017-10-01

Department of the Army position, policy or decision unless so designated by other documentation. REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704...penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR...TERMS Breast cancer, adjuvant treatment, aspirin, randomized controlled trial 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF

A Double Blind Trial of Divalproex Sodium for Affective Lability and Alcohol Use Following Traumatic Brain Injury

DTIC Science & Technology

2009-10-01

SUBJECT TERMS Traumatic Brain Injury, Alcohol Use , Mood , Mood Stabilization 16. SECURITY CLASSIFICATION OF: U 17. LIMITATION OF ABSTRACT 18...1995 for this indication. Also, it is used in conjunction with lithium or carbamazepine to prevent recurrent manic or depressive episodes during long...0652 TITLE: A Double Blind Trial of Divalproex Sodium for Affective Lability and Alcohol Use Following Traumatic Brain Injury PRINCIPAL

One Hundred Twenty-One Resected Solid Pseudopapillary Tumors of the Pancreas: An 8-Year Single-Institution Experience at Zhongshan Hospital, Shanghai, China.

PubMed

Xu, Yadong; Zhao, Guochao; Pu, Ning; Nuerxiati, Abulimiti; Ji, Yuan; Zhang, Lei; Rong, Yefei; Lou, Wenhui; Wang, Dansong; Kuang, Tiantao; Xu, Xuefeng; Wu, Wenchuan

2017-09-01

The aims of this study were to introduce our experience with treating patients with pancreatic solid pseudopapillary tumors (SPTs) and to investigate the clinical risk factors for recurrence of SPTs because no consensus has been established to date. One hundred twenty-one patients underwent surgical resection from January 2008 to December 2015 in our institution. Clinical data were collected from the standardized reports. Of the 121 patients, 93 (76.9%) were women, 28 (23.1%) were men, and the mean age at diagnosis was 33.7 years (range, 11-68 years). Sixty patients were subjected to short-term complications, and 8 patients experienced long-term complications, some of whom may require surgery. The tumor located in the distal pancreas (P = 0.02), and a Ki-67 index value > 1.5 (P = 0.01) indicated malignancy according to the World Health Organization 2000 classification. One hundred three patients responded to follow-up, and 3 cases (2.9%) were subject to liver metastases. Recurrence was more frequently observed in tumors classified as high-grade malignancies according to the World Health Organization 2010 classification (P = 0.013), synchronous metastases (P < 0.001), peripancreatic fat infiltration (P = 0.018), and lymphovascular invasion (P < 0.001). Evaluating the risk of the recurrence of SPTs still requires systematic and multicenter trials in the future, even some pathological features showed statistical differences.

Effects of induction docetaxel, platinum, and fluorouracil chemotherapy in patients with stage III or IVA/B nasopharyngeal cancer treated with concurrent chemoradiation therapy: Final results of 2 parallel phase 2 clinical trials.

PubMed

Kong, Lin; Zhang, Youwang; Hu, Chaosu; Guo, Ye; Lu, Jiade J

2017-06-15

The effects of docetaxel, platinum, and fluorouracil (TPF) induction chemotherapy plus concurrent chemoradiotherapy (CCRT) on locoregionally advanced nasopharyngeal cancer (NPC) are unclear. This study examined the long-term outcomes of the addition of this regimen to CCRT for stage III and IVA/B NPC. Two parallel, single-arm phase 2 trials were performed synchronously to evaluate the efficacy and toxicity of TPF-based induction chemotherapy in patients with stage III or IVA/B NPC. The induction chemotherapy, which preceded standard intensity-modulated radiation therapy/platinum-based chemoradiation, consisted of 3 cycles of docetaxel (75 mg/m 2 on day 1), cisplatin (75 mg/m 2 on day 1), and a continuous infusion of fluorouracil (500 mg/m 2 /d on days 1-5) every 4 weeks. The primary endpoint for both trials was 5-year overall survival (OS). Between January 2007 and July 2010, 52 eligible patients with stage III NPC and 64 eligible patients with nonmetastatic stage IV NPC were accrued to the 2 trials. With a median follow-up of 67 months, the 5-year OS, progression-free survival, distant metastasis-free survival, and local progression-free survival (LPFS) rates were all improved in comparison with historical benchmarks for patients with stage III or IVA/IVB NPC. Multivariate analyses indicated that T and N classifications (T1/T2 vs T3/T4 and N3 vs N0-N2) were the only significant prognosticators for OS. The number of induction chemotherapy cycles was the only significant prognostic factor for predicting LPFS. TPF-based induction chemotherapy appears to significantly improve outcomes in comparison with historical data when it is administered before CCRT for locoregionally advanced NPC. A phase 3 trial is currently being performed to confirm this benefit. Cancer 2017;123:2258-2267. © 2017 American Cancer Society. © 2017 American Cancer Society.

Solid phase excitation-emission fluorescence method for the classification of complex substances: Cortex Phellodendri and other traditional Chinese medicines as examples.

PubMed

Gu, Yao; Ni, Yongnian; Kokot, Serge

2012-09-13

A novel, simple and direct fluorescence method for analysis of complex substances and their potential substitutes has been researched and developed. Measurements involved excitation and emission (EEM) fluorescence spectra of powdered, complex, medicinal herbs, Cortex Phellodendri Chinensis (CPC) and the similar Cortex Phellodendri Amurensis (CPA); these substances were compared and discriminated from each other and the potentially adulterated samples (Caulis mahoniae (CM) and David poplar bark (DPB)). Different chemometrics methods were applied for resolution of the complex spectra, and the excitation spectra were found to be the most informative; only the rank-ordering PROMETHEE method was able to classify the samples with single ingredients (CPA, CPC, CM) or those with binary mixtures (CPA/CPC, CPA/CM, CPC/CM). Interestingly, it was essential to use the geometrical analysis for interactive aid (GAIA) display for a full understanding of the classification results. However, these two methods, like the other chemometrics models, were unable to classify composite spectral matrices consisting of data from samples of single ingredients and binary mixtures; this suggested that the excitation spectra of the different samples were very similar. However, the method is useful for classification of single-ingredient samples and, separately, their binary mixtures; it may also be applied for similar classification work with other complex substances.

Ensemble Sparse Classification of Alzheimer’s Disease

PubMed Central

Liu, Manhua; Zhang, Daoqiang; Shen, Dinggang

2012-01-01

The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer’s disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI). Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification. However, due to noise and small sample size of neuroimaging data, it is challenging to train only a global classifier that can be robust enough to achieve good classification performance. In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. Specifically, to capture the local spatial consistency, each brain image is partitioned into a number of local patches and a subset of patches is randomly selected from the patch pool to build a weak classifier. Here, the sparse representation-based classification (SRC) method, which has shown effective for classification of image data (e.g., face), is used to construct each weak classifier. Then, multiple weak classifiers are combined to make the final decision. We evaluate our method on 652 subjects (including 198 AD patients, 225 MCI and 229 normal controls) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using MR images. The experimental results show that our method achieves an accuracy of 90.8% and an area under the ROC curve (AUC) of 94.86% for AD classification and an accuracy of 87.85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images. PMID:22270352

Somatosensory spatial attention modulates amplitudes, latencies, and latency jitter of laser-evoked brain potentials.

PubMed

Franz, Marcel; Nickel, Moritz M; Ritter, Alexander; Miltner, Wolfgang H R; Weiss, Thomas

2015-04-01

Several studies provided evidence that the amplitudes of laser-evoked potentials (LEPs) are modulated by attention. However, previous reports were based on across-trial averaging of LEP responses at the expense of losing information about intertrial variability related to attentional modulation. The aim of this study was to investigate the effects of somatosensory spatial attention on single-trial parameters (i.e., amplitudes, latencies, and latency jitter) of LEP components (N2 and P2). Twelve subjects participated in a sustained spatial attention paradigm while noxious laser stimuli (left hand) and noxious electrical stimuli (right hand) were sequentially delivered to the dorsum of the respective hand with nonnoxious air puffs randomly interspersed within the sequence of noxious stimuli. Participants were instructed to mentally count all stimuli (i.e., noxious and nonnoxious) applied to the attended location. Laser stimuli, presented to the attended hand (ALS), elicited larger single-trial amplitudes of the N2 component compared with unattended laser stimuli (ULS). In contrast, single-trial amplitudes of the P2 component were not significantly affected by spatial attention. Single-trial latencies of the N2 and P2 were significantly smaller for ALS vs. ULS. Additionally, the across-trial latency jitter of the N2 component was reduced for ALS. Conversely, the latency jitter of the P2 component was smaller for ULS compared with ALS. With the use of single-trial analysis, the study provided new insights into brain dynamics of LEPs related to spatial attention. Our results indicate that single-trial parameters of LEP components are differentially modulated by spatial attention. Copyright © 2015 the American Physiological Society.

Evaluation of new antiemetic agents and definition of antineoplastic agent emetogenicity--an update.

PubMed

Grunberg, Steven M; Osoba, David; Hesketh, Paul J; Gralla, Richard J; Borjeson, Sussanne; Rapoport, Bernardo L; du Bois, Andreas; Tonato, Maurizio

2005-02-01

Development of effective antiemetic therapy depends upon an understanding of both the antiemetic agents and the emetogenic challenges these agents are designed to address. New potential antiemetic agents should be studied in an orderly manner, proceeding from phase I to phase II open-label trials and then to randomized double-blind phase III trials comparing new agents and regimens to best standard therapy. Use of placebos in place of antiemetic therapy against highly or moderately emetogenic chemotherapy is unacceptable. Nausea and vomiting should be evaluated separately and for both the acute and delayed periods. Defining the emetogenicity of new antineoplastic agents is a challenge, since such data are often not reliably recorded during early drug development. A four-level classification system is proposed for emetogenicity of intravenous antineoplastic agents. A separate four-level classification system for emetogenicity of oral antineoplastic agents, which are often given over an extended period of time, is also proposed.

The International Neuroblastoma Risk Group (INRG) Classification System: An INRG Task Force Report

PubMed Central

Cohn, Susan L.; Pearson, Andrew D.J.; London, Wendy B.; Monclair, Tom; Ambros, Peter F.; Brodeur, Garrett M.; Faldum, Andreas; Hero, Barbara; Iehara, Tomoko; Machin, David; Mosseri, Veronique; Simon, Thorsten; Garaventa, Alberto; Castel, Victoria; Matthay, Katherine K.

2009-01-01

Purpose Because current approaches to risk classification and treatment stratification for children with neuroblastoma (NB) vary greatly throughout the world, it is difficult to directly compare risk-based clinical trials. The International Neuroblastoma Risk Group (INRG) classification system was developed to establish a consensus approach for pretreatment risk stratification. Patients and Methods The statistical and clinical significance of 13 potential prognostic factors were analyzed in a cohort of 8,800 children diagnosed with NB between 1990 and 2002 from North America and Australia (Children's Oncology Group), Europe (International Society of Pediatric Oncology Europe Neuroblastoma Group and German Pediatric Oncology and Hematology Group), and Japan. Survival tree regression analyses using event-free survival (EFS) as the primary end point were performed to test the prognostic significance of the 13 factors. Results Stage, age, histologic category, grade of tumor differentiation, the status of the MYCN oncogene, chromosome 11q status, and DNA ploidy were the most highly statistically significant and clinically relevant factors. A new staging system (INRG Staging System) based on clinical criteria and tumor imaging was developed for the INRG Classification System. The optimal age cutoff was determined to be between 15 and 19 months, and 18 months was selected for the classification system. Sixteen pretreatment groups were defined on the basis of clinical criteria and statistically significantly different EFS of the cohort stratified by the INRG criteria. Patients with 5-year EFS more than 85%, more than 75% to ≤ 85%, ≥ 50% to ≤ 75%, or less than 50% were classified as very low risk, low risk, intermediate risk, or high risk, respectively. Conclusion By defining homogenous pretreatment patient cohorts, the INRG classification system will greatly facilitate the comparison of risk-based clinical trials conducted in different regions of the world and the development of international collaborative studies. PMID:19047291

In the (sub)tropics allergic rhinitis and its impact on asthma classification of allergic rhinitis is more useful than perennial-seasonal classification.

PubMed

Larenas-Linnemann, Désirée; Michels, Alexandra; Dinger, Hanna; Arias-Cruz, Alfredo; Ambriz Moreno, Marichuy; Bedolla Barajas, Martin; Javier, Ruth Cerino; Cid Del Prado, Maria de la Luz; Cruz Moreno, Manuel Alejandro; Vergara, Laura Diego; García Almaráz, Roberto; García-Cobas, Cecilia Y; Garcia Imperial, Daniel Alberto; Muñoz, Rosa Garcia; Hernandez Colín, Dante; Linares Zapien, Francisco Javier; Luna Pech, Jorge Agustín; Matta Campos, Juan Jose; Martinez Jimenez, Norma; Avalos, Miguel Medina; Medina Hernandez, Alejandra; Maldonado, Albero Monteverde; López, Doris Nereida; Pizano Nazara, Luis Julian; Sanchez, Emanuel Ramirez; Ramos López, José Domingo; Rodriguez-Pérez, Noel; Rodriguez Ortiz, Pablo G; Shah-Hosseini, Kijawasch; Mösges, Ralph

2014-01-01

Two different allergic rhinitis (AR) symptom phenotype classifications exist. Treatment recommendations are based on intermittent-persistent (INT-PER) cataloging, but clinical trials still use the former seasonal AR-perennial AR (SAR-PAR) classification. This study was designed to describe how INT-PER, mild-moderate/severe and SAR-PAR of patients seen by allergists are distributed over the different climate zones in a (sub)tropical country and how these phenotypes relate to allergen sensitization patterns. Six climate zones throughout Mexico were determined, based on National Geographic Institute (Instituto Nacional de Estadística y Geografía) data. Subsequent AR patients (2-68 years old) underwent a blinded, standardized skin-prick test and filled out a validated questionnaire phenotyping AR. Five hundred twenty-nine subjects participated in this study. In the tropical zone with 87% house-dust mite sensitization, INT (80.9%; p < 0.001) and PAR (91%; p = 0.04) were more frequent than in the subtropics. In the central high-pollen areas, there was less moderate/severe AR (65.5%; p < 0.005). Frequency of comorbid asthma showed a clear north-south gradient, from 25% in the dry north to 59% in the tropics (p < 0.005). No differences exist in AR cataloging among patients with different sensitization patterns, with two minor exceptions (more PER in tree sensitized and more PAR in mold positives; p < 0.05). In a (sub)tropical country the SAR-PAR classification seems of limited value and bears poor relation with the INT-PER classification. INT is more frequent in the tropical zone. Because PER has been shown to relate to AR severity, clinical trials should select patients based on INT-PER combined with the severity cataloging because these make for a better treatment guide than SAR-PAR.

Interobserver and intraobserver variability in the identification of the Lenke classification lumbar modifier in adolescent idiopathic scoliosis.

PubMed

Duong, Luc; Cheriet, Farida; Labelle, Hubert; Cheung, Kenneth M C; Abel, Mark F; Newton, Peter O; McCall, Richard E; Lenke, Lawrence G; Stokes, Ian A F

2009-08-01

Interobserver and intraobserver reliability study for the identification of the Lenke classification lumbar modifier by a panel of experts compared with a computer algorithm. To measure the variability of the Lenke classification lumbar modifier and determine if computer assistance using 3-dimensional spine models can improve the reliability of classification. The lumbar modifier has been proposed to subclassify Lenke scoliotic curve types into A, B, and C on the basis of the relationship between the central sacral vertical line (CSVL) and the apical lumbar vertebra. Landmarks for identification of the CSVL have not been clearly defined, and the reliability of the actual CSVL position and lumbar modifier selection have never been tested independently. Therefore, the value of the lumbar modifier for curve classification remains unknown. The preoperative radiographs of 68 patients with adolescent idiopathic scoliosis presenting a Lenke type 1 curve were measured manually twice by 6 members of the Scoliosis Research Society 3-dimensional classification committee at 6 months interval. Intraobserver and interobserver reliability was quantified using the percentage of agreement and kappa statistics. In addition, the lumbar curve of all subjects was reconstructed in 3-dimension using a stereoradiographic technique and was submitted to a computer algorithm to infer the lumbar modifier according to measurements from the pedicles. Interobserver rates for the first trial showed a mean kappa value of 0.56. Second trial rates were higher with a mean kappa value of 0.64. Intraobserver rates were evaluated at a mean kappa value of 0.69. The computer algorithm was successful in identifying the lumbar curve type and was in agreement with the observers by a proportion up to 93%. Agreement between and within observers for the Lenke lumbar modifier is only moderate to substantial with manual methods. Computer assistance with 3-dimensional models of the spine has the potential to decrease this variability.

Single-Trial Normalization for Event-Related Spectral Decomposition Reduces Sensitivity to Noisy Trials

PubMed Central

Grandchamp, Romain; Delorme, Arnaud

2011-01-01

In electroencephalography, the classical event-related potential model often proves to be a limited method to study complex brain dynamics. For this reason, spectral techniques adapted from signal processing such as event-related spectral perturbation (ERSP) – and its variant event-related synchronization and event-related desynchronization – have been used over the past 20 years. They represent average spectral changes in response to a stimulus. These spectral methods do not have strong consensus for comparing pre- and post-stimulus activity. When computing ERSP, pre-stimulus baseline removal is usually performed after averaging the spectral estimate of multiple trials. Correcting the baseline of each single-trial prior to averaging spectral estimates is an alternative baseline correction method. However, we show that this method leads to positively skewed post-stimulus ERSP values. We eventually present new single-trial-based ERSP baseline correction methods that perform trial normalization or centering prior to applying classical baseline correction methods. We show that single-trial correction methods minimize the contribution of artifactual data trials with high-amplitude spectral estimates and are robust to outliers when performing statistical inference testing. We then characterize these methods in terms of their time–frequency responses and behavior compared to classical ERSP methods. PMID:21994498

Research Support for the Laboratory for Lightwave Technology

DTIC Science & Technology

1992-12-31

34 .. . ."/ 12a. DISTRIBUTION AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE UNLIMITED 13. ABSTRACT (Mawimum 200words) 4 SEE ATTACHED ABSTRACT DT I 14. SUBJECT...8217TERMS 15. NUMBER OF PAGES 16. PRICE CODE 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACT...temperature ceramic nano- phase single crystal oxides that may be produced at a high rate . The synthesis of both glasses and ceramics using novel techniques

How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters

PubMed Central

Nunez, Michael D.; Vandekerckhove, Joachim; Srinivasan, Ramesh

2016-01-01

Perceptual decision making can be accounted for by drift-diffusion models, a class of decision-making models that assume a stochastic accumulation of evidence on each trial. Fitting response time and accuracy to a drift-diffusion model produces evidence accumulation rate and non-decision time parameter estimates that reflect cognitive processes. Our goal is to elucidate the effect of attention on visual decision making. In this study, we show that measures of attention obtained from simultaneous EEG recordings can explain per-trial evidence accumulation rates and perceptual preprocessing times during a visual decision making task. Models assuming linear relationships between diffusion model parameters and EEG measures as external inputs were fit in a single step in a hierarchical Bayesian framework. The EEG measures were features of the evoked potential (EP) to the onset of a masking noise and the onset of a task-relevant signal stimulus. Single-trial evoked EEG responses, P200s to the onsets of visual noise and N200s to the onsets of visual signal, explain single-trial evidence accumulation and preprocessing times. Within-trial evidence accumulation variance was not found to be influenced by attention to the signal or noise. Single-trial measures of attention lead to better out-of-sample predictions of accuracy and correct reaction time distributions for individual subjects. PMID:28435173

How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters.

PubMed

Nunez, Michael D; Vandekerckhove, Joachim; Srinivasan, Ramesh

2017-02-01

Perceptual decision making can be accounted for by drift-diffusion models, a class of decision-making models that assume a stochastic accumulation of evidence on each trial. Fitting response time and accuracy to a drift-diffusion model produces evidence accumulation rate and non-decision time parameter estimates that reflect cognitive processes. Our goal is to elucidate the effect of attention on visual decision making. In this study, we show that measures of attention obtained from simultaneous EEG recordings can explain per-trial evidence accumulation rates and perceptual preprocessing times during a visual decision making task. Models assuming linear relationships between diffusion model parameters and EEG measures as external inputs were fit in a single step in a hierarchical Bayesian framework. The EEG measures were features of the evoked potential (EP) to the onset of a masking noise and the onset of a task-relevant signal stimulus. Single-trial evoked EEG responses, P200s to the onsets of visual noise and N200s to the onsets of visual signal, explain single-trial evidence accumulation and preprocessing times. Within-trial evidence accumulation variance was not found to be influenced by attention to the signal or noise. Single-trial measures of attention lead to better out-of-sample predictions of accuracy and correct reaction time distributions for individual subjects.

A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

NASA Astrophysics Data System (ADS)

Chang, Lan; Zhang, Mengmeng; Li, Wei

2017-10-01

A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

Combined target factor analysis and Bayesian soft-classification of interference-contaminated samples: forensic fire debris analysis.

PubMed

Williams, Mary R; Sigman, Michael E; Lewis, Jennifer; Pitan, Kelly McHugh

2012-10-10

A bayesian soft classification method combined with target factor analysis (TFA) is described and tested for the analysis of fire debris data. The method relies on analysis of the average mass spectrum across the chromatographic profile (i.e., the total ion spectrum, TIS) from multiple samples taken from a single fire scene. A library of TIS from reference ignitable liquids with assigned ASTM classification is used as the target factors in TFA. The class-conditional distributions of correlations between the target and predicted factors for each ASTM class are represented by kernel functions and analyzed by bayesian decision theory. The soft classification approach assists in assessing the probability that ignitable liquid residue from a specific ASTM E1618 class, is present in a set of samples from a single fire scene, even in the presence of unspecified background contributions from pyrolysis products. The method is demonstrated with sample data sets and then tested on laboratory-scale burn data and large-scale field test burns. The overall performance achieved in laboratory and field test of the method is approximately 80% correct classification of fire debris samples. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Classifying Values by Categories

ERIC Educational Resources Information Center

Gündüz, Mevlüt

2016-01-01

The aim of this study is to make a new classification regarding the fact that the current classifications may change constantly because of values? gaining a different dimension and importance every single day. In this research descriptive research, which was used frequently in qualitative research methods, was preferred. This research was…

Classification of spatially unresolved objects

NASA Technical Reports Server (NTRS)

Nalepka, R. F.; Horwitz, H. M.; Hyde, P. D.; Morgenstern, J. P.

1972-01-01

A proportion estimation technique for classification of multispectral scanner images is reported that uses data point averaging to extract and compute estimated proportions for a single average data point to classify spatial unresolved areas. Example extraction calculations of spectral signatures for bare soil, weeds, alfalfa, and barley prove quite accurate.

The Database for Aggregate Analysis of ClinicalTrials.gov (AACT) and Subsequent Regrouping by Clinical Specialty

PubMed Central

Tasneem, Asba; Aberle, Laura; Ananth, Hari; Chakraborty, Swati; Chiswell, Karen; McCourt, Brian J.; Pietrobon, Ricardo

2012-01-01

Background The ClinicalTrials.gov registry provides information regarding characteristics of past, current, and planned clinical studies to patients, clinicians, and researchers; in addition, registry data are available for bulk download. However, issues related to data structure, nomenclature, and changes in data collection over time present challenges to the aggregate analysis and interpretation of these data in general and to the analysis of trials according to clinical specialty in particular. Improving usability of these data could enhance the utility of ClinicalTrials.gov as a research resource. Methods/Principal Results The purpose of our project was twofold. First, we sought to extend the usability of ClinicalTrials.gov for research purposes by developing a database for aggregate analysis of ClinicalTrials.gov (AACT) that contains data from the 96,346 clinical trials registered as of September 27, 2010. Second, we developed and validated a methodology for annotating studies by clinical specialty, using a custom taxonomy employing Medical Subject Heading (MeSH) terms applied by an NLM algorithm, as well as MeSH terms and other disease condition terms provided by study sponsors. Clinical specialists reviewed and annotated MeSH and non-MeSH disease condition terms, and an algorithm was created to classify studies into clinical specialties based on both MeSH and non-MeSH annotations. False positives and false negatives were evaluated by comparing algorithmic classification with manual classification for three specialties. Conclusions/Significance The resulting AACT database features study design attributes parsed into discrete fields, integrated metadata, and an integrated MeSH thesaurus, and is available for download as Oracle extracts (.dmp file and text format). This publicly-accessible dataset will facilitate analysis of studies and permit detailed characterization and analysis of the U.S. clinical trials enterprise as a whole. In addition, the methodology we present for creating specialty datasets may facilitate other efforts to analyze studies by specialty groups. PMID:22438982

The database for aggregate analysis of ClinicalTrials.gov (AACT) and subsequent regrouping by clinical specialty.

PubMed

Tasneem, Asba; Aberle, Laura; Ananth, Hari; Chakraborty, Swati; Chiswell, Karen; McCourt, Brian J; Pietrobon, Ricardo

2012-01-01

The ClinicalTrials.gov registry provides information regarding characteristics of past, current, and planned clinical studies to patients, clinicians, and researchers; in addition, registry data are available for bulk download. However, issues related to data structure, nomenclature, and changes in data collection over time present challenges to the aggregate analysis and interpretation of these data in general and to the analysis of trials according to clinical specialty in particular. Improving usability of these data could enhance the utility of ClinicalTrials.gov as a research resource. The purpose of our project was twofold. First, we sought to extend the usability of ClinicalTrials.gov for research purposes by developing a database for aggregate analysis of ClinicalTrials.gov (AACT) that contains data from the 96,346 clinical trials registered as of September 27, 2010. Second, we developed and validated a methodology for annotating studies by clinical specialty, using a custom taxonomy employing Medical Subject Heading (MeSH) terms applied by an NLM algorithm, as well as MeSH terms and other disease condition terms provided by study sponsors. Clinical specialists reviewed and annotated MeSH and non-MeSH disease condition terms, and an algorithm was created to classify studies into clinical specialties based on both MeSH and non-MeSH annotations. False positives and false negatives were evaluated by comparing algorithmic classification with manual classification for three specialties. The resulting AACT database features study design attributes parsed into discrete fields, integrated metadata, and an integrated MeSH thesaurus, and is available for download as Oracle extracts (.dmp file and text format). This publicly-accessible dataset will facilitate analysis of studies and permit detailed characterization and analysis of the U.S. clinical trials enterprise as a whole. In addition, the methodology we present for creating specialty datasets may facilitate other efforts to analyze studies by specialty groups.

The Power of Neuroimaging Biomarkers for Screening Frontotemporal Dementia

PubMed Central

McMillan, Corey T.; Avants, Brian B.; Cook, Philip; Ungar, Lyle; Trojanowski, John Q.; Grossman, Murray

2014-01-01

Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative disease that can result from either frontotemporal lobar degeneration (FTLD) or Alzheimer’s disease (AD) pathology. It is critical to establish statistically powerful biomarkers that can achieve substantial cost-savings and increase feasibility of clinical trials. We assessed three broad categories of neuroimaging methods to screen underlying FTLD and AD pathology in a clinical FTD series: global measures (e.g., ventricular volume), anatomical volumes of interest (VOIs) (e.g., hippocampus) using a standard atlas, and data-driven VOIs using Eigenanatomy. We evaluated clinical FTD patients (N=93) with cerebrospinal fluid, gray matter (GM) MRI, and diffusion tensor imaging (DTI) to assess whether they had underlying FTLD or AD pathology. Linear regression was performed to identify the optimal VOIs for each method in a training dataset and then we evaluated classification sensitivity and specificity in an independent test cohort. Power was evaluated by calculating minimum sample sizes (mSS) required in the test classification analyses for each model. The data-driven VOI analysis using a multimodal combination of GM MRI and DTI achieved the greatest classification accuracy (89% SENSITIVE; 89% SPECIFIC) and required a lower minimum sample size (N=26) relative to anatomical VOI and global measures. We conclude that a data-driven VOI approach employing Eigenanatomy provides more accurate classification, benefits from increased statistical power in unseen datasets, and therefore provides a robust method for screening underlying pathology in FTD patients for entry into clinical trials. PMID:24687814

Property Specification Patterns for intelligence building software

NASA Astrophysics Data System (ADS)

Chun, Seungsu

2018-03-01

In this paper, through the property specification pattern research for Modal MU(μ) logical aspects present a single framework based on the pattern of intelligence building software. In this study, broken down by state property specification pattern classification of Dwyer (S) and action (A) and was subdivided into it again strong (A) and weaknesses (E). Through these means based on a hierarchical pattern classification of the property specification pattern analysis of logical aspects Mu(μ) was applied to the pattern classification of the examples used in the actual model checker. As a result, not only can a more accurate classification than the existing classification systems were easy to create and understand the attributes specified.

Animal modelling of traumatic brain injury in preclinical drug development: where do we go from here?

PubMed Central

Marklund, Niklas; Hillered, Lars

2011-01-01

Traumatic brain injury (TBI) is the leading cause of death and disability in young adults. Survivors of TBI frequently suffer from long-term personality changes and deficits in cognitive and motor performance, urgently calling for novel pharmacological treatment options. To date, all clinical trials evaluating neuroprotective compounds have failed in demonstrating clinical efficacy in cohorts of severely injured TBI patients. The purpose of the present review is to describe the utility of animal models of TBI for preclinical evaluation of pharmacological compounds. No single animal model can adequately mimic all aspects of human TBI owing to the heterogeneity of clinical TBI. To successfully develop compounds for clinical TBI, a thorough evaluation in several TBI models and injury severities is crucial. Additionally, brain pharmacokinetics and the time window must be carefully evaluated. Although the search for a single-compound, ‘silver bullet’ therapy is ongoing, a combination of drugs targeting various aspects of neuroprotection, neuroinflammation and regeneration may be needed. In summary, finding drugs and prove clinical efficacy in TBI is a major challenge ahead for the research community and the drug industry. For a successful translation of basic science knowledge to the clinic to occur we believe that a further refinement of animal models and functional outcome methods is important. In the clinical setting, improved patient classification, more homogenous patient cohorts in clinical trials, standardized treatment strategies, improved central nervous system drug delivery systems and monitoring of target drug levels and drug effects is warranted. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21175576

On the applicability of brain reading for predictive human-machine interfaces in robotics.

PubMed

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

Effects of Korean red ginseng (Panax Ginseng Meyer) on bisphenol A exposure and gynecologic complaints: single blind, randomized clinical trial of efficacy and safety.

PubMed

Yang, Mihi; Lee, Ho-Sun; Hwang, Min-Woo; Jin, Mirim

2014-07-25

Korean red ginseng (KRG) is a processed ginseng from raw ginseng to enhance safety, preservation and efficacy, known having beneficial effects on women's health due to its estrogen like function. While estrogen supplementation showed some modulation of endocrine disrupting chemicals, bisphenol A (BPA) has been focused as a potential endocrine disrupting chemical. In this study, we examined the efficacy and safety outcomes of KRG against BPA, focusing on female quality of life (QOL). Individual variations in susceptibility to KRG were also investigated with the Sasang Typology, the personalized medicine used for hundred years in Korea. We performed a single-blind randomized clinical trial. Study subjects were young women (N = 22), consumed 2.7 g of KRG or placebo per day for 2 weeks and filled up questionnaires regarding gynecologic complaints at the 4 time spots. We analyzed urinary total BPA and malondialdehyde (MDA), an oxidative stress biomarker, with GC/MS and HPLC/UVD respectively, and diagnosed their Sasang Typology with the questionnaire for the Sasang constitution Classification (QSCC II). KRG consumption decreased urinary BPA and MDA levels (ps < 0.05) and alleviated 'menstrual irregularity', 'menstrual pain', and 'constipation' (ps < 0.05). SoEum type (Lesser Yin person) among the Sasang types showed significant alleviation in insomnia, flushing, perspiration and appetite by KRG consumption, rather than other Sasang types. During the intervention, no one experienced any aggravated side effects. We suggest KRG is efficient for protection for female QOL and BPA- exposure and - related oxidative stress. However, individual variation in susceptibility to KRG should be further considered for identifying ideal therapy. KCT0000920.

Effects of Nintendo Wii™ Training on Occupational Performance, Balance, and Daily Living Activities in Children with Spastic Hemiplegic Cerebral Palsy: A Single-Blind and Randomized Trial.

PubMed

Atasavun Uysal, Songül; Baltaci, Gül

2016-10-05

This study aimed at assessing how the addition of Nintendo Wii ™ (NW) system to the traditional therapy influences occupational performance, balance, and daily living activities in children with spastic hemiplegic Cerebral Palsy (CP). The present study is a single-blind and randomized trial involving 24 children aged 6-14 years, classified as level I or II on the Gross Motor Function Classification System. The children were allocated into two groups: an intervention and a control group, and their families participated in the study. The activity performance analysis of the children was undertaken by using the Canadian Occupational Performance Measure (COPM), functional balance was measured with the Pediatric Balance Scale (PBS), and activities of daily living were assessed with Pediatric Evaluation of Disability Inventory (PEDI). Twenty-four children with CP were randomly divided into two groups: intervention (n = 12) and control group (n = 12). All children in both groups continued their traditional physiotherapy program twice a week, 45 minutes per session, whereas the participants in the intervention group, additionally, were trained with NW, two other days of the week for 12 weeks, with each session lasting for 30 minutes. Self-care, mobility, PEDI total, PBS, and performance of COPM scores increased in the NW group after intervention. Self-care, mobility, and total PEDI increased in the control group as well. However, there was no statistically significant difference found between the groups, except for PBS (P < 0.05). NW contributed to the implementation of occupational performance, daily living activities, and functional balance. We recommend that NW could be used in the rehabilitation program to engage play-based activities with fun.

On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics

PubMed Central

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors. PMID:24358125

Octanoic acid in alcohol-responsive essential tremor

PubMed Central

McCrossin, Gayle; Lungu, Codrin; Considine, Elaine; Toro, Camilo; Nahab, Fatta B.; Auh, Sungyoung; Buchwald, Peter; Grimes, George J.; Starling, Judith; Potti, Gopal; Scheider, Linda; Kalowitz, Daniel; Bowen, Daniel; Carnie, Andrea; Hallett, Mark

2013-01-01

Objective: To assess safety and efficacy of an oral, single, low dose of octanoic acid (OA) in subjects with alcohol-responsive essential tremor (ET). Methods: We conducted a double-blind, placebo-controlled, crossover, phase I/II clinical trial evaluating the effect of 4 mg/kg OA in 19 subjects with ET. The primary outcome was accelerometric postural tremor power of the dominant hand 80 minutes after administration. Secondary outcomes included digital spiral analysis, pharmacokinetic sampling, as well as safety measures. Results: OA was safe and well tolerated. Nonserious adverse events were mild (Common Terminology Criteria for Adverse Events grade 1) and equally present after OA and placebo. At the primary outcome, OA effects were not different from placebo. Secondary outcome analyses of digital spiral analysis, comparison across the entire time course in weighted and nonweighted accelerometry, as well as nondominant hand tremor power did not show a benefit of OA over placebo. The analysis of individual time points showed that OA improved tremor at 300 minutes (dominant hand, F1,16 = 5.49, p = 0.032 vs placebo), with a maximum benefit at 180 minutes after OA (both hands, F1,16 = 6.1, p = 0.025). Conclusions: Although the effects of OA and placebo at the primary outcome were not different, secondary outcome measures suggest superiority of OA in reducing tremor at later time points, warranting further trials at higher dose levels. Classification of evidence: This study provides Class I evidence that a single 4-mg/kg dose of OA is not effective in reducing postural tremor in patients with ET at a primary outcome of 80 minutes, but is effective for a secondary outcome after 180 minutes. PMID:23408867

A matter of timing: harm reduction in learned helplessness.

PubMed

Richter, Sophie Helene; Sartorius, Alexander; Gass, Peter; Vollmayr, Barbara

2014-11-03

Learned helplessness has excellent validity as an animal model for depression, but problems in reproducibility limit its use and the high degree of stress involved in the paradigm raises ethical concerns. We therefore aimed to identify which and how many trials of the learned helplessness paradigm are necessary to distinguish between helpless and non-helpless rats. A trial-by-trial reanalysis of tests from 163 rats with congenital learned helplessness or congenital non-learned helplessness and comparison of 82 rats exposed to inescapable shock with 38 shock-controls revealed that neither the first test trials, when rats showed unspecific hyperlocomotion, nor trials of the last third of the test, when almost all animals responded quickly to the stressor, contributed to sensitivity and specificity of the test. Considering only trials 3-10 improved the classification of helpless and non-helpless rats. The refined analysis allows abbreviation of the test for learned helplessness from 15 trials to 10 trials thereby reducing pain and stress of the experimental animals without losing statistical power.

Secondary mediation and regression analyses of the PTClinResNet database: determining causal relationships among the International Classification of Functioning, Disability and Health levels for four physical therapy intervention trials.

PubMed

Mulroy, Sara J; Winstein, Carolee J; Kulig, Kornelia; Beneck, George J; Fowler, Eileen G; DeMuth, Sharon K; Sullivan, Katherine J; Brown, David A; Lane, Christianne J

2011-12-01

Each of the 4 randomized clinical trials (RCTs) hosted by the Physical Therapy Clinical Research Network (PTClinResNet) targeted a different disability group (low back disorder in the Muscle-Specific Strength Training Effectiveness After Lumbar Microdiskectomy [MUSSEL] trial, chronic spinal cord injury in the Strengthening and Optimal Movements for Painful Shoulders in Chronic Spinal Cord Injury [STOMPS] trial, adult stroke in the Strength Training Effectiveness Post-Stroke [STEPS] trial, and pediatric cerebral palsy in the Pediatric Endurance and Limb Strengthening [PEDALS] trial for children with spastic diplegic cerebral palsy) and tested the effectiveness of a muscle-specific or functional activity-based intervention on primary outcomes that captured pain (STOMPS, MUSSEL) or locomotor function (STEPS, PEDALS). The focus of these secondary analyses was to determine causal relationships among outcomes across levels of the International Classification of Functioning, Disability and Health (ICF) framework for the 4 RCTs. With the database from PTClinResNet, we used 2 separate secondary statistical approaches-mediation analysis for the MUSSEL and STOMPS trials and regression analysis for the STEPS and PEDALS trials-to test relationships among muscle performance, primary outcomes (pain related and locomotor related), activity and participation measures, and overall quality of life. Predictive models were stronger for the 2 studies with pain-related primary outcomes. Change in muscle performance mediated or predicted reductions in pain for the MUSSEL and STOMPS trials and, to some extent, walking speed for the STEPS trial. Changes in primary outcome variables were significantly related to changes in activity and participation variables for all 4 trials. Improvement in activity and participation outcomes mediated or predicted increases in overall quality of life for the 3 trials with adult populations. Variables included in the statistical models were limited to those measured in the 4 RCTs. It is possible that other variables also mediated or predicted the changes in outcomes. The relatively small sample size in the PEDALS trial limited statistical power for those analyses. Evaluating the mediators or predictors of change between each ICF level and for 2 fundamentally different outcome variables (pain versus walking) provided insights into the complexities inherent across 4 prevalent disability groups.

Reliability of Single-Leg Balance and Landing Tests in Rugby Union; Prospect of Using Postural Control to Monitor Fatigue.

PubMed

Troester, Jordan C; Jasmin, Jason G; Duffield, Rob

2018-06-01

The present study examined the inter-trial (within test) and inter-test (between test) reliability of single-leg balance and single-leg landing measures performed on a force plate in professional rugby union players using commercially available software (SpartaMARS, Menlo Park, USA). Twenty-four players undertook test - re-test measures on two occasions (7 days apart) on the first training day of two respective pre-season weeks following 48h rest and similar weekly training loads. Two 20s single-leg balance trials were performed on a force plate with eyes closed. Three single-leg landing trials were performed by jumping off two feet and landing on one foot in the middle of a force plate 1m from the starting position. Single-leg balance results demonstrated acceptable inter-trial reliability (ICC = 0.60-0.81, CV = 11-13%) for sway velocity, anterior-posterior sway velocity, and mediolateral sway velocity variables. Acceptable inter-test reliability (ICC = 0.61-0.89, CV = 7-13%) was evident for all variables except mediolateral sway velocity on the dominant leg (ICC = 0.41, CV = 15%). Single-leg landing results only demonstrated acceptable inter-trial reliability for force based measures of relative peak landing force and impulse (ICC = 0.54-0.72, CV = 9-15%). Inter-test results indicate improved reliability through the averaging of three trials with force based measures again demonstrating acceptable reliability (ICC = 0.58-0.71, CV = 7-14%). Of the variables investigated here, total sway velocity and relative landing impulse are the most reliable measures of single-leg balance and landing performance, respectively. These measures should be considered for monitoring potential changes in postural control in professional rugby union.

Foraging enrichment for stabled horses: effects on behaviour and selection.

PubMed

Goodwin, D; Davidson, H P B; Harris, P

2002-11-01

The restricted access to pasture experienced by many competition horses has been linked to the exhibition of stereotypic and redirected behaviour patterns. It has been suggested that racehorses provided with more than one source of forage are less likely to perform these patterns; however, the reasons for this are currently unclear. To investigate this in 4 replicated trials, up to 12 horses were introduced into each of 2 identical stables containing a single forage, or 6 forages for 5 min. To detect novelty effects, in the first and third trials the single forage was hay. In the second and fourth, it was the preferred forage from the preceding trial. Trials were videotaped and 12 mutually exclusive behaviour patterns compared. When hay was presented as the single forage (Trials 1 and 3), all recorded behaviour patterns were significantly different between stables; e.g. during Trial 3 in the 'Single' stable, horses looked over the stable door more frequently (P<0.001), moved for longer (P<0.001), foraged on straw bedding longer (P<0.001), and exhibited behaviour indicative of motivation to search for alternative resources (P<0.001) more frequently. When a previously preferred forage was presented as the single forage (Trials 2 and 4) behaviour was also significantly different between stables, e.g in Trial 4 horses looked out over the stable door more frequently (P<0.005) and foraged for longer in their straw bedding (P<0.005). Further study is required to determine whether these effects persist over longer periods. However, these trials indicate that enrichment of the stable environment through provision of multiple forages may have welfare benefits for horses, in reducing straw consumption and facilitating the expression of highly motivated foraging behaviour.

Sleep-Disordered Breathing in Chronic SCI: A Randomized Controlled Trial of Treatment Impact on Cognition, Quality of Life, and Cardiovascular Disease

DTIC Science & Technology

2014-10-01

SCI. In this prospective randomized controlled trial, we will objectively measure sleep disordered breathing ( SDB ) in chronic SCI patients using...portable sleep studies, and systematically evaluate the association between SDB , cognitive performance, mood, pain, and CV measures. We will randomize...randomized shortly. 15. SUBJECT TERMS SDB , SCI, PAP, CV 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a

Classification Consistency and Accuracy for Complex Assessments Using Item Response Theory

ERIC Educational Resources Information Center

Lee, Won-Chan

2010-01-01

In this article, procedures are described for estimating single-administration classification consistency and accuracy indices for complex assessments using item response theory (IRT). This IRT approach was applied to real test data comprising dichotomous and polytomous items. Several different IRT model combinations were considered. Comparisons…

Conceptual Scoring and Classification Accuracy of Vocabulary Testing in Bilingual Children

ERIC Educational Resources Information Center

Anaya, Jissel B.; Peña, Elizabeth D.; Bedore, Lisa M.

2018-01-01

Purpose: This study examined the effects of single-language and conceptual scoring on the vocabulary performance of bilingual children with and without specific language impairment. We assessed classification accuracy across 3 scoring methods. Method: Participants included Spanish-English bilingual children (N = 247) aged 5;1 (years;months) to…

Use of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for improving the accuracy of the risk classification of type 1 diabetes.

PubMed

Sosenko, Jay M; Skyler, Jay S; Mahon, Jeffrey; Krischer, Jeffrey P; Greenbaum, Carla J; Rafkin, Lisa E; Beam, Craig A; Boulware, David C; Matheson, Della; Cuthbertson, David; Herold, Kevan C; Eisenbarth, George; Palmer, Jerry P

2014-04-01

OBJECTIVE We studied the utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for improving the accuracy of type 1 diabetes (T1D) risk classification in TrialNet Natural History Study (TNNHS) participants. RESEARCH DESIGN AND METHODS The cumulative incidence of T1D was compared between normoglycemic individuals with DPTRS values >7.00 and dysglycemic individuals in the TNNHS (n = 991). It was also compared between individuals with DPTRS values <7.00 or >7.00 among those with dysglycemia and those with multiple autoantibodies in the TNNHS. DPTRS values >7.00 were compared with dysglycemia for characterizing risk in Diabetes Prevention Trial-Type 1 (DPT-1) (n = 670) and TNNHS participants. The reliability of DPTRS values >7.00 was compared with dysglycemia in the TNNHS. RESULTS The cumulative incidence of T1D for normoglycemic TNNHS participants with DPTRS values >7.00 was comparable to those with dysglycemia. Among those with dysglycemia, the cumulative incidence was much higher (P < 0.001) for those with DPTRS values >7.00 than for those with values <7.00 (3-year risks: 0.16 for <7.00 and 0.46 for >7.00). Dysglycemic individuals in DPT-1 were at much higher risk for T1D than those with dysglycemia in the TNNHS (P < 0.001); there was no significant difference in risk between the studies among those with DPTRS values >7.00. The proportion in the TNNHS reverting from dysglycemia to normoglycemia at the next visit was higher than the proportion reverting from DPTRS values >7.00 to values <7.00 (36 vs. 23%). CONCLUSIONS DPTRS thresholds can improve T1D risk classification accuracy by identifying high-risk normoglycemic and low-risk dysglycemic individuals. The 7.00 DPTRS threshold characterizes risk more consistently between populations and has greater reliability than dysglycemia.

Customized oligonucleotide microarray gene expression-based classification of neuroblastoma patients outperforms current clinical risk stratification.

PubMed

Oberthuer, André; Berthold, Frank; Warnat, Patrick; Hero, Barbara; Kahlert, Yvonne; Spitz, Rüdiger; Ernestus, Karen; König, Rainer; Haas, Stefan; Eils, Roland; Schwab, Manfred; Brors, Benedikt; Westermann, Frank; Fischer, Matthias

2006-11-01

To develop a gene expression-based classifier for neuroblastoma patients that reliably predicts courses of the disease. Two hundred fifty-one neuroblastoma specimens were analyzed using a customized oligonucleotide microarray comprising 10,163 probes for transcripts with differential expression in clinical subgroups of the disease. Subsequently, the prediction analysis for microarrays (PAM) was applied to a first set of patients with maximally divergent clinical courses (n = 77). The classification accuracy was estimated by a complete 10-times-repeated 10-fold cross validation, and a 144-gene predictor was constructed from this set. This classifier's predictive power was evaluated in an independent second set (n = 174) by comparing results of the gene expression-based classification with those of risk stratification systems of current trials from Germany, Japan, and the United States. The first set of patients was accurately predicted by PAM (cross-validated accuracy, 99%). Within the second set, the PAM classifier significantly separated cohorts with distinct courses (3-year event-free survival [EFS] 0.86 +/- 0.03 [favorable; n = 115] v 0.52 +/- 0.07 [unfavorable; n = 59] and 3-year overall survival 0.99 +/- 0.01 v 0.84 +/- 0.05; both P < .0001) and separated risk groups of current neuroblastoma trials into subgroups with divergent outcome (NB2004: low-risk 3-year EFS 0.86 +/- 0.04 v 0.25 +/- 0.15, P < .0001; intermediate-risk 1.00 v 0.57 +/- 0.19, P = .018; high-risk 0.81 +/- 0.10 v 0.56 +/- 0.08, P = .06). In a multivariate Cox regression model, the PAM predictor classified patients of the second set more accurately than risk stratification of current trials from Germany, Japan, and the United States (P < .001; hazard ratio, 4.756 [95% CI, 2.544 to 8.893]). Integration of gene expression-based class prediction of neuroblastoma patients may improve risk estimation of current neuroblastoma trials.

Feature Relevance Assessment of Multispectral Airborne LIDAR Data for Tree Species Classification

NASA Astrophysics Data System (ADS)

Amiri, N.; Heurich, M.; Krzystek, P.; Skidmore, A. K.

2018-04-01

The presented experiment investigates the potential of Multispectral Laser Scanning (MLS) point clouds for single tree species classification. The basic idea is to simulate a MLS sensor by combining two different Lidar sensors providing three different wavelngthes. The available data were acquired in the summer 2016 at the same date in a leaf-on condition with an average point density of 37 points/m2. For the purpose of classification, we segmented the combined 3D point clouds consisiting of three different spectral channels into 3D clusters using Normalized Cut segmentation approach. Then, we extracted four group of features from the 3D point cloud space. Once a varity of features has been extracted, we applied forward stepwise feature selection in order to reduce the number of irrelevant or redundant features. For the classification, we used multinomial logestic regression with L1 regularization. Our study is conducted using 586 ground measured single trees from 20 sample plots in the Bavarian Forest National Park, in Germany. Due to lack of reference data for some rare species, we focused on four classes of species. The results show an improvement between 4-10 pp for the tree species classification by using MLS data in comparison to a single wavelength based approach. A cross validated (15-fold) accuracy of 0.75 can be achieved when all feature sets from three different spectral channels are used. Our results cleary indicates that the use of MLS point clouds has great potential to improve detailed forest species mapping.

A randomized, double-blind, placebo-controlled trial of simvastatin to treat Alzheimer disease

PubMed Central

Bell, K.L.; Galasko, D.; Galvin, J.E.; Thomas, R.G.; van Dyck, C.H.; Aisen, P.S.

2011-01-01

Background: Lowering cholesterol is associated with reduced CNS amyloid deposition and increased dietary cholesterol increases amyloid accumulation in animal studies. Epidemiologic data suggest that use of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) may decrease the risk of Alzheimer disease (AD) and a single-site trial suggested possible benefit in cognition with statin treatment in AD, supporting the hypothesis that statin therapy is useful in the treatment of AD. Objective: To determine if the lipid-lowering agent simvastatin slows the progression of symptoms in AD. Methods: This randomized, double-blind, placebo-controlled trial of simvastatin was conducted in individuals with mild to moderate AD and normal lipid levels. Participants were randomly assigned to receive simvastatin, 20 mg/day, for 6 weeks then 40 mg per day for the remainder of 18 months or identical placebo. The primary outcome was the rate of change in the Alzheimer's Disease Assessment Scale–cognitive portion (ADAS-Cog). Secondary outcomes measured clinical global change, cognition, function, and behavior. Results: A total of 406 individuals were randomized: 204 to simvastatin and 202 to placebo. Simvastatin lowered lipid levels but had no effect on change in ADAS-Cog score or the secondary outcome measures. There was no evidence of increased adverse events with simvastatin treatment. Conclusion: Simvastatin had no benefit on the progression of symptoms in individuals with mild to moderate AD despite significant lowering of cholesterol. Classification of evidence: This study provides Class I evidence that simvastatin 40 mg/day does not slow decline on the ADAS-Cog. PMID:21795660

Comparison of Critical Power and W' Derived From 2 or 3 Maximal Tests.

PubMed

Simpson, Len Parker; Kordi, Mehdi

2017-07-01

Typically, accessing the asymptote (critical power; CP) and curvature constant (W') parameters of the hyperbolic power-duration relationship requires multiple constant-power exhaustive-exercise trials spread over several visits. However, more recently single-visit protocols and personal power meters have been used. This study investigated the practicality of using a 2-trial, single-visit protocol in providing reliable CP and W' estimates. Eight trained cyclists underwent 3- and 12-min maximal-exercise trials in a single session to derive (2-trial) CP and W' estimates. On a separate occasion a 5-min trial was performed, providing a 3rd trial to calculate (3-trial) CP and W'. There were no differences in CP (283 ± 66 vs 282 ± 65 W) or W' (18.72 ± 6.21 vs 18.27 ± 6.29 kJ) obtained from either the 2-trial or 3-trial method, respectively. After 2 familiarization sessions (completing a 3- and a 12-min trial on both occasions), both CP and W' remained reliable over additional separate measurements. The current study demonstrates that after 2 familiarization sessions, reliable CP and W' parameters can be obtained from trained cyclists using only 2 maximal-exercise trials. These results offer practitioners a practical, time-efficient solution for incorporating power-duration testing into applied athlete support.

Single-Trial Regression Elucidates the Role of Prefrontal Theta Oscillations in Response Conflict

PubMed Central

Cohen, Michael X; Cavanagh, James F.

2011-01-01

In most cognitive neuroscience experiments there are many behavioral and experimental dynamics, and many indices of brain activity, that vary from trial to trial. For example, in studies of response conflict, conflict is usually treated as a binary variable (i.e., response conflict exists or does not in any given trial), whereas some evidence and intuition suggests that conflict may vary in intensity from trial to trial. Here we demonstrate that single-trial multiple regression of time–frequency electrophysiological activity reveals neural mechanisms of cognitive control that are not apparent in cross-trial averages. We also introduce a novel extension to oscillation phase coherence and synchronization analyses, based on “weighted” phase modulation, that has advantages over standard coherence measures in terms of linking electrophysiological dynamics to trial-varying behavior and experimental variables. After replicating previous response conflict findings using trial-averaged data, we extend these findings using single-trial analytic methods to provide novel evidence for the role of medial frontal–lateral prefrontal theta-band synchronization in conflict-induced response time dynamics, including a role for lateral prefrontal theta-band activity in biasing response times according to perceptual conflict. Given that these methods shed new light on the prefrontal mechanisms of response conflict, they are also likely to be useful for investigating other neurocognitive processes. PMID:21713190

Robot-assisted single-site compared with laparoscopic single-incision cholecystectomy for benign gallbladder disease: protocol for a randomized controlled trial.

PubMed

Grochola, Lukasz Filip; Soll, Christopher; Zehnder, Adrian; Wyss, Roland; Herzog, Pascal; Breitenstein, Stefan

2017-02-09

Recent advances in robotic technology suggest that the utilization of the da Vinci Single-Site™ platform for cholecystectomy is safe, feasible and results in a shorter learning curve compared to conventional single-incision laparoscopic cholecystectomy. Moreover, the robot-assisted technology has been shown to reduce the surgeon's stress load compared to standard single-incision laparoscopy in an experimental setup, suggesting an important advantage of the da Vinci platform. However, the above-mentioned observations are based solely on case series, case reports and experimental data, as high-quality clinical trials to demonstrate the benefits of the da Vinci Single-Site™ cholecystectomy have not been performed to date. This study addresses the question whether robot-assisted Single-Site™ cholecystectomy provides significant benefits over single-incision laparoscopic cholecystectomy in terms of surgeon's stress load, while matching the standards of the conventional single-incision approach with regard to peri- and postoperative outcomes. It is designed as a single centre, single-blinded randomized controlled trial, which compares both surgical approaches with the primary endpoint surgeon's physical and mental stress load at the time of surgery. In addition, the study aims to assess secondary endpoints such as operating time, conversion rates, additional trocar placement, intra-operative blood loss, length of hospital stay, costs of procedure, health-related quality of life, cosmesis and complications. Patients as well as ward staff are blinded until the 1 st postoperative year. Sample size calculation based on the results of a previously published experimental setup utilizing an estimated effect size of surgeon's comfort of 0.8 (power of 0.8, alpha-error level of 0.05, error margin of 10-15%) resulted in a number of 30 randomized patients per arm. The study is the first randomized controlled trial that compares the da Vinci Single Site™ platform to conventional laparoscopic approaches in cholecystectomy, one of the most frequently performed operations in general surgery. This trial is registered at clinicaltrials.gov (trial number: NCT02485392 ). Registered February 19, 2015.

Rapid acquisition of mean Raman spectra of eukaryotic cells for a robust single cell classification.

PubMed

Schie, Iwan W; Kiselev, Roman; Krafft, Christoph; Popp, Jürgen

2016-11-14

Raman spectroscopy has previously been used to identify eukaryotic and prokaryotic cells. While prokaryotic cells are small in size and can be assessed by a single Raman spectrum, the larger size of eukaryotic cells and their complex organization requires the acquisition of multiple Raman spectra to properly characterize them. A Raman spectrum from a diffraction-limited spot at an arbitrary location within a cell results in spectral variations that affect classification approaches. To probe whole cells with Raman imaging at high spatial resolution is time consuming, because a large number of Raman spectra need to be collected, resulting in low cell throughput and impairing statistical analysis due to low cell numbers. Here we propose a method to overcome the effects of cellular heterogeneity by acquiring integrated Raman spectra covering a large portion of a cell. The acquired spectrum represents the mean macromolecular composition of a cell with an exposure time that is comparable to acquisition of a single Raman spectrum. Data sets were collected from T lymphocyte Jurkat cells, and pancreatic cell lines Capan1 and MiaPaca2. Cell classification by support vector machines was compared for single spectra, spectra of images and integrated Raman spectra of cells. The integrated approach provides better and more stable prediction for individual cells, and in the current implementation, the mean macromolecular information of a cell can be acquired faster than with the acquisition of individual spectra from a comparable region. It is expected that this approach will have a major impact on the implementation of Raman based cell classification.

Single-trial detection of visual evoked potentials by common spatial patterns and wavelet filtering for brain-computer interface.

PubMed

Tu, Yiheng; Huang, Gan; Hung, Yeung Sam; Hu, Li; Hu, Yong; Zhang, Zhiguo

2013-01-01

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.

Investigation of Pharmaceutical Residues in Hospital Effluents, in Ground- and Drinking Water from Bundeswehr Facilities, and their Removal During Drinking Water Purification (Arzneimittelrueckstaende in Trinkwasser(versorgungsanlagen) und Krankenhausabwaessern der Bundeswehr: Methodenentwicklung - Verkommen - Wasseraufbereitung)

DTIC Science & Technology

1999-11-01

Drinking water processing plant , Analysis, Calculation model, Field experiment 16. PRICE CODE 17. SECURITY CLASSIFICATION 18. SECURITY CLASSIFICATION...sewage effluents and from the sewer of the municipal sewage treatment plant in Berlin-Ruhleben. In the field trials, the MDWPUs that both apply reverse...waste water samples, along the municipal sewer system and In the influents and effluents of the receiving sewage treatment plants . To estimate the

The Effects of Test Trial and Processing Level on Immediate and Delayed Retention.

PubMed

Chang, Sau Hou

2017-03-01

The purpose of the present study was to investigate the effects of test trial and processing level on immediate and delayed retention. A 2 × 2 × 2 mixed ANOVAs was used with two between-subject factors of test trial (single test, repeated test) and processing level (shallow, deep), and one within-subject factor of final recall (immediate, delayed). Seventy-six college students were randomly assigned first to the single test (studied the stimulus words three times and took one free-recall test) and the repeated test trials (studied the stimulus words once and took three consecutive free-recall tests), and then to the shallow processing level (asked whether each stimulus word was presented in capital letter or in small letter) and the deep processing level (whether each stimulus word belonged to a particular category) to study forty stimulus words. The immediate test was administered five minutes after the trials, whereas the delayed test was administered one week later. Results showed that single test trial recalled more words than repeated test trial in immediate final free-recall test, participants in deep processing performed better than those in shallow processing in both immediate and delayed retention. However, the dominance of single test trial and deep processing did not happen in delayed retention. Additional study trials did not further enhance the delayed retention of words encoded in deep processing, but did enhance the delayed retention of words encoded in shallow processing.

The Effects of Test Trial and Processing Level on Immediate and Delayed Retention

PubMed Central

Chang, Sau Hou

2017-01-01

The purpose of the present study was to investigate the effects of test trial and processing level on immediate and delayed retention. A 2 × 2 × 2 mixed ANOVAs was used with two between-subject factors of test trial (single test, repeated test) and processing level (shallow, deep), and one within-subject factor of final recall (immediate, delayed). Seventy-six college students were randomly assigned first to the single test (studied the stimulus words three times and took one free-recall test) and the repeated test trials (studied the stimulus words once and took three consecutive free-recall tests), and then to the shallow processing level (asked whether each stimulus word was presented in capital letter or in small letter) and the deep processing level (whether each stimulus word belonged to a particular category) to study forty stimulus words. The immediate test was administered five minutes after the trials, whereas the delayed test was administered one week later. Results showed that single test trial recalled more words than repeated test trial in immediate final free-recall test, participants in deep processing performed better than those in shallow processing in both immediate and delayed retention. However, the dominance of single test trial and deep processing did not happen in delayed retention. Additional study trials did not further enhance the delayed retention of words encoded in deep processing, but did enhance the delayed retention of words encoded in shallow processing. PMID:28344679

The effects of a rhythm and music-based therapy program and therapeutic riding in late recovery phase following stroke: a study protocol for a three-armed randomized controlled trial

PubMed Central

2012-01-01

Background Stroke represents one of the most costly and long-term disabling conditions in adulthood worldwide and there is a need to determine the effectiveness of rehabilitation programs in the late phase after stroke. Limited scientific support exists for training incorporating rhythm and music as well as therapeutic riding and well-designed trials to determine the effectiveness of these treatment modalities are warranted. Methods/Design A single blinded three-armed randomized controlled trial is described with the aim to evaluate whether it is possible to improve the overall health status and functioning of individuals in the late phase of stroke (1-5 years after stroke) through a rhythm and music-based therapy program or therapeutic riding. About 120 individuals will be consecutively and randomly allocated to one of three groups: (T1) rhythm and music-based therapy program; (T2) therapeutic riding; or (T3) control group receiving the T1 training program a year later. Evaluation is conducted prior to and after the 12-week long intervention as well as three and six months later. The evaluation comprises a comprehensive functional and cognitive assessment (both qualitative and quantitative), and questionnaires. Based on the International classification of functioning, disability, and health (ICF), the outcome measures are classified into six comprehensive domains, with participation as the primary outcome measure assessed by the Stroke Impact Scale (SIS, version 2.0.). The secondary outcome measures are grouped within the following domains: body function, activity, environmental factors and personal factors. Life satisfaction and health related quality of life constitute an additional domain. Current status A total of 84 participants were randomised and have completed the intervention. Recruitment proceeds and follow-up is on-going, trial results are expected in early 2014. Discussion This study will ascertain whether any of the two intervention programs can improve overall health status and functioning in the late phase of stroke. A positive outcome would increase the scientific basis for the use of such interventions in the late phase after stroke. Trial registration Clinical Trials.gov Identifier: NCT01372059 PMID:23171380

Effect of Daikenchuto (TJ-100) on gastrointestinal symptoms following laparoscopic colectomy in patients with colon cancer: study protocol for a randomized controlled trial.

PubMed

Hoshino, Nobuaki; Kawada, Kenji; Hida, Koya; Wada, Toshiaki; Takahashi, Ryo; Yoshitomi, Mami; Sakai, Yoshiharu

2017-11-21

Postoperative paralytic ileus can be a difficult complication for both surgeons and patients. Causes and treatments have been discussed for more than two centuries, but have not yet been fully resolved. Daikenchuto (TJ-100, DKT) is a traditional Japanese herbal medicine. Recently, some beneficial mechanisms of DKT to relieve paralytic ileus have been reported. DKT can suppress inflammation, increase intestinal blood flow, and accelerate bowel movements. Therefore, we have designed a randomized controlled trial to investigate the effects of DKT on postoperative gastrointestinal symptoms following laparoscopic colectomy in patients with left-sided colon cancer at a single institution. As primary endpoints, the following outcomes will be evaluated: (i) grade of abdominal pain determined using the numeric rating scale (NRS), (ii) grade of abdominal distention determined using the NRS, and (iii) quality of life determined using the Gastrointestinal Quality Life Index (GIQLI). As secondary endpoints, the following will be evaluated: (i) postoperative nutritional status (Onodera's Prognostic Nutritional Index (PNI) and the Controlling Nutritional Status score (CONUT score)), (ii) duration to initial flatus, (iii) duration to initial defecation, (iv) bowel gas volume, (v) character of stool (Bristol Stool Form Scale), (vi) defecation frequency per day, (vii) postoperative complications (Clavien-Dindo classification), (viii) length of postoperative hospital stay, and (ix) metabolites in the stool and blood. This trial is an open-label study, and needs to include 40 patients (20 patients per group) and is expected to span 2 years. To our knowledge, this is the first randomized controlled trial to investigate the effects of DKT on postoperative subjective outcomes (i.e., postoperative quality of life) following laparoscopic colectomy as primary endpoints. Exploratory metabolomics analysis of metabolites in stool and blood will be conducted in this trial, which previously has only been performed in a few human studies. The study aims to guide a future full-scale pragmatic randomized trial to assess the overall effectiveness of DKT to improve the postoperative quality of life following laparoscopic colectomy. UMIN-CTR (Japan), UMIN000023318 . Registered on 25 July 2016.

Single treatments that have lasting effects: some thoughts on the antidepressant effects of ketamine and botulinum toxin and the anxiolytic effect of psilocybin.

PubMed

Young, Simon N

2013-03-01

Recent clinical trials suggest that 3 single biological treatments have effects that persist. Based on research showing that the muscles involved in facial expressions can feed back to influence mood, a single trial diminishing glabella frown lines with botulinum toxin demonstrated a significant antidepressant effect for 16 weeks. Based primarily on research with animal models of depression suggesting that glutamate may be involved in depression, the N-methyl-D-aspartate antagonist ketamine has been tested in several trials. A single dose decreased depression for up to a week. The reported effects of the use of mushrooms containing psilocybin by a number of cultures around the world has stimulated several trials showing beneficial effects of a single dose of psilocybin for over a year in healthy people, and for up to 3 months in patients with anxiety disorders who have advanced cancer. This article discusses these studies, their rationale, their possible mechanisms of action, the future clinical research required to establish these therapies and the basic research required to optimize single treatments that have lasting effects.

Classification of change detection and change blindness from near-infrared spectroscopy signals

NASA Astrophysics Data System (ADS)

Tanaka, Hirokazu; Katura, Takusige

2011-08-01

Using a machine-learning classification algorithm applied to near-infrared spectroscopy (NIRS) signals, we classify a success (change detection) or a failure (change blindness) in detecting visual changes for a change-detection task. Five subjects perform a change-detection task, and their brain activities are continuously monitored. A support-vector-machine algorithm is applied to classify the change-detection and change-blindness trials, and correct classification probability of 70-90% is obtained for four subjects. Two types of temporal shapes in classification probabilities are found: one exhibiting a maximum value after the task is completed (postdictive type), and another exhibiting a maximum value during the task (predictive type). As for the postdictive type, the classification probability begins to increase immediately after the task completion and reaches its maximum in about the time scale of neuronal hemodynamic response, reflecting a subjective report of change detection. As for the predictive type, the classification probability shows an increase at the task initiation and is maximal while subjects are performing the task, predicting the task performance in detecting a change. We conclude that decoding change detection and change blindness from NIRS signal is possible and argue some future applications toward brain-machine interfaces.

[Accuracy improvement of spectral classification of crop using microwave backscatter data].

PubMed

Jia, Kun; Li, Qiang-Zi; Tian, Yi-Chen; Wu, Bing-Fang; Zhang, Fei-Fei; Meng, Ji-Hua

2011-02-01

In the present study, VV polarization microwave backscatter data used for improving accuracies of spectral classification of crop is investigated. Classification accuracy using different classifiers based on the fusion data of HJ satellite multi-spectral and Envisat ASAR VV backscatter data are compared. The results indicate that fusion data can take full advantage of spectral information of HJ multi-spectral data and the structure sensitivity feature of ASAR VV polarization data. The fusion data enlarges the spectral difference among different classifications and improves crop classification accuracy. The classification accuracy using fusion data can be increased by 5 percent compared to the single HJ data. Furthermore, ASAR VV polarization data is sensitive to non-agrarian area of planted field, and VV polarization data joined classification can effectively distinguish the field border. VV polarization data associating with multi-spectral data used in crop classification enlarges the application of satellite data and has the potential of spread in the domain of agriculture.

Duration of treatment for asymptomatic bacteriuria during pregnancy.

PubMed

Villar, J; Lydon-Rochelle, M T; Gülmezoglu, A M; Roganti, A

2000-01-01

A Cochrane systematic review has shown that drug treatment of asymptomatic bacteriuria in pregnant women substantially decreases the risk of pyelonephritis and reduces the risk of preterm delivery. However, it is not clear whether single dose therapy is as effective as longer conventional antibiotic treatment. The objective of this review was to assess the effects of different durations of treatment for asymptomatic bacteriuria in pregnancy. We searched the Cochrane Pregnancy and Childbirth Group trials register, the Cochrane Controlled Trials Register and the reference lists of articles. Randomised and quasi-randomised trials comparing antimicrobial therapeutic regimens that differed in duration (particularly comparing single dose with longer duration regimens) in pregnant women diagnosed with asymptomatic bacteriuria. Trial quality was assessed and data were extracted independently by the reviewers. Eight studies involving over 400 women were included. All were comparisons of single dose treatment with four to seven day treatments. The trials were generally of poor quality. No difference in 'no-cure' rate was detected between single dose and short course (4-7 day) treatment for asymptomatic bacteriuria in pregnant women (relative risk 1.13, 95% confidence interval 0.82 to 1.54) as well as in the recurrent asymptomtic bacteriuria (relative risk 1.08, 95% confidence interval 0.70 to 1.66). However these results showed significant heterogeneity. No differences were detected for preterm births and pyelonephritis although sample size of trials was small. Longer duration treatment was associated with an increase in reports of adverse effects (relative risk 0.53, 95% confidence interval 0.31 to 0.91). There is not enough evidence to evaluate whether single dose or longer duration doses are more effective in treating asymptomatic bacteriuria in pregnant women. Because single dose has lower cost and increases compliance, this comparison should be explored in a properly sized randomized controlled trial.

Accurate label-free 3-part leukocyte recognition with single cell lens-free imaging flow cytometry.

PubMed

Li, Yuqian; Cornelis, Bruno; Dusa, Alexandra; Vanmeerbeeck, Geert; Vercruysse, Dries; Sohn, Erik; Blaszkiewicz, Kamil; Prodanov, Dimiter; Schelkens, Peter; Lagae, Liesbet

2018-05-01

Three-part white blood cell differentials which are key to routine blood workups are typically performed in centralized laboratories on conventional hematology analyzers operated by highly trained staff. With the trend of developing miniaturized blood analysis tool for point-of-need in order to accelerate turnaround times and move routine blood testing away from centralized facilities on the rise, our group has developed a highly miniaturized holographic imaging system for generating lens-free images of white blood cells in suspension. Analysis and classification of its output data, constitutes the final crucial step ensuring appropriate accuracy of the system. In this work, we implement reference holographic images of single white blood cells in suspension, in order to establish an accurate ground truth to increase classification accuracy. We also automate the entire workflow for analyzing the output and demonstrate clear improvement in the accuracy of the 3-part classification. High-dimensional optical and morphological features are extracted from reconstructed digital holograms of single cells using the ground-truth images and advanced machine learning algorithms are investigated and implemented to obtain 99% classification accuracy. Representative features of the three white blood cell subtypes are selected and give comparable results, with a focus on rapid cell recognition and decreased computational cost. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Auditory “bubbles”: Efficient classification of the spectrotemporal modulations essential for speech intelligibility

PubMed Central

Venezia, Jonathan H.; Hickok, Gregory; Richards, Virginia M.

2016-01-01

Speech intelligibility depends on the integrity of spectrotemporal patterns in the signal. The current study is concerned with the speech modulation power spectrum (MPS), which is a two-dimensional representation of energy at different combinations of temporal and spectral (i.e., spectrotemporal) modulation rates. A psychophysical procedure was developed to identify the regions of the MPS that contribute to successful reception of auditory sentences. The procedure, based on the two-dimensional image classification technique known as “bubbles” (Gosselin and Schyns (2001). Vision Res. 41, 2261–2271), involves filtering (i.e., degrading) the speech signal by removing parts of the MPS at random, and relating filter patterns to observer performance (keywords identified) over a number of trials. The result is a classification image (CImg) or “perceptual map” that emphasizes regions of the MPS essential for speech intelligibility. This procedure was tested using normal-rate and 2×-time-compressed sentences. The results indicated: (a) CImgs could be reliably estimated in individual listeners in relatively few trials, (b) CImgs tracked changes in spectrotemporal modulation energy induced by time compression, though not completely, indicating that “perceptual maps” deviated from physical stimulus energy, and (c) the bubbles method captured variance in intelligibility not reflected in a common modulation-based intelligibility metric (spectrotemporal modulation index or STMI). PMID:27586738

An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making.

PubMed

French, Robert M; Glady, Yannick; Thibaut, Jean-Pierre

2017-08-01

In recent years, eyetracking has begun to be used to study the dynamics of analogy making. Numerous scanpath-comparison algorithms and machine-learning techniques are available that can be applied to the raw eyetracking data. We show how scanpath-comparison algorithms, combined with multidimensional scaling and a classification algorithm, can be used to resolve an outstanding question in analogy making-namely, whether or not children's and adults' strategies in solving analogy problems are different. (They are.) We show which of these scanpath-comparison algorithms is best suited to the kinds of analogy problems that have formed the basis of much analogy-making research over the years. Furthermore, we use machine-learning classification algorithms to examine the item-to-item saccade vectors making up these scanpaths. We show which of these algorithms best predicts, from very early on in a trial, on the basis of the frequency of various item-to-item saccades, whether a child or an adult is doing the problem. This type of analysis can also be used to predict, on the basis of the item-to-item saccade dynamics in the first third of a trial, whether or not a problem will be solved correctly.

Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI.

PubMed

Elliott, Colm; Arnold, Douglas L; Collins, D Louis; Arbel, Tal

2013-08-01

Detection of new Multiple Sclerosis (MS) lesions on magnetic resonance imaging (MRI) is important as a marker of disease activity and as a potential surrogate for relapses. We propose an approach where sequential scans are jointly segmented, to provide a temporally consistent tissue segmentation while remaining sensitive to newly appearing lesions. The method uses a two-stage classification process: 1) a Bayesian classifier provides a probabilistic brain tissue classification at each voxel of reference and follow-up scans, and 2) a random-forest based lesion-level classification provides a final identification of new lesions. Generative models are learned based on 364 scans from 95 subjects from a multi-center clinical trial. The method is evaluated on sequential brain MRI of 160 subjects from a separate multi-center clinical trial, and is compared to 1) semi-automatically generated ground truth segmentations and 2) fully manual identification of new lesions generated independently by nine expert raters on a subset of 60 subjects. For new lesions greater than 0.15 cc in size, the classifier has near perfect performance (99% sensitivity, 2% false detection rate), as compared to ground truth. The proposed method was also shown to exceed the performance of any one of the nine expert manual identifications.

Sleep-Disordered Breathing in Chronic SCI: A Randomized Controlled Trial of Treatment Impact on Cognition, Quality of Life, and Cardiovascular Disease

DTIC Science & Technology

2015-10-01

randomized controlled trial, we will objectively measure sleep disordered breathing ( SDB ) in chronic SCI patients using portable sleep studies, and...systematically evaluate the association between SDB , cognitive performance, mood, pain, and CV measures. We will randomize participants to 4 months of PAP...TERMS SDB , SCI, PAP, CV 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON USAMRMC a

Sleep Disordered Breathing in Chronic SCI: A Randomized Controlled Trial of Treatment Impact on Cognition, Quality of Life, and Cardiovascular Disease

DTIC Science & Technology

2015-11-30

randomized controlled trial, we will objectively measure sleep disordered breathing ( SDB ) in chronic SCI patients using portable sleep studies, and...systematically evaluate the association between SDB , cognitive performance, mood, pain, and CV measures. We will randomize participants to 4 months of PAP...TERMS SDB , SCI, PAP, CV 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON USAMRMC a

Fire severity classification: Uses and abuses

Treesearch

Theresa B. Jain; Russell T. Graham

2003-01-01

Burn severity (also referred to as fire severity) is not a single definition, but rather a concept and its classification is a function of the measured units unique to the system of interest. The systems include: flora and fauna, soil microbiology and hydrologic processes, atmospheric inputs, fire management, and society. Depending on the particular system of interest...

Highly Accurate Classification of Watson-Crick Basepairs on Termini of Single DNA Molecules

PubMed Central

Winters-Hilt, Stephen; Vercoutere, Wenonah; DeGuzman, Veronica S.; Deamer, David; Akeson, Mark; Haussler, David

2003-01-01

We introduce a computational method for classification of individual DNA molecules measured by an α-hemolysin channel detector. We show classification with better than 99% accuracy for DNA hairpin molecules that differ only in their terminal Watson-Crick basepairs. Signal classification was done in silico to establish performance metrics (i.e., where train and test data were of known type, via single-species data files). It was then performed in solution to assay real mixtures of DNA hairpins. Hidden Markov Models (HMMs) were used with Expectation/Maximization for denoising and for associating a feature vector with the ionic current blockade of the DNA molecule. Support Vector Machines (SVMs) were used as discriminators, and were the focus of off-line training. A multiclass SVM architecture was designed to place less discriminatory load on weaker discriminators, and novel SVM kernels were used to boost discrimination strength. The tuning on HMMs and SVMs enabled biophysical analysis of the captured molecule states and state transitions; structure revealed in the biophysical analysis was used for better feature selection. PMID:12547778

Classifying psychosis--challenges and opportunities.

PubMed

Gaebel, Wolfgang; Zielasek, Jürgen; Cleveland, Helen-Rose

2012-12-01

Within the efforts to revise ICD-10 and DSM-IV-TR, work groups on the classification of psychotic disorders appointed by the World Health Organization (WHO) and the American Psychiatric Association (APA) have proposed several changes to the corresponding classification criteria of schizophrenia and other psychotic disorders in order to increase the clinical utility, reliability and validity of these diagnoses. These proposed revisions are subject to field trials with the objective of studying whether they will lead to an improvement of the classification systems in comparison to their previous versions. Both a challenge and an opportunity, the APA and WHO have also considered harmonizing between the two classifications. The current status of both suggests that this goal can only be met in part. The main proposed revisions include changes to the number and types of symptoms of schizophrenia, the replacement of existing schizophrenia subtypes with dimensional assessments or symptom specifiers, different modifications of the criteria for schizoaffective disorder, a reorganization of the delusional disorders and the acute and transient psychotic disorders in ICD-11, as well as the revision of course and psychomotor symptoms/catatonia specifiers in both classification systems.

Classification of Clinical Research Study Eligibility Criteria to Support Multi-Stage Cohort Identification Using Clinical Data Repositories.

PubMed

Cimino, James J; Lancaster, William J; Wyatt, Mathew C

2017-01-01

One of the challenges to using electronic health record (EHR) repositories for research is the difficulty mapping study subject eligibility criteria to the query capabilities of the repository. We sought to characterize criteria as "easy" (searchable in a typical repository), "hard" (requiring manual review of the record data), and "impossible" (not typically available in EHR repositories). We obtained 292 criteria from 20 studies available from Clinical Trials.gov and rated them according to our three types, plus a fourth "mixed" type. We had good agreement among three independent reviewers and chose 274 criteria that were characterized by single types for further analysis. The resulting analysis showed typical features of criteria that do and don't map to repositories. We propose that these features be used to guide researchers in specifying eligibility criteria to improve development of enrollment workflow, including the definition of EHR repository queries for self-service or analyst-mediated retrievals.

An investigation of the time course of category congruence and priming distance effects in number classification tasks.

PubMed

Perry, Jason R; Lupker, Stephen J

2012-09-01

The issue investigated in the present research is the nature of the information that is responsible for producing masked priming effects (e.g., semantic information or stimulus-response [S-R] associations) when responding to number stimuli. This issue was addressed by assessing both the magnitude of the category congruence (priming) effect and the nature of the priming distance effect across trials using single-digit primes and targets. Participants made either magnitude (i.e., whether the number presented was larger or smaller than 5) or identification (i.e., press the left button if the number was either a 1, 2, 3, or 4 or the right button if the number was either a 6, 7, 8, or 9) judgments. The results indicated that, regardless of task instruction, there was a clear priming distance effect and a significantly increasing category congruence effect. These results indicated that both semantic activation and S-R associations play important roles in producing masked priming effects.

A novel technique for phase synchrony measurement from the complex motor imaginary potential of combined body and limb action

NASA Astrophysics Data System (ADS)

Zhou, Zhong-xing; Wan, Bai-kun; Ming, Dong; Qi, Hong-zhi

2010-08-01

In this study, we proposed and evaluated the use of the empirical mode decomposition (EMD) technique combined with phase synchronization analysis to investigate the human brain synchrony of the supplementary motor area (SMA) and primary motor area (M1) during complex motor imagination of combined body and limb action. We separated the EEG data of the SMA and M1 into intrinsic mode functions (IMFs) using the EMD method and determined the characteristic IMFs by power spectral density (PSD) analysis. Thereafter, the instantaneous phases of the characteristic IMFs were obtained by the Hilbert transformation, and the single-trial phase-locking value (PLV) features for brain synchrony measurement between the SMA and M1 were investigated separately. The classification performance suggests that the proposed approach is effective for phase synchronization analysis and is promising for the application of a brain-computer interface in motor nerve reconstruction of the lower limbs.

Efficacy of Occupational Therapy Using Ayres Sensory Integration®: A Systematic Review.

PubMed

Schaaf, Roseann C; Dumont, Rachel L; Arbesman, Marian; May-Benson, Teresa A

This systematic review addresses the question "What is the efficacy of occupational therapy using Ayres Sensory Integration ® (ASI) to support functioning and participation as defined by the International Classification of Functioning, Disability and Health for persons with challenges in processing and integrating sensory information that interfere with everyday life participation?" Three randomized controlled trials, 1 retroactive analysis, and 1 single-subject ABA design published from 2007 to 2015, all of which happened to study children with autism, met inclusion criteria. The evidence is strong that ASI intervention demonstrates positive outcomes for improving individually generated goals of functioning and participation as measured by Goal Attainment Scaling for children with autism. Moderate evidence supported improvements in impairment-level outcomes of improvement in autistic behaviors and skills-based outcomes of reduction in caregiver assistance with self-care activities. Child outcomes in play, sensory-motor, and language skills and reduced caregiver assistance with social skills had emerging but insufficient evidence. Copyright © 2018 by the American Occupational Therapy Association, Inc.

Theta Oscillations Rapidly Convey Odor-Specific Content in Human Piriform Cortex.

PubMed

Jiang, Heidi; Schuele, Stephan; Rosenow, Joshua; Zelano, Christina; Parvizi, Josef; Tao, James X; Wu, Shasha; Gottfried, Jay A

2017-04-05

Olfactory oscillations are pervasive throughout vertebrate and invertebrate nervous systems. Such observations have long implied that rhythmic activity patterns play a fundamental role in odor coding. Using intracranial EEG recordings from rare patients with medically resistant epilepsy, we find that theta oscillations are a distinct electrophysiological signature of olfactory processing in the human brain. Across seven patients, odor stimulation enhanced theta power in human piriform cortex, with robust effects at the level of single trials. Importantly, classification analysis revealed that piriform oscillatory activity conveys olfactory-specific information that can be decoded within 110-518 ms of a sniff, and maximally within the theta frequency band. This temporal window was also associated with increased theta-specific phase coupling between piriform cortex and hippocampus. Together these findings suggest that human piriform cortex has access to olfactory content in the time-frequency domain and can utilize these signals to rapidly differentiate odor stimuli. Copyright © 2017 Elsevier Inc. All rights reserved.

[A research on real-time ventricular QRS classification methods for single-chip-microcomputers].

PubMed

Peng, L; Yang, Z; Li, L; Chen, H; Chen, E; Lin, J

1997-05-01

Ventricular QRS classification is key technique of ventricular arrhythmias detection in single-chip-microcomputer based dynamic electrocardiogram real-time analyser. This paper adopts morphological feature vector including QRS amplitude, interval information to reveal QRS morphology. After studying the distribution of QRS morphology feature vector of MIT/BIH DB ventricular arrhythmia files, we use morphological feature vector cluster to classify multi-morphology QRS. Based on the method, morphological feature parameters changing method which is suitable to catch occasional ventricular arrhythmias is presented. Clinical experiments verify missed ventricular arrhythmia is less than 1% by this method.

Inclusion and definition of acute renal dysfunction in critically ill patients in randomized controlled trials: a systematic review.

PubMed

da Hora Passos, Rogerio; Ramos, Joao Gabriel Rosa; Gobatto, André; Caldas, Juliana; Macedo, Etienne; Batista, Paulo Benigno

2018-04-24

In evidence-based medicine, multicenter, prospective, randomized controlled trials (RCTs) are the gold standard for evaluating treatment benefits and ensuring the effectiveness of interventions. Patient-centered outcomes, such as mortality, are most often the preferred evaluated outcomes. While there is currently agreement on how to classify renal dysfunction in critically ill patients , the application frequency of this new classification system in RCTs has not previously been evaluated. In this study, we aim to assess the definition of renal dysfunction in multicenter RCTs involving critically ill patients that included mortality as a primary endpoint. A comprehensive search was conducted for publications reporting multicenter randomized controlled trials (RCTs) involving adult patients in intensive care units (ICUs) that included mortality as a primary outcome. MEDLINE and PUBMED were queried for relevant articles in core clinical journals published between May 2004 and December 2017. Of 418 articles reviewed, 46 multicenter RCTs with a primary endpoint related to mortality were included. Thirty-six (78.3%) of the trial reports provided information on renal function in the participants. Only seven articles (15.2%) included mean or median serum creatinine levels, mean creatinine clearance or estimated glomerular filtration rates. Sequential organ failure assessment (SOFA) score was the most commonly used definition of renal dysfunction (20 studies; 43.5%). Risk, Injury, Failure, Loss, End-stage renal disease (RIFLE), Acute Kidney Injury Network (AKIN) and Kidney Disease Improving Global Outcomes (KDIGO) criteria were used in five (10.9%) trials. In thirteen trials (28.3%), no renal dysfunction criteria were reported. Only one trial excluded patients with renal dysfunction, and it used urinary output or need for renal replacement therapy (RRT) as criteria for this diagnosis. The presence of renal dysfunction was included as a baseline patient characteristic in most RCTs. The RIFLE, AKIN and KDIGO classification systems were infrequently used; renal dysfunction was generally defined using the SOFA score.

Analysis and visualization of single-trial event-related potentials

NASA Technical Reports Server (NTRS)

Jung, T. P.; Makeig, S.; Westerfield, M.; Townsend, J.; Courchesne, E.; Sejnowski, T. J.

2001-01-01

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Copyright 2001 Wiley-Liss, Inc.

Single-visit or multiple-visit root canal treatment: systematic review, meta-analysis and trial sequential analysis

PubMed Central

Schwendicke, Falk; Göstemeyer, Gerd

2017-01-01

Objectives Single-visit root canal treatment has some advantages over conventional multivisit treatment, but might increase the risk of complications. We systematically evaluated the risk of complications after single-visit or multiple-visit root canal treatment using meta-analysis and trial-sequential analysis. Data Controlled trials comparing single-visit versus multiple-visit root canal treatment of permanent teeth were included. Trials needed to assess the risk of long-term complications (pain, infection, new/persisting/increasing periapical lesions ≥1 year after treatment), short-term pain or flare-up (acute exacerbation of initiation or continuation of root canal treatment). Sources Electronic databases (PubMed, EMBASE, Cochrane Central) were screened, random-effects meta-analyses performed and trial-sequential analysis used to control for risk of random errors. Evidence was graded according to GRADE. Study selection 29 trials (4341 patients) were included, all but 6 showing high risk of bias. Based on 10 trials (1257 teeth), risk of complications was not significantly different in single-visit versus multiple-visit treatment (risk ratio (RR) 1.00 (95% CI 0.75 to 1.35); weak evidence). Based on 20 studies (3008 teeth), risk of pain did not significantly differ between treatments (RR 0.99 (95% CI 0.76 to 1.30); moderate evidence). Risk of flare-up was recorded by 8 studies (1110 teeth) and was significantly higher after single-visit versus multiple-visit treatment (RR 2.13 (95% CI 1.16 to 3.89); very weak evidence). Trial-sequential analysis revealed that firm evidence for benefit, harm or futility was not reached for any of the outcomes. Conclusions There is insufficient evidence to rule out whether important differences between both strategies exist. Clinical significance Dentists can provide root canal treatment in 1 or multiple visits. Given the possibly increased risk of flare-ups, multiple-visit treatment might be preferred for certain teeth (eg, those with periapical lesions). PMID:28148534

Duration of treatment for asymptomatic bacteriuria during pregnancy.

PubMed

Widmer, Mariana; Gülmezoglu, A Metin; Mignini, Luciano; Roganti, Ariel

2011-12-07

A Cochrane systematic review has shown that drug treatment of asymptomatic bacteriuria in pregnant women substantially decreases the risk of pyelonephritis and reduces the risk of preterm delivery. However, it is not clear whether single-dose therapy is as effective as longer conventional antibiotic treatment. To assess the effects of different durations of treatment for asymptomatic bacteriuria in pregnancy. We searched the Cochrane Pregnancy and Childbirth Group's Trials Register (31 August 2011) and reference lists of identified articles. Randomized and quasi-randomized trials comparing antimicrobial therapeutic regimens that differed in duration (particularly comparing single dose with longer duration regimens) in pregnant women diagnosed with asymptomatic bacteriuria. We assessed trial quality and extracted data independently. We included 13 studies, involving 1622 women. All were comparisons of single-dose treatment with four- to seven-day treatments. The trials were generally of limited quality. The 'no cure rate' for asymptomatic bacteriuria in pregnant women was slightly higher for the single-dose than for the short-course treatment; however, these results were not statistically significant and showed heterogeneity. When comparing the trials that used the same antibiotic in both treatment and control groups with the trials that used different antibiotics in both groups, the 'no cure rate' risk ratio was similar. There was no statistically significant difference in the recurrence of asymptomatic bacteriuria rate between treatment and control groups. Slight differences were detected for preterm births and pyelonephritis although, apart from one trial, the sample size of the trials was inadequate. Single-dose treatment was associated with a decrease in reports of 'any side-effects' . Single-dose regimen of antibiotics may be less effective than the seven-day regimen. Women with asymptomatic bacteriuria in pregnancy should be treated by the standard regimen of antibiotics until more data become available testing seven-day compared with three- or five-day regimens.

Aggregation of Sentinel-2 time series classifications as a solution for multitemporal analysis

NASA Astrophysics Data System (ADS)

Lewiński, Stanislaw; Nowakowski, Artur; Malinowski, Radek; Rybicki, Marcin; Kukawska, Ewa; Krupiński, Michał

2017-10-01

The general aim of this work was to elaborate efficient and reliable aggregation method that could be used for creating a land cover map at a global scale from multitemporal satellite imagery. The study described in this paper presents methods for combining results of land cover/land use classifications performed on single-date Sentinel-2 images acquired at different time periods. For that purpose different aggregation methods were proposed and tested on study sites spread on different continents. The initial classifications were performed with Random Forest classifier on individual Sentinel-2 images from a time series. In the following step the resulting land cover maps were aggregated pixel by pixel using three different combinations of information on the number of occurrences of a certain land cover class within a time series and the posterior probability of particular classes resulting from the Random Forest classification. From the proposed methods two are shown superior and in most cases were able to reach or outperform the accuracy of the best individual classifications of single-date images. Moreover, the aggregations results are very stable when used on data with varying cloudiness. They also enable to reduce considerably the number of cloudy pixels in the resulting land cover map what is significant advantage for mapping areas with frequent cloud coverage.

Use of collateral information to improve LANDSAT classification accuracies

NASA Technical Reports Server (NTRS)

Strahler, A. H. (Principal Investigator)

1981-01-01

Methods to improve LANDSAT classification accuracies were investigated including: (1) the use of prior probabilities in maximum likelihood classification as a methodology to integrate discrete collateral data with continuously measured image density variables; (2) the use of the logit classifier as an alternative to multivariate normal classification that permits mixing both continuous and categorical variables in a single model and fits empirical distributions of observations more closely than the multivariate normal density function; and (3) the use of collateral data in a geographic information system as exercised to model a desired output information layer as a function of input layers of raster format collateral and image data base layers.

Drug related webpages classification using images and text information based on multi-kernel learning

NASA Astrophysics Data System (ADS)

Hu, Ruiguang; Xiao, Liping; Zheng, Wenjuan

2015-12-01

In this paper, multi-kernel learning(MKL) is used for drug-related webpages classification. First, body text and image-label text are extracted through HTML parsing, and valid images are chosen by the FOCARSS algorithm. Second, text based BOW model is used to generate text representation, and image-based BOW model is used to generate images representation. Last, text and images representation are fused with a few methods. Experimental results demonstrate that the classification accuracy of MKL is higher than those of all other fusion methods in decision level and feature level, and much higher than the accuracy of single-modal classification.

Adherence in single-parent households in a long-term asthma clinical trial.

PubMed

Spicher, Mary; Bollers, Nancy; Chinn, Tamara; Hall, Anita; Plunkett, Anne; Rodgers, Denise; Sundström, D A; Wilson, Laura

2012-01-01

Adherence of participants in a long-term clinical trial is necessary to assure validity of findings. This article examines adherence differences between single-parent and two-parent families in the Childhood Asthma Management Program (CAMP). Adherence was defined as the percentage of completed daily diary cards and scheduled study visits during the course of the trial. Logistic regression and ordinal logistic regression analyses were used. Children from single-parent families had a lower percentage of completed diary cards (72% vs. 84%) than two-parent families. Single-parent families were also more likely to reschedule visits (62% vs. 45%) and miss more clinic visits (23% vs. 17%) than two-parent families. Suggestions are given for study coordinators to assist participants in completing a long-term clinical trial. Many suggestions may be adapted for nurses in inpatient or outpatient settings for assisting parents of patients with chronic diseases.

Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating

NASA Astrophysics Data System (ADS)

Matikainen, Leena; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Puttonen, Eetu; Ahokas, Eero

2017-06-01

During the last 20 years, airborne laser scanning (ALS), often combined with passive multispectral information from aerial images, has shown its high feasibility for automated mapping processes. The main benefits have been achieved in the mapping of elevated objects such as buildings and trees. Recently, the first multispectral airborne laser scanners have been launched, and active multispectral information is for the first time available for 3D ALS point clouds from a single sensor. This article discusses the potential of this new technology in map updating, especially in automated object-based land cover classification and change detection in a suburban area. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from an object-based random forests analysis suggest that the multispectral ALS data are very useful for land cover classification, considering both elevated classes and ground-level classes. The overall accuracy of the land cover classification results with six classes was 96% compared with validation points. The classes under study included building, tree, asphalt, gravel, rocky area and low vegetation. Compared to classification of single-channel data, the main improvements were achieved for ground-level classes. According to feature importance analyses, multispectral intensity features based on several channels were more useful than those based on one channel. Automatic change detection for buildings and roads was also demonstrated by utilising the new multispectral ALS data in combination with old map vectors. In change detection of buildings, an old digital surface model (DSM) based on single-channel ALS data was also used. Overall, our analyses suggest that the new data have high potential for further increasing the automation level in mapping. Unlike passive aerial imaging commonly used in mapping, the multispectral ALS technology is independent of external illumination conditions, and there are no shadows on intensity images produced from the data. These are significant advantages in developing automated classification and change detection procedures.

Phenotype classification of single cells using SRS microscopy, RNA sequencing, and microfluidics (Conference Presentation)

NASA Astrophysics Data System (ADS)

Streets, Aaron M.; Cao, Chen; Zhang, Xiannian; Huang, Yanyi

2016-03-01

Phenotype classification of single cells reveals biological variation that is masked in ensemble measurement. This heterogeneity is found in gene and protein expression as well as in cell morphology. Many techniques are available to probe phenotypic heterogeneity at the single cell level, for example quantitative imaging and single-cell RNA sequencing, but it is difficult to perform multiple assays on the same single cell. In order to directly track correlation between morphology and gene expression at the single cell level, we developed a microfluidic platform for quantitative coherent Raman imaging and immediate RNA sequencing (RNA-Seq) of single cells. With this device we actively sort and trap cells for analysis with stimulated Raman scattering microscopy (SRS). The cells are then processed in parallel pipelines for lysis, and preparation of cDNA for high-throughput transcriptome sequencing. SRS microscopy offers three-dimensional imaging with chemical specificity for quantitative analysis of protein and lipid distribution in single cells. Meanwhile, the microfluidic platform facilitates single-cell manipulation, minimizes contamination, and furthermore, provides improved RNA-Seq detection sensitivity and measurement precision, which is necessary for differentiating biological variability from technical noise. By combining coherent Raman microscopy with RNA sequencing, we can better understand the relationship between cellular morphology and gene expression at the single-cell level.

A neuromorphic network for generic multivariate data classification

PubMed Central

Schmuker, Michael; Pfeil, Thomas; Nawrot, Martin Paul

2014-01-01

Computational neuroscience has uncovered a number of computational principles used by nervous systems. At the same time, neuromorphic hardware has matured to a state where fast silicon implementations of complex neural networks have become feasible. En route to future technical applications of neuromorphic computing the current challenge lies in the identification and implementation of functional brain algorithms. Taking inspiration from the olfactory system of insects, we constructed a spiking neural network for the classification of multivariate data, a common problem in signal and data analysis. In this model, real-valued multivariate data are converted into spike trains using “virtual receptors” (VRs). Their output is processed by lateral inhibition and drives a winner-take-all circuit that supports supervised learning. VRs are conveniently implemented in software, whereas the lateral inhibition and classification stages run on accelerated neuromorphic hardware. When trained and tested on real-world datasets, we find that the classification performance is on par with a naïve Bayes classifier. An analysis of the network dynamics shows that stable decisions in output neuron populations are reached within less than 100 ms of biological time, matching the time-to-decision reported for the insect nervous system. Through leveraging a population code, the network tolerates the variability of neuronal transfer functions and trial-to-trial variation that is inevitably present on the hardware system. Our work provides a proof of principle for the successful implementation of a functional spiking neural network on a configurable neuromorphic hardware system that can readily be applied to real-world computing problems. PMID:24469794

Inter-reader reproducibility of dynamic contrast-enhanced magnetic resonance imaging in patients with non-small cell lung cancer treated with bevacizumab and erlotinib.

PubMed

van den Boogaart, Vivian E M; de Lussanet, Quido G; Houben, Ruud M A; de Ruysscher, Dirk; Groen, Harry J M; Marcus, J Tim; Smit, Egbert F; Dingemans, Anne-Marie C; Backes, Walter H

2016-03-01

Objectives When evaluating anti-tumor treatment response by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) it is necessary to assure its validity and reproducibility. This has not been well addressed in lung tumors. Therefore we have evaluated the inter-reader reproducibility of response classification by DCE-MRI in patients with non-small cell lung cancer (NSCLC) treated with bevacizumab and erlotinib enrolled in a multicenter trial. Twenty-one patients were scanned before and 3 weeks after start of treatment with DCE-MRI in a multicenter trial. The scans were evaluated by two independent readers. The primary lung tumor was used for response assessment. Responses were assessed in terms of relative changes in tumor mean trans endothelial transfer rate (K(trans)) and its heterogeneity in terms of the spatial standard deviation. Reproducibility was expressed by the inter-reader variability, intra-class correlation coefficient (ICC) and dichotomous response classification. The inter-reader variability and ICC for the relative K(trans) were 5.8% and 0.930, respectively. For tumor heterogeneity the inter-reader variability and ICC were 0.017 and 0.656, respectively. For the two readers the response classification for relative K(trans) was concordant in 20 of 21 patients (k=0.90, p<0.0001) and for tumor heterogeneity in 19 of 21 patients (k=0.80, p<0.0001). Strong agreement was seen with regard to the inter-reader variability and reproducibility of response classification by the two readers of lung cancer DCE-MRI scans. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Antenatal breastfeeding education for increasing breastfeeding duration

PubMed Central

Lumbiganon, Pisake; Martis, Ruth; Laopaiboon, Malinee; Festin, Mario R; Ho, Jacqueline J; Hakimi, Mohammad

2014-01-01

Background Breastfeeding (BF) is well recognised as the best food for infants. The impact of antenatal BF education on the duration of BF has not been evaluated. Objectives To evaluate the effectiveness of antenatal BF education for increasing BF initiation and duration. Search methods We searched the Cochrane Pregnancy and Childbirth Group’s Trials Register (21 April 2010), CENTRAL (The Cochrane Library 2010, Issue 2), MEDLINE (1966 to April 2010) and SCOPUS (January 1985 to April 2010). We contacted experts and searched reference lists of retrieved articles. We updated the search of the Pregnancy and Childbirth Group’s Trials Register on 28 September 2011 and added the results to the awaiting classification section of the review. Selection criteria All identified published, unpublished and ongoing randomised controlled trials (RCTs) assessing the effect of formal antenatal BF education or comparing two different methods of formal antenatal BF education, on duration of BF. We excluded RCTs that also included intrapartum or postpartum BF education. Data collection and analysis We assessed all potential studies identified as a result of the search strategy. Two review authors extracted data from each included study using the agreed form and assessed risk of bias. We resolved discrepancies through discussion. Main results We included 17 studies with 7131 women in the review and 14 studies involving 6932 women contributed data to the analyses. We did not do any meta-analysis because there was only one study for each comparison. Five studies compared a single method of BF education with routine care. Peer counselling significantly increased BF initiation. Three studies compared one form of BF education versus another. No intervention was significantly more effective than another intervention in increasing initiation or duration of BF. Seven studies compared multiple methods versus a single method of BF education. Combined BF educational interventions were not significantly better than a single intervention in initiating or increasing BF duration. However, in one trial a combined BF education significantly reduced nipple pain and trauma. One study compared different combinations of interventions. There was a marginally significant increase in exclusive BF at six months in women receiving a booklet plus video plus lactation consultation (LC) compared with the booklet plus video only. Two studies compared multiple methods of BF education versus routine care. The combination of BF booklet plus video plus LC was significantly better than routine care for exclusive BF at three months. Authors’ conclusions Because there were significant methodological limitations and the observed effect sizes were small, it is not appropriate to recommend any antenatal BF education. There is an urgent need to conduct RCTs study with adequate power to evaluate the effectiveness of antenatal BF education. PMID:22071830

Diagnostic Criteria, Classification and Treatment Goals in Multiple Sclerosis: The Chronicles of Time and Space.

PubMed

Ntranos, Achilles; Lublin, Fred

2016-10-01

Multiple sclerosis (MS) is one of the most diverse human diseases. Since its first description by Charcot in the nineteenth century, the diagnostic criteria, clinical course classification, and treatment goals for MS have been constantly revised and updated to improve diagnostic accuracy, physician communication, and clinical trial design. These changes have improved the clinical outcomes and quality of life for patients with the disease. Recent technological and research breakthroughs will almost certainly further change how we diagnose, classify, and treat MS in the future. In this review, we summarize the key events in the history of MS, explain the reasoning behind the current criteria for MS diagnosis, classification, and treatment, and provide suggestions for further improvements that will keep enhancing the clinical practice of MS.

Field trial of applicability of lot quality assurance sampling survey method for rapid assessment of prevalence of active trachoma.

PubMed Central

Myatt, Mark; Limburg, Hans; Minassian, Darwin; Katyola, Damson

2003-01-01

OBJECTIVE: To test the applicability of lot quality assurance sampling (LQAS) for the rapid assessment of the prevalence of active trachoma. METHODS: Prevalence of active trachoma in six communities was found by examining all children aged 2-5 years. Trial surveys were conducted in these communities. A sampling plan appropriate for classifying communities with prevalences < or =20% and > or =40% was applied to the survey data. Operating characteristic and average sample number curves were plotted, and screening test indices were calculated. The ability of LQAS to provide a three-class classification system was investigated. FINDINGS: Ninety-six trial surveys were conducted. All communities with prevalences < or =20% and > or =40% were identified correctly. The method discriminated between communities with prevalences < or =30% and >30%, with sensitivity of 98% (95% confidence interval (CI)=88.2-99.9%), specificity of 84.4% (CI=69.9-93.0%), positive predictive value of 87.7% (CI=75.7-94.5%), negative predictive value of 97.4% (CI=84.9-99.9%), and accuracy of 91.7% (CI=83.8-96.1%). Agreement between the three prevalence classes and survey classifications was 84.4% (CI=75.2-90.7%). The time needed to complete the surveys was consistent with the need to complete a survey in one day. CONCLUSION: Lot quality assurance sampling provides a method of classifying communities according to the prevalence of active trachoma. It merits serious consideration as a replacement for the assessment of the prevalence of active trachoma with the currently used trachoma rapid assessment method. It may be extended to provide a multi-class classification method. PMID:14997240

Classification Accuracy Increase Using Multisensor Data Fusion

NASA Astrophysics Data System (ADS)

Makarau, A.; Palubinskas, G.; Reinartz, P.

2011-09-01

The practical use of very high resolution visible and near-infrared (VNIR) data is still growing (IKONOS, Quickbird, GeoEye-1, etc.) but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use of classification products. Employment of hyperspectral data is another solution, but their low spatial resolution (comparing to multispectral data) restrict their usage for many applications. Another improvement can be achieved by fusion approaches of multisensory data since this may increase the quality of scene classification. Integration of Synthetic Aperture Radar (SAR) and optical data is widely performed for automatic classification, interpretation, and change detection. In this paper we present an approach for very high resolution SAR and multispectral data fusion for automatic classification in urban areas. Single polarization TerraSAR-X (SpotLight mode) and multispectral data are integrated using the INFOFUSE framework, consisting of feature extraction (information fission), unsupervised clustering (data representation on a finite domain and dimensionality reduction), and data aggregation (Bayesian or neural network). This framework allows a relevant way of multisource data combination following consensus theory. The classification is not influenced by the limitations of dimensionality, and the calculation complexity primarily depends on the step of dimensionality reduction. Fusion of single polarization TerraSAR-X, WorldView-2 (VNIR or full set), and Digital Surface Model (DSM) data allow for different types of urban objects to be classified into predefined classes of interest with increased accuracy. The comparison to classification results of WorldView-2 multispectral data (8 spectral bands) is provided and the numerical evaluation of the method in comparison to other established methods illustrates the advantage in the classification accuracy for many classes such as buildings, low vegetation, sport objects, forest, roads, rail roads, etc.

Selectivity of N170 for visual words in the right hemisphere: Evidence from single-trial analysis.

PubMed

Yang, Hang; Zhao, Jing; Gaspar, Carl M; Chen, Wei; Tan, Yufei; Weng, Xuchu

2017-08-01

Neuroimaging and neuropsychological studies have identified the involvement of the right posterior region in the processing of visual words. Interestingly, in contrast, ERP studies of the N170 typically demonstrate selectivity for words more strikingly over the left hemisphere. Why is right hemisphere selectivity for words during the N170 epoch typically not observed, despite the clear involvement of this region in word processing? One possibility is that amplitude differences measured on averaged ERPs in previous studies may have been obscured by variation in peak latency across trials. This study examined this possibility by using single-trial analysis. Results show that words evoked greater single-trial N170s than control stimuli in the right hemisphere. Additionally, we observed larger trial-to-trial variability on N170 peak latency for words as compared to control stimuli over the right hemisphere. Results demonstrate that, in contrast to much of the prior literature, the N170 can be selective to words over the right hemisphere. This discrepancy is explained in terms of variability in trial-to-trial peak latency for responses to words over the right hemisphere. © 2017 Society for Psychophysiological Research.

Prime agricultural land monitoring and assessment component of the California Integrated Remote Sensing System

NASA Technical Reports Server (NTRS)

Estes, J. E.; Tinney, L. R. (Principal Investigator); Streich, T.

1981-01-01

The use of digital LANDSAT techniques for monitoring agricultural land use conversions was studied. Two study areas were investigated: one in Ventura County and the other in Fresno County (California). Ventura test site investigations included the use of three dates of LANDSAT data to improve classification performance beyond that previously obtained using single data techniques. The 9% improvement is considered highly significant. Also developed and demonstrated using Ventura County data is an automated cluster labeling procedure, considered a useful example of vertical data integration. Fresno County results for a single data LANDSAT classification paralleled those found in Ventura, demonstrating that the urban/rural fringe zone of most interest is a difficult environment to classify using LANDSAT data. A general raster to vector conversion program was developed to allow LANDSAT classification products to be transferred to an operational county level geographic information system in Fresno.

Use of multi-frequency, multi-polarization, multi-angle airborne radars for class discrimination in a southern temperature forest

NASA Technical Reports Server (NTRS)

Mehta, N. C.

1984-01-01

The utility of radar scatterometers for discrimination and characterization of natural vegetation was investigated. Backscatter measurements were acquired with airborne multi-frequency, multi-polarization, multi-angle radar scatterometers over a test site in a southern temperate forest. Separability between ground cover classes was studied using a two-class separability measure. Very good separability is achieved between most classes. Longer wavelength is useful in separating trees from non-tree classes, while shorter wavelength and cross polarization are helpful for discrimination among tree classes. Using the maximum likelihood classifier, 50% overall classification accuracy is achieved using a single, short-wavelength scatterometer channel. Addition of multiple incidence angles and another radar band improves classification accuracy by 20% and 50%, respectively, over the single channel accuracy. Incorporation of a third radar band seems redundant for vegetation classification. Vertical transmit polarization is critically important for all classes.

Investigation of correlation classification techniques

NASA Technical Reports Server (NTRS)

Haskell, R. E.

1975-01-01

A two-step classification algorithm for processing multispectral scanner data was developed and tested. The first step is a single pass clustering algorithm that assigns each pixel, based on its spectral signature, to a particular cluster. The output of that step is a cluster tape in which a single integer is associated with each pixel. The cluster tape is used as the input to the second step, where ground truth information is used to classify each cluster using an iterative method of potentials. Once the clusters have been assigned to classes the cluster tape is read pixel-by-pixel and an output tape is produced in which each pixel is assigned to its proper class. In addition to the digital classification programs, a method of using correlation clustering to process multispectral scanner data in real time by means of an interactive color video display is also described.

A Bayesian Approach to Estimating Coupling Between Neural Components: Evaluation of the Multiple Component, Event-Related Potential (mcERP) Algorithm

NASA Technical Reports Server (NTRS)

Shah, Ankoor S.; Knuth, Kevin H.; Truccolo, Wilson A.; Ding, Ming-Zhou; Bressler, Steven L.; Schroeder, Charles E.; Clancy, Daniel (Technical Monitor)

2002-01-01

Accurate measurement of single-trial responses is key to a definitive use of complex electromagnetic and hemodynamic measurements in the investigation of brain dynamics. We developed the multiple component, Event-Related Potential (mcERP) approach to single-trial response estimation. To improve our resolution of dynamic interactions between neuronal ensembles located in different layers within a cortical region and/or in different cortical regions. The mcERP model assets that multiple components defined as stereotypic waveforms comprise the stimulus-evoked response and that these components may vary in amplitude and latency from trial to trial. Maximum a posteriori (MAP) solutions for the model are obtained by iterating a set of equations derived from the posterior probability. Our first goal was to use the ANTWERP algorithm to analyze interactions (specifically latency and amplitude correlation) between responses in different layers within a cortical region. Thus, we evaluated the model by applying the algorithm to synthetic data containing two correlated local components and one independent far-field component. Three cases were considered: the local components were correlated by an interaction in their single-trial amplitudes, by an interaction in their single-trial latencies, or by an interaction in both amplitude and latency. We then analyzed the accuracy with which the algorithm estimated the component waveshapes and the single-trial parameters as a function of the linearity of each of these relationships. Extensions of these analyses to real data are discussed as well as ongoing work to incorporate more detailed prior information.

Single-use instruments, cutting blocks, and trials increase efficiency in the operating room during total knee arthroplasty: a prospective comparison of navigated and non-navigated cases.

PubMed

Mont, Michael A; McElroy, Mark J; Johnson, Aaron J; Pivec, Robert

2013-08-01

The purpose of this prospective controlled trial was to determine if efficiency increases could be achieved in non-navigated and navigated total knee arthroplasties by replacing traditional saws, cutting blocks, and trials with specialized saws and single-use cutting blocks and trials. Various timing metrics during total knee arthroplasty, including operating room preparation times and specific intra-operative times, were measured in 400 procedures performed by eight different surgeons at 6 institutions. Efficiency increases were the result of statistically significant reductions in combined instrument setup and cleanup times as well as in adjusted surgical episode times in navigated total knee arthroplasties. Single-use instruments show promising benefits, but adequate patient follow-up is needed to confirm safety and efficacy before they can be widely adopted. Nevertheless, the authors believe that the use of single-use instruments, cutting guides, and trial implants for total knee arthroplasty will play an increasing role in improving operating room efficiency. Copyright © 2013 Elsevier Inc. All rights reserved.

Woodland Mapping at Single-Tree Levels Using Object-Oriented Classification of Unmanned Aerial Vehicle (uav) Images

NASA Astrophysics Data System (ADS)

Chenari, A.; Erfanifard, Y.; Dehghani, M.; Pourghasemi, H. R.

2017-09-01

Remotely sensed datasets offer a reliable means to precisely estimate biophysical characteristics of individual species sparsely distributed in open woodlands. Moreover, object-oriented classification has exhibited significant advantages over different classification methods for delineation of tree crowns and recognition of species in various types of ecosystems. However, it still is unclear if this widely-used classification method can have its advantages on unmanned aerial vehicle (UAV) digital images for mapping vegetation cover at single-tree levels. In this study, UAV orthoimagery was classified using object-oriented classification method for mapping a part of wild pistachio nature reserve in Zagros open woodlands, Fars Province, Iran. This research focused on recognizing two main species of the study area (i.e., wild pistachio and wild almond) and estimating their mean crown area. The orthoimage of study area was consisted of 1,076 images with spatial resolution of 3.47 cm which was georeferenced using 12 ground control points (RMSE=8 cm) gathered by real-time kinematic (RTK) method. The results showed that the UAV orthoimagery classified by object-oriented method efficiently estimated mean crown area of wild pistachios (52.09±24.67 m2) and wild almonds (3.97±1.69 m2) with no significant difference with their observed values (α=0.05). In addition, the results showed that wild pistachios (accuracy of 0.90 and precision of 0.92) and wild almonds (accuracy of 0.90 and precision of 0.89) were well recognized by image segmentation. In general, we concluded that UAV orthoimagery can efficiently produce precise biophysical data of vegetation stands at single-tree levels, which therefore is suitable for assessment and monitoring open woodlands.

Spontaneous Fluctuations in Sensory Processing Predict Within-Subject Reaction Time Variability.

PubMed

Ribeiro, Maria J; Paiva, Joana S; Castelo-Branco, Miguel

2016-01-01

When engaged in a repetitive task our performance fluctuates from trial-to-trial. In particular, inter-trial reaction time variability has been the subject of considerable research. It has been claimed to be a strong biomarker of attention deficits, increases with frontal dysfunction, and predicts age-related cognitive decline. Thus, rather than being just a consequence of noise in the system, it appears to be under the control of a mechanism that breaks down under certain pathological conditions. Although the underlying mechanism is still an open question, consensual hypotheses are emerging regarding the neural correlates of reaction time inter-trial intra-individual variability. Sensory processing, in particular, has been shown to covary with reaction time, yet the spatio-temporal profile of the moment-to-moment variability in sensory processing is still poorly characterized. The goal of this study was to characterize the intra-individual variability in the time course of single-trial visual evoked potentials and its relationship with inter-trial reaction time variability. For this, we chose to take advantage of the high temporal resolution of the electroencephalogram (EEG) acquired while participants were engaged in a 2-choice reaction time task. We studied the link between single trial event-related potentials (ERPs) and reaction time using two different analyses: (1) time point by time point correlation analyses thereby identifying time windows of interest; and (2) correlation analyses between single trial measures of peak latency and amplitude and reaction time. To improve extraction of single trial ERP measures related with activation of the visual cortex, we used an independent component analysis (ICA) procedure. Our ERP analysis revealed a relationship between the N1 visual evoked potential and reaction time. The earliest time point presenting a significant correlation of its respective amplitude with reaction time occurred 175 ms after stimulus onset, just after the onset of the N1 peak. Interestingly, single trial N1 latency correlated significantly with reaction time, while N1 amplitude did not. In conclusion, our findings suggest that inter-trial variability in the timing of extrastriate visual processing contributes to reaction time variability.

Spontaneous Fluctuations in Sensory Processing Predict Within-Subject Reaction Time Variability

PubMed Central

Ribeiro, Maria J.; Paiva, Joana S.; Castelo-Branco, Miguel

2016-01-01

When engaged in a repetitive task our performance fluctuates from trial-to-trial. In particular, inter-trial reaction time variability has been the subject of considerable research. It has been claimed to be a strong biomarker of attention deficits, increases with frontal dysfunction, and predicts age-related cognitive decline. Thus, rather than being just a consequence of noise in the system, it appears to be under the control of a mechanism that breaks down under certain pathological conditions. Although the underlying mechanism is still an open question, consensual hypotheses are emerging regarding the neural correlates of reaction time inter-trial intra-individual variability. Sensory processing, in particular, has been shown to covary with reaction time, yet the spatio-temporal profile of the moment-to-moment variability in sensory processing is still poorly characterized. The goal of this study was to characterize the intra-individual variability in the time course of single-trial visual evoked potentials and its relationship with inter-trial reaction time variability. For this, we chose to take advantage of the high temporal resolution of the electroencephalogram (EEG) acquired while participants were engaged in a 2-choice reaction time task. We studied the link between single trial event-related potentials (ERPs) and reaction time using two different analyses: (1) time point by time point correlation analyses thereby identifying time windows of interest; and (2) correlation analyses between single trial measures of peak latency and amplitude and reaction time. To improve extraction of single trial ERP measures related with activation of the visual cortex, we used an independent component analysis (ICA) procedure. Our ERP analysis revealed a relationship between the N1 visual evoked potential and reaction time. The earliest time point presenting a significant correlation of its respective amplitude with reaction time occurred 175 ms after stimulus onset, just after the onset of the N1 peak. Interestingly, single trial N1 latency correlated significantly with reaction time, while N1 amplitude did not. In conclusion, our findings suggest that inter-trial variability in the timing of extrastriate visual processing contributes to reaction time variability. PMID:27242470

The Haematological Malignancy Research Network (HMRN): a new information strategy for population based epidemiology and health service research

PubMed Central

Smith, Alexandra; Roman, Eve; Howell, Debra; Jones, Richard; Patmore, Russell; Jack, Andrew

2010-01-01

The Haematological Malignancy Research Network (HMRN) was established in 2004 to provide robust generalizable data to inform clinical practice and research. It comprises an ongoing population-based cohort of patients newly diagnosed by a single integrated haematopathology laboratory in two adjacent UK Cancer Networks (population 3·6 million). With an emphasis on primary-source data, prognostic factors, sequential treatment/response history, and socio-demographic details are recorded to clinical trial standards. Data on 8131 patients diagnosed over the 4 years 2004–08 are examined here using the latest World Health Organization classification. HMRN captures all diagnoses (adult and paediatric) and the diagnostic age ranged from 4 weeks to 99 years (median 70·4 years). In line with published estimates, first-line clinical trial entry varied widely by disease subtype and age, falling from 59·5% in those aged <15 years to 1·9% in those aged over 75 years – underscoring the need for contextual population-based treatment and response data of the type collected by HMRN. The critical importance of incorporating molecular and prognostic markers into comparative survival analyses is illustrated with reference to diffuse-large B-cell lymphoma, acute myeloid leukaemia and myeloma. With respect to aetiology, several descriptive factors are highlighted and discussed, including the unexplained male predominance evident for most subtypes across all ages. PMID:19958356

Ensemble of sparse classifiers for high-dimensional biological data.

PubMed

Kim, Sunghan; Scalzo, Fabien; Telesca, Donatello; Hu, Xiao

2015-01-01

Biological data are often high in dimension while the number of samples is small. In such cases, the performance of classification can be improved by reducing the dimension of data, which is referred to as feature selection. Recently, a novel feature selection method has been proposed utilising the sparsity of high-dimensional biological data where a small subset of features accounts for most variance of the dataset. In this study we propose a new classification method for high-dimensional biological data, which performs both feature selection and classification within a single framework. Our proposed method utilises a sparse linear solution technique and the bootstrap aggregating algorithm. We tested its performance on four public mass spectrometry cancer datasets along with two other conventional classification techniques such as Support Vector Machines and Adaptive Boosting. The results demonstrate that our proposed method performs more accurate classification across various cancer datasets than those conventional classification techniques.

LANDSAT applications to wetlands classification in the upper Mississippi River Valley. Ph.D. Thesis. Final Report

NASA Technical Reports Server (NTRS)

Lillesand, T. M.; Werth, L. F. (Principal Investigator)

1980-01-01

A 25% improvement in average classification accuracy was realized by processing double-date vs. single-date data. Under the spectrally and spatially complex site conditions characterizing the geographical area used, further improvement in wetland classification accuracy is apparently precluded by the spectral and spatial resolution restrictions of the LANDSAT MSS. Full scene analysis of scanning densitometer data extracted from scale infrared photography failed to permit discrimination of many wetland and nonwetland cover types. When classification of photographic data was limited to wetland areas only, much more detailed and accurate classification could be made. The integration of conventional image interpretation (to simply delineate wetland boundaries) and machine assisted classification (to discriminate among cover types present within the wetland areas) appears to warrant further research to study the feasibility and cost of extending this methodology over a large area using LANDSAT and/or small scale photography.

A Randomized Control Trial of a Community Mental Health Intervention for Military Personnel

DTIC Science & Technology

2013-10-01

reporting period. 15. SUBJECT TERMS Mental health literacy , Mental Health First Aid (MHFA), curriculum adaptation 16. SECURITY CLASSIFICATION OF...Stress First Aid and suicide prevention gatekeeper training by providing a mental health literacy component that is currently not addressed

Patients' preferences for selection of endpoints in cardiovascular clinical trials.

PubMed

Chow, Robert D; Wankhedkar, Kashmira P; Mete, Mihriye

2014-01-01

To reduce the duration and overall costs of cardiovascular trials, use of the combined endpoints in trial design has become commonplace. Though this methodology may serve the needs of investigators and trial sponsors, the preferences of patients or potential trial subjects in the trial design process has not been studied. To determine the preferences of patients in the design of cardiovascular trials. Participants were surveyed in a pilot study regarding preferences among various single endpoints commonly used in cardiovascular trials, preference for single vs. composite endpoints, and the likelihood of compliance with a heart medication if patients similar to them participated in the trial design process. One hundred adult English-speaking patients, 38% male, from a primary care ambulatory practice located in an urban setting. Among single endpoints, participants rated heart attack as significantly more important than death from other causes (4.53 vs. 3.69, p=0.004) on a scale of 1-6. Death from heart disease was rated as significantly more important than chest pain (4.73 vs. 2.47, p<0.001), angioplasty/PCI/CABG (4.73 vs. 2.43, p<0.001), and stroke (4.73 vs. 2.43, p<0.001). Participants also expressed a slight preference for combined endpoints over single endpoint (43% vs. 57%), incorporation of the opinions of the study patient population into the design of trials (48% vs. 41% for researchers), and a greater likelihood of medication compliance if patient preferences were considered during trial design (67% indicated a significant to major effect). Patients are able to make judgments and express preferences regarding trial design. They prefer that the opinions of the study population rather than the general population be incorporated into the design of the study. This novel approach to study design would not only incorporate patient preferences into medical decision making, but it also has the potential to improve compliance with cardiovascular medications.

Single treatments that have lasting effects: some thoughts on the antidepressant effects of ketamine and botulinum toxin and the anxiolytic effect of psilocybin

PubMed Central

Young, Simon N.

2013-01-01

Recent clinical trials suggest that 3 single biological treatments have effects that persist. Based on research showing that the muscles involved in facial expressions can feed back to influence mood, a single trial diminishing glabella frown lines with botulinum toxin demonstrated a significant antidepressant effect for 16 weeks. Based primarily on research with animal models of depression suggesting that glutamate may be involved in depression, the N-methyl-d-aspartate antagonist ketamine has been tested in several trials. A single dose decreased depression for up to a week. The reported effects of the use of mushrooms containing psilocybin by a number of cultures around the world has stimulated several trials showing beneficial effects of a single dose of psilocybin for over a year in healthy people, and for up to 3 months in patients with anxiety disorders who have advanced cancer. This article discusses these studies, their rationale, their possible mechanisms of action, the future clinical research required to establish these therapies and the basic research required to optimize single treatments that have lasting effects. PMID:23171696

Non-ablative radiofrequency associated or not with low-level laser therapy on the treatment of facial wrinkles in adult women: A randomized single-blind clinical trial.

PubMed

Pereira, Thalita Rodrigues Christovam; Vassão, Patrícia Gabrielli; Venancio, Michele Garcia; Renno, Ana Cláudia Muniz; Aveiro, Mariana Chaves

2017-06-01

The objective of this study was to evaluate the effects of Non-ablative Radiofrequency (RF) associated or not with low-level laser therapy (LLLT) on aspect of facial wrinkles among adult women. Forty-six participants were randomized into three groups: Control Group (CG, n = 15), RF Group (RG, n = 16), and RF and LLLT Group (RLG, n = 15). Every participant was evaluated on baseline (T0), after eight weeks (T8) and eight weeks after the completion of treatment (follow-up). They were photographed in order to classify nasolabial folds and periorbital wrinkles (Modified Fitzpatrick Wrinkle Scale and Fitzpatrick Wrinkle Classification System, respectively) and improvement on appearance (Global Aesthetic Improvement Scale). Photograph analyses were performed by 3 blinded evaluators. Classification of nasolabial and periorbital wrinkles did not show any significant difference between groups. Aesthetic appearance indicated a significant improvement for nasolabial folds on the right side of face immediately after treatment (p = 0.018) and follow-up (p = 0.029) comparison. RG presented better results than CG on T8 (p = 0.041, ES = -0.49) and on follow-up (p = 0.041, ES = -0.49) and better than RLG on T8 (p = 0.041, ES = -0.49). RLG presented better results than CG on follow-up (p = 0.007, ES = -0.37). Nasolabial folds and periorbital wrinkles did not change throughout the study; however, some aesthetic improvement was observed. LLLT did not potentiate RF treatment.

Pattern classification of kinematic and kinetic running data to distinguish gender, shod/barefoot and injury groups with feature ranking.

PubMed

Eskofier, Bjoern M; Kraus, Martin; Worobets, Jay T; Stefanyshyn, Darren J; Nigg, Benno M

2012-01-01

The identification of differences between groups is often important in biomechanics. This paper presents group classification tasks using kinetic and kinematic data from a prospective running injury study. Groups composed of gender, of shod/barefoot running and of runners who developed patellofemoral pain syndrome (PFPS) during the study, and asymptotic runners were classified. The features computed from the biomechanical data were deliberately chosen to be generic. Therefore, they were suited for different biomechanical measurements and classification tasks without adaptation to the input signals. Feature ranking was applied to reveal the relevance of each feature to the classification task. Data from 80 runners were analysed for gender and shod/barefoot classification, while 12 runners were investigated in the injury classification task. Gender groups could be differentiated with 84.7%, shod/barefoot running with 98.3%, and PFPS with 100% classification rate. For the latter group, one single variable could be identified that alone allowed discrimination.

Arthroscopic study of injuries in articular fractures of distal radius extremity

PubMed Central

Araf, Marcelo; Mattar, Rames

2014-01-01

OBJECTIVE: To analyze the incidence of wrist ligament and cartilage associated fractures of the distal radius, through arthroscopy, correlating with AO/ASIF classification. METHODS: Thirty patients aged between 20 and 50 years old, with closed fracture from groups B and C according to AO/ASIF classification were selected. All of them were submitted to wrist arthroscopy to address intra-articular injuries and reduction and osteosynthesis of the fracture. RESULTS: A high incidence of intra-articular injuries was noticed, and 76.6% of them presented injury of the triangular fibrocartilage complex, 36.6% of the intrinsic scapholunate ligament, 6.6% of the intrinsic triquetrolunate ligament, and 33% articular cartilage injury larger than three millimeters. Patients with fractures from type C according to AO/ASIF classification presented a higher incidence of ligament injuries. CONCLUSION: There is no relationship between the presence of chondral injury and the AO/ASIF classification of the fractures in the cases reported in this study. Level of Evidence III, Non Randomized Controlled Trial. PMID:25061421

A consensus approach to the classification of pediatric pulmonary hypertensive vascular disease: Report from the PVRI Pediatric Taskforce, Panama 2011

PubMed Central

del Cerro, Maria Jesus; Abman, Steven; Diaz, Gabriel; Freudenthal, Alexandra Heath; Freudenthal, Franz; Harikrishnan, S.; Haworth, Sheila G.; Ivy, Dunbar; Lopes, Antonio A.; Raj, J. Usha; Sandoval, Julio; Stenmark, Kurt; Adatia, Ian

2011-01-01

Current classifications of pulmonary hypertension have contributed a great deal to our understanding of pulmonary vascular disease, facilitated drug trials, and improved our understanding of congenital heart disease in adult survivors. However, these classifications are not applicable readily to pediatric disease. The classification system that we propose is based firmly in clinical practice. The specific aims of this new system are to improve diagnostic strategies, to promote appropriate clinical investigation, to improve our understanding of disease pathogenesis, physiology and epidemiology, and to guide the development of human disease models in laboratory and animal studies. It should be also an educational resource. We emphasize the concepts of perinatal maladaptation, maldevelopment and pulmonary hypoplasia as causative factors in pediatric pulmonary hypertension. We highlight the importance of genetic, chromosomal and multiple congenital malformation syndromes in the presentation of pediatric pulmonary hypertension. We divide pediatric pulmonary hypertensive vascular disease into 10 broad categories. PMID:21874158

Gout Classification Criteria: Update and Implications

PubMed Central

Vargas-Santos, Ana Beatriz; Taylor, William J.

2016-01-01

Gout is the most common inflammatory arthritis, with a rising prevalence and incidence worldwide. There has been a resurgence in gout research, fueled, in part, by a number of advances in pharmacologic therapy for gout. The conduct of clinical trials and other observational research in gout requires a standardized and validated means of assembling well-defined groups of patients with gout for such research purposes. Recently, an international collaborative effort that involved a data-driven process with state-of-the art methodology supported by the American College of Rheumatology and the European League Against Rheumatism led to publication of new gout classification criteria. PMID:27342957

Assessing the criterion validity of four highly abbreviated measures from the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS).

PubMed

Gromisch, Elizabeth S; Zemon, Vance; Holtzer, Roee; Chiaravalloti, Nancy D; DeLuca, John; Beier, Meghan; Farrell, Eileen; Snyder, Stacey; Schairer, Laura C; Glukhovsky, Lisa; Botvinick, Jason; Sloan, Jessica; Picone, Mary Ann; Kim, Sonya; Foley, Frederick W

2016-10-01

Cognitive dysfunction is prevalent in multiple sclerosis. As self-reported cognitive functioning is unreliable, brief objective screening measures are needed. Utilizing widely used full-length neuropsychological tests, this study aimed to establish the criterion validity of highly abbreviated versions of the Brief Visuospatial Memory Test - Revised (BVMT-R), Symbol Digit Modalities Test (SDMT), Delis-Kaplan Executive Function System (D-KEFS) Sorting Test, and Controlled Oral Word Association Test (COWAT) in order to begin developing an MS-specific screening battery. Participants from Holy Name Medical Center and the Kessler Foundation were administered one or more of these four measures. Using test-specific criterion to identify impairment at both -1.5 and -2.0 SD, receiver-operating-characteristic (ROC) analyses of BVMT-R Trial 1, Trial 2, and Trial 1 + 2 raw data (N = 286) were run to calculate the classification accuracy of the abbreviated version, as well as the sensitivity and specificity. The same methods were used for SDMT 30-s and 60-s (N = 321), D-KEFS Sorting Free Card Sort 1 (N = 120), and COWAT letters F and A (N = 298). Using these definitions of impairment, each analysis yielded high classification accuracy (89.3 to 94.3%). BVMT-R Trial 1, SDMT 30-s, D-KEFS Free Card Sort 1, and COWAT F possess good criterion validity in detecting impairment on their respective overall measure, capturing much of the same information as the full version. Along with the first two trials of the California Verbal Learning Test - Second Edition (CVLT-II), these five highly abbreviated measures may be used to develop a brief screening battery.

Biomedical literature classification using encyclopedic knowledge: a Wikipedia-based bag-of-concepts approach.

PubMed

Mouriño García, Marcos Antonio; Pérez Rodríguez, Roberto; Anido Rifón, Luis E

2015-01-01

Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that allows for accessing to documents of interest in a simple and effective way; thus, it is necessary that these documents are sorted based on some criteria-that is to say, they have to be classified. Documents to classify are usually represented following the bag-of-words (BoW) paradigm. Features are words in the text-thus suffering from synonymy and polysemy-and their weights are just based on their frequency of occurrence. This paper presents an empirical study of the efficiency of a classifier that leverages encyclopedic background knowledge-concretely Wikipedia-in order to create bag-of-concepts (BoC) representations of documents, understanding concept as "unit of meaning", and thus tackling synonymy and polysemy. Besides, the weighting of concepts is based on their semantic relevance in the text. For the evaluation of the proposal, empirical experiments have been conducted with one of the commonly used corpora for evaluating classification and retrieval of biomedical information, OHSUMED, and also with a purpose-built corpus of MEDLINE biomedical abstracts, UVigoMED. Results obtained show that the Wikipedia-based bag-of-concepts representation outperforms the classical bag-of-words representation up to 157% in the single-label classification problem and up to 100% in the multi-label problem for OHSUMED corpus, and up to 122% in the single-label classification problem and up to 155% in the multi-label problem for UVigoMED corpus.

Biomedical literature classification using encyclopedic knowledge: a Wikipedia-based bag-of-concepts approach

PubMed Central

Pérez Rodríguez, Roberto; Anido Rifón, Luis E.

2015-01-01

Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that allows for accessing to documents of interest in a simple and effective way; thus, it is necessary that these documents are sorted based on some criteria—that is to say, they have to be classified. Documents to classify are usually represented following the bag-of-words (BoW) paradigm. Features are words in the text—thus suffering from synonymy and polysemy—and their weights are just based on their frequency of occurrence. This paper presents an empirical study of the efficiency of a classifier that leverages encyclopedic background knowledge—concretely Wikipedia—in order to create bag-of-concepts (BoC) representations of documents, understanding concept as “unit of meaning”, and thus tackling synonymy and polysemy. Besides, the weighting of concepts is based on their semantic relevance in the text. For the evaluation of the proposal, empirical experiments have been conducted with one of the commonly used corpora for evaluating classification and retrieval of biomedical information, OHSUMED, and also with a purpose-built corpus of MEDLINE biomedical abstracts, UVigoMED. Results obtained show that the Wikipedia-based bag-of-concepts representation outperforms the classical bag-of-words representation up to 157% in the single-label classification problem and up to 100% in the multi-label problem for OHSUMED corpus, and up to 122% in the single-label classification problem and up to 155% in the multi-label problem for UVigoMED corpus. PMID:26468436

A manual and an automatic TERS based virus discrimination

NASA Astrophysics Data System (ADS)

Olschewski, Konstanze; Kämmer, Evelyn; Stöckel, Stephan; Bocklitz, Thomas; Deckert-Gaudig, Tanja; Zell, Roland; Cialla-May, Dana; Weber, Karina; Deckert, Volker; Popp, Jürgen

2015-02-01

Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%.Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr07033j

Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

NASA Astrophysics Data System (ADS)

Löw, Fabian; Schorcht, Gunther; Michel, Ulrich; Dech, Stefan; Conrad, Christopher

2012-10-01

Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as useŕs and produceŕs accuracy.

Improvement of Information Transfer Rates Using a Hybrid EEG-NIRS Brain-Computer Interface with a Short Trial Length: Offline and Pseudo-Online Analyses.

PubMed

Shin, Jaeyoung; Kim, Do-Won; Müller, Klaus-Robert; Hwang, Han-Jeong

2018-06-05

Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (≥10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min). To alleviate the ITR degradation, we propose a more practical hBCI operated by intuitive mental tasks, such as mental arithmetic (MA) and word chain (WC) tasks, performed within a short trial length (5 s). In addition, the suitability of the WC as a BCI task was assessed, which has so far rarely been used in the BCI field. In this experiment, EEG and NIRS data were simultaneously recorded while participants performed MA and WC tasks without preliminary training and remained relaxed (baseline; BL). Each task was performed for 5 s, which was a shorter time than previous hBCI studies. Subsequently, a classification was performed to discriminate MA-related or WC-related brain activations from BL-related activations. By using hBCI in the offline/pseudo-online analyses, average classification accuracies of 90.0 ± 7.1/85.5 ± 8.1% and 85.8 ± 8.6/79.5 ± 13.4% for MA vs. BL and WC vs. BL, respectively, were achieved. These were significantly higher than those of the unimodal EEG- or NIRS-BCI in most cases. Given the short trial length and improved classification accuracy, the average ITRs were improved by more than 96.6% for MA vs. BL and 87.1% for WC vs. BL, respectively, compared to those reported in previous studies. The suitability of implementing a more practical hBCI based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies.

Memory Reactivation Predicts Resistance to Retroactive Interference: Evidence from Multivariate Classification and Pattern Similarity Analyses

PubMed Central

Rugg, Michael D.

2016-01-01

Memory reactivation—the reinstatement of processes and representations engaged when an event is initially experienced—is believed to play an important role in strengthening and updating episodic memory. The present study examines how memory reactivation during a potentially interfering event influences memory for a previously experienced event. Participants underwent fMRI during the encoding phase of an AB/AC interference task in which some words were presented twice in association with two different encoding tasks (AB and AC trials) and other words were presented once (DE trials). The later memory test required retrieval of the encoding tasks associated with each of the study words. Retroactive interference was evident for the AB encoding task and was particularly strong when the AC encoding task was remembered rather than forgotten. We used multivariate classification and pattern similarity analysis (PSA) to measure reactivation of the AB encoding task during AC trials. The results demonstrated that reactivation of generic task information measured with multivariate classification predicted subsequent memory for the AB encoding task regardless of whether interference was strong and weak (trials for which the AC encoding task was remembered or forgotten, respectively). In contrast, reactivation of neural patterns idiosyncratic to a given AB trial measured with PSA only predicted memory when the strength of interference was low. These results suggest that reactivation of features of an initial experience shared across numerous events in the same category, but not features idiosyncratic to a particular event, are important in resisting retroactive interference caused by new learning. SIGNIFICANCE STATEMENT Reactivating a previously encoded memory is believed to provide an opportunity to strengthen the memory, but also to return the memory to a labile state, making it susceptible to interference. However, there is debate as to how memory reactivation elicited by a potentially interfering event influences subsequent retrieval of the memory. The findings of the current study indicate that reactivating features idiosyncratic to a particular experience during interference only influences subsequent memory when interference is relatively weak. Critically, reactivation of generic contextual information predicts subsequent source memory when retroactive interference is either strong and weak. The results indicate that reactivation of generic information about a prior episode mitigates forgetting due to retroactive interference. PMID:27076433

Multiple kernel learning using single stage function approximation for binary classification problems

NASA Astrophysics Data System (ADS)

Shiju, S.; Sumitra, S.

2017-12-01

In this paper, the multiple kernel learning (MKL) is formulated as a supervised classification problem. We dealt with binary classification data and hence the data modelling problem involves the computation of two decision boundaries of which one related with that of kernel learning and the other with that of input data. In our approach, they are found with the aid of a single cost function by constructing a global reproducing kernel Hilbert space (RKHS) as the direct sum of the RKHSs corresponding to the decision boundaries of kernel learning and input data and searching that function from the global RKHS, which can be represented as the direct sum of the decision boundaries under consideration. In our experimental analysis, the proposed model had shown superior performance in comparison with that of existing two stage function approximation formulation of MKL, where the decision functions of kernel learning and input data are found separately using two different cost functions. This is due to the fact that single stage representation helps the knowledge transfer between the computation procedures for finding the decision boundaries of kernel learning and input data, which inturn boosts the generalisation capacity of the model.

Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps?

NASA Astrophysics Data System (ADS)

Turner, Joseph A.; Babcock, Russell C.; Hovey, Renae; Kendrick, Gary A.

2018-05-01

Numerous machine-learning classifiers are available for benthic habitat map production, which can lead to different results. This study highlights the performance of the Random Forest (RF) classifier, which was significantly better than Classification Trees (CT), Naïve Bayes (NB), and a multi-model ensemble in terms of overall accuracy, Balanced Error Rate (BER), Kappa, and area under the curve (AUC) values. RF accuracy was often higher than 90% for each substratum class, even at the most detailed level of the substratum classification and AUC values also indicated excellent performance (0.8-1). Total agreement between classifiers was high at the broadest level of classification (75-80%) when differentiating between hard and soft substratum. However, this sharply declined as the number of substratum categories increased (19-45%) including a mix of rock, gravel, pebbles, and sand. The model ensemble, produced from the results of all three classifiers by majority voting, did not show any increase in predictive performance when compared to the single RF classifier. This study shows how a single classifier may be sufficient to produce benthic seabed maps and model ensembles of multiple classifiers.

Assessment of satellite and aircraft multispectral scanner data for strip-mine monitoring

NASA Technical Reports Server (NTRS)

Spisz, E. W.; Dooley, J. T.

1980-01-01

The application of LANDSAT multispectral scanner data to describe the mining and reclamation changes of a hilltop surface coal mine in the rugged, mountainous area of eastern Kentucky is presented. Original single band satellite imagery, computer enhanced single band imagery, and computer classified imagery are presented for four different data sets in order to demonstrate the land cover changes that can be detected. Data obtained with an 11 band multispectral scanner on board a C-47 aircraft at an altitude of 3000 meters are also presented. Comparing the satellite data with color, infrared aerial photography, and ground survey data shows that significant changes in the disrupted area can be detected from LANDSAT band 5 satellite imagery for mines with more than 100 acres of disturbed area. However, band-ratio (bands 5/6) imagery provides greater contrast than single band imagery and can provide a qualitative level 1 classification of the land cover that may be useful for monitoring either the disturbed mining area or the revegetation progress. However, if a quantitative, accurate classification of the barren or revegetated classes is required, it is necessary to perform a detailed, four band computer classification of the data.

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

Evidence for single-dose protection by the bivalent HPV vaccine-Review of the Costa Rica HPV vaccine trial and future research studies.

PubMed

Kreimer, Aimée R; Herrero, Rolando; Sampson, Joshua N; Porras, Carolina; Lowy, Douglas R; Schiller, John T; Schiffman, Mark; Rodriguez, Ana Cecilia; Chanock, Stephen; Jimenez, Silvia; Schussler, John; Gail, Mitchell H; Safaeian, Mahboobeh; Kemp, Troy J; Cortes, Bernal; Pinto, Ligia A; Hildesheim, Allan; Gonzalez, Paula

2018-01-20

The Costa Rica Vaccine Trial (CVT), a phase III randomized clinical trial, provided the initial data that one dose of the HPV vaccine could provide durable protection against HPV infection. Although the study design was to administer all participants three doses of HPV or control vaccine, 20% of women did not receive the three-dose regimens, mostly due to involuntary reasons unrelated to vaccination. In 2011, we reported that a single dose of the bivalent HPV vaccine could be as efficacious as three doses of the vaccine using the endpoint of persistent HPV infection accumulated over the first four years of the trial; findings independently confirmed in the GSK-sponsored PATRICIA trial. Antibody levels after one dose, although lower than levels elicited by three doses, were 9-times higher than levels elicited by natural infection. Importantly, levels remained essentially constant over at least seven years, suggesting that the observed protection provided by a single dose might be durable. Much work has been done to assure these non-randomized findings are valid. Yet, the group of recipients who received one dose of the bivalent HPV vaccine in the CVT and PATRICIA trials was small and not randomly selected nor blinded to the number of doses received. The next phase of research is to conduct a formal randomized, controlled trial to evaluate the protection afforded by a single dose of HPV vaccine. Complementary studies are in progress to bridge our findings to other populations, and to further document the long-term durability of antibody response following a single dose. Published by Elsevier Ltd.

Working memory load-dependent spatio-temporal activity of single-trial P3 response detected with an adaptive wavelet denoiser.

PubMed

Zhang, Qiushi; Yang, Xueqian; Yao, Li; Zhao, Xiaojie

2017-03-27

Working memory (WM) refers to the holding and manipulation of information during cognitive tasks. Its underlying neural mechanisms have been explored through both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Trial-by-trial coupling of simultaneously collected EEG and fMRI signals has become an important and promising approach to study the spatio-temporal dynamics of such cognitive processes. Previous studies have demonstrated a modulation effect of the WM load on both the BOLD response in certain brain areas and the amplitude of P3. However, much remains to be explored regarding the WM load-dependent relationship between the amplitude of ERP components and cortical activities, and the low signal-to-noise ratio (SNR) of the EEG signal still poses a challenge to performing single-trial analyses. In this paper, we investigated the spatio-temporal activities of P3 during an n-back verbal WM task by introducing an adaptive wavelet denoiser into the extraction of single-trial P3 features and using general linear model (GLM) to integrate simultaneously collected EEG and fMRI data. Our results replicated the modulation effect of the WM load on the P3 amplitude. Additionally, the activation of single-trial P3 amplitudes was detected in multiple brain regions, including the insula, the cuneus, the lingual gyrus (LG), and the middle occipital gyrus (MOG). Moreover, we found significant correlations between P3 features and behavioral performance. These findings suggest that the single-trial integration of simultaneous EEG and fMRI signals may provide new insights into classical cognitive functions. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

A multi-disciplinary consensus statement concerning surgical approaches to low-grade, high-grade astrocytomas and diffuse intrinsic pontine gliomas in childhood (CPN Paris 2011) using the Delphi method.

PubMed

Walker, David A; Liu, JoFen; Kieran, Mark; Jabado, Nada; Picton, Susan; Packer, Roger; St Rose, Christian

2013-04-01

Astrocytic tumors account for 42% of childhood brain tumors, arising in all anatomical regions and associated with neurofibromatosis type 1 (NF1) in 15%. Anatomical site determines the degree and risk of resectability; the more complete resection, the better the survival rates. New biological markers and modern radiotherapy techniques are altering the risk assessments of clinical decisions for tumor resection and biopsy. The increasingly distinct pediatric neuro-oncology multidisciplinary team (PNMDT) is developing a distinct evidence base. A multidisciplinary consensus conference on pediatric neurosurgery was held in February 2011, where 92 invited participants reviewed evidence for clinical management of hypothalamic chiasmatic glioma (HCLGG), diffuse intrinsic pontine glioma (DIPG), and high-grade glioma (HGG). Twenty-seven statements were drafted and subjected to online Delphi consensus voting by participants, seeking >70% agreement from >60% of respondents; where <70% consensus occurred, the statement was modified and resubmitted for voting. Twenty-seven statements meeting consensus criteria are reported. For HCLGG, statements describing overall therapeutic purpose and indications for biopsy, observation, or treatment aimed at limiting the risk of visual damage and the need for on-going clinical trials were made. Primary surgical resection was not recommended. For DIPG, biopsy was recommended to ascertain biological characteristics to enhance understanding and targeting of treatments, especially in clinical trials. For HGG, biopsy is essential, the World Health Organization classification was recommended; selection of surgical strategy to achieve gross total resection in a single or multistep process should be discussed with the PNMDT and integrated with trials based drug strategies for adjuvant therapies.

Deep learning application: rubbish classification with aid of an android device

NASA Astrophysics Data System (ADS)

Liu, Sijiang; Jiang, Bo; Zhan, Jie

2017-06-01

Deep learning is a very hot topic currently in pattern recognition and artificial intelligence researches. Aiming at the practical problem that people usually don't know correct classifications some rubbish should belong to, based on the powerful image classification ability of the deep learning method, we have designed a prototype system to help users to classify kinds of rubbish. Firstly the CaffeNet Model was adopted for our classification network training on the ImageNet dataset, and the trained network was deployed on a web server. Secondly an android app was developed for users to capture images of unclassified rubbish, upload images to the web server for analyzing backstage and retrieve the feedback, so that users can obtain the classification guide by an android device conveniently. Tests on our prototype system of rubbish classification show that: an image of one single type of rubbish with origin shape can be better used to judge its classification, while an image containing kinds of rubbish or rubbish with changed shape may fail to help users to decide rubbish's classification. However, the system still shows promising auxiliary function for rubbish classification if the network training strategy can be optimized further.

Acute effects of a single exercise class on appetite, energy intake and mood. Is there a time of day effect?

PubMed

Maraki, M; Tsofliou, F; Pitsiladis, Y P; Malkova, D; Mutrie, N; Higgins, S

2005-12-01

This study aimed to investigate the acute effects of a single exercise class on appetite sensations, energy intake and mood, and to determine if there was a time of day effect. Twelve healthy, young, normal weight females, who were non-regular exercisers, participated in four trials: morning control, morning exercise, evening control and evening exercise. Exercise trials were a one-hour class of aerobic and muscle conditioning exercise of varying intensities, to music. Control trials were a one-hour rest. Ratings of perceived exertion were significantly greater during the warm-up and muscle conditioning parts of the morning exercise trial compared to those of the evening exercise trial. Although both exercise trials, compared to control trials, produced an increase in appetite sensations, they did not alter energy intake and produced a decrease in 'relative' energy intake. In relation to mood, both exercise trials increased positive affect and decreased negative affect. These results suggest that a single exercise class, representative of that offered by many sports centres, regardless of whether it is performed in the morning or evening produces a short-term negative energy balance and improves mood in normal weight women. However, when this type of exercise was performed in the morning it was perceived to require more effort.

Research design considerations for single-dose analgesic clinical trials in acute pain: IMMPACT recommendations.

PubMed

Cooper, Stephen A; Desjardins, Paul J; Turk, Dennis C; Dworkin, Robert H; Katz, Nathaniel P; Kehlet, Henrik; Ballantyne, Jane C; Burke, Laurie B; Carragee, Eugene; Cowan, Penney; Croll, Scott; Dionne, Raymond A; Farrar, John T; Gilron, Ian; Gordon, Debra B; Iyengar, Smriti; Jay, Gary W; Kalso, Eija A; Kerns, Robert D; McDermott, Michael P; Raja, Srinivasa N; Rappaport, Bob A; Rauschkolb, Christine; Royal, Mike A; Segerdahl, Märta; Stauffer, Joseph W; Todd, Knox H; Vanhove, Geertrui F; Wallace, Mark S; West, Christine; White, Richard E; Wu, Christopher

2016-02-01

This article summarizes the results of a meeting convened by the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) on key considerations and best practices governing the design of acute pain clinical trials. We discuss the role of early phase clinical trials, including pharmacokinetic-pharmacodynamic (PK-PD) trials, and the value of including both placebo and active standards of comparison in acute pain trials. This article focuses on single-dose and short-duration trials with emphasis on the perioperative and study design factors that influence assay sensitivity. Recommendations are presented on assessment measures, study designs, and operational factors. Although most of the methodological advances have come from studies of postoperative pain after dental impaction, bunionectomy, and other surgeries, the design considerations discussed are applicable to many other acute pain studies conducted in different settings.

Intra- and Interobserver Reliability of Three Classification Systems for Hallux Rigidus.

PubMed

Dillard, Sarita; Schilero, Christina; Chiang, Sharon; Pham, Peter

2018-04-18

There are over ten classification systems currently used in the staging of hallux rigidus. This results in confusion and inconsistency with radiographic interpretation and treatment. The reliability of hallux rigidus classification systems has not yet been tested. The purpose of this study was to evaluate intra- and interobserver reliability using three commonly used classifications for hallux rigidus. Twenty-one plain radiograph sets were presented to ten ACFAS board-certified foot and ankle surgeons. Each physician classified each radiograph based on clinical experience and knowledge according to the Regnauld, Roukis, and Hattrup and Johnson classification systems. The two-way mixed single-measure consistency intraclass correlation was used to calculate intra- and interrater reliability. The intrarater reliability of individual sets for the Roukis and Hattrup and Johnson classification systems was "fair to good" (Roukis, 0.62±0.19; Hattrup and Johnson, 0.62±0.28), whereas the intrarater reliability of individual sets for the Regnauld system bordered between "fair to good" and "poor" (0.43±0.24). The interrater reliability of the mean classification was "excellent" for all three classification systems. Conclusions Reliable and reproducible classification systems are essential for treatment and prognostic implications in hallux rigidus. In our study, Roukis classification system had the best intrarater reliability. Although there are various classification systems for hallux rigidus, our results indicate that all three of these classification systems show reliability and reproducibility.

On the exact solutions of high order wave equations of KdV type (I)

NASA Astrophysics Data System (ADS)

Bulut, Hasan; Pandir, Yusuf; Baskonus, Haci Mehmet

2014-12-01

In this paper, by means of a proper transformation and symbolic computation, we study high order wave equations of KdV type (I). We obtained classification of exact solutions that contain soliton, rational, trigonometric and elliptic function solutions by using the extended trial equation method. As a result, the motivation of this paper is to utilize the extended trial equation method to explore new solutions of high order wave equation of KdV type (I). This method is confirmed by applying it to this kind of selected nonlinear equations.

Effectiveness and Patient Acceptability of Stellate Ganglion Block (SGB) for Treatment of Posttraumatic Stress Disorder (PTSD) Symptoms among Active Duty Military Members

DTIC Science & Technology

2017-03-01

ORGANIZATION: Research Triangle Institute Research Triangle Park, NC 27709-0155 REPORT DATE: March 2017 TYPE OF REPORT: Annual PREPARED FOR: U.S...ganglion block, Posttraumatic Stress Disorder, randomized controlled trial, qualitative research 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...Posttraumatic Stress Disorder, randomized controlled trial,  qualitative   research     3.  Accomplishments    The major goals of this project for year two

Application of tripolar concentric electrodes and prefeature selection algorithm for brain-computer interface.

PubMed

Besio, Walter G; Cao, Hongbao; Zhou, Peng

2008-04-01

For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

A robust probabilistic collaborative representation based classification for multimodal biometrics

NASA Astrophysics Data System (ADS)

Zhang, Jing; Liu, Huanxi; Ding, Derui; Xiao, Jianli

2018-04-01

Most of the traditional biometric recognition systems perform recognition with a single biometric indicator. These systems have suffered noisy data, interclass variations, unacceptable error rates, forged identity, and so on. Due to these inherent problems, it is not valid that many researchers attempt to enhance the performance of unimodal biometric systems with single features. Thus, multimodal biometrics is investigated to reduce some of these defects. This paper proposes a new multimodal biometric recognition approach by fused faces and fingerprints. For more recognizable features, the proposed method extracts block local binary pattern features for all modalities, and then combines them into a single framework. For better classification, it employs the robust probabilistic collaborative representation based classifier to recognize individuals. Experimental results indicate that the proposed method has improved the recognition accuracy compared to the unimodal biometrics.

Single-visit or multiple-visit root canal treatment: systematic review, meta-analysis and trial sequential analysis.

PubMed

Schwendicke, Falk; Göstemeyer, Gerd

2017-02-01

Single-visit root canal treatment has some advantages over conventional multivisit treatment, but might increase the risk of complications. We systematically evaluated the risk of complications after single-visit or multiple-visit root canal treatment using meta-analysis and trial-sequential analysis. Controlled trials comparing single-visit versus multiple-visit root canal treatment of permanent teeth were included. Trials needed to assess the risk of long-term complications (pain, infection, new/persisting/increasing periapical lesions ≥1 year after treatment), short-term pain or flare-up (acute exacerbation of initiation or continuation of root canal treatment). Electronic databases (PubMed, EMBASE, Cochrane Central) were screened, random-effects meta-analyses performed and trial-sequential analysis used to control for risk of random errors. Evidence was graded according to GRADE. 29 trials (4341 patients) were included, all but 6 showing high risk of bias. Based on 10 trials (1257 teeth), risk of complications was not significantly different in single-visit versus multiple-visit treatment (risk ratio (RR) 1.00 (95% CI 0.75 to 1.35); weak evidence). Based on 20 studies (3008 teeth), risk of pain did not significantly differ between treatments (RR 0.99 (95% CI 0.76 to 1.30); moderate evidence). Risk of flare-up was recorded by 8 studies (1110 teeth) and was significantly higher after single-visit versus multiple-visit treatment (RR 2.13 (95% CI 1.16 to 3.89); very weak evidence). Trial-sequential analysis revealed that firm evidence for benefit, harm or futility was not reached for any of the outcomes. There is insufficient evidence to rule out whether important differences between both strategies exist. Dentists can provide root canal treatment in 1 or multiple visits. Given the possibly increased risk of flare-ups, multiple-visit treatment might be preferred for certain teeth (eg, those with periapical lesions). Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Grand average ERP-image plotting and statistics: A method for comparing variability in event-related single-trial EEG activities across subjects and conditions

PubMed Central

Delorme, Arnaud; Miyakoshi, Makoto; Jung, Tzyy-Ping; Makeig, Scott

2014-01-01

With the advent of modern computing methods, modeling trial-to-trial variability in biophysical recordings including electroencephalography (EEG) has become of increasingly interest. Yet no widely used method exists for comparing variability in ordered collections of single-trial data epochs across conditions and subjects. We have developed a method based on an ERP-image visualization tool in which potential, spectral power, or some other measure at each time point in a set of event-related single-trial data epochs are represented as color coded horizontal lines that are then stacked to form a 2-D colored image. Moving-window smoothing across trial epochs can make otherwise hidden event-related features in the data more perceptible. Stacking trials in different orders, for example ordered by subject reaction time, by context-related information such as inter-stimulus interval, or some other characteristic of the data (e.g., latency-window mean power or phase of some EEG source) can reveal aspects of the multifold complexities of trial-to-trial EEG data variability. This study demonstrates new methods for computing and visualizing grand ERP-image plots across subjects and for performing robust statistical testing on the resulting images. These methods have been implemented and made freely available in the EEGLAB signal-processing environment that we maintain and distribute. PMID:25447029

Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

2011-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

Investigating the effects of visual distractors on the performance of a motor imagery brain-computer interface.

PubMed

Emami, Zahra; Chau, Tom

2018-06-01

Brain-computer interfaces (BCIs) allow users to operate a device or application by means of cognitive activity. This technology will ultimately be used in real-world environments which include the presence of distractors. The purpose of the study was to determine the effect of visual distractors on BCI performance. Sixteen able-bodied participants underwent neurofeedback training to achieve motor imagery-guided BCI control in an online paradigm using electroencephalography (EEG) to measure neural signals. Participants then completed two sessions of the motor imagery EEG-BCI protocol in the presence of infrequent, small visual distractors. BCI performance was determined based on classification accuracy. The presence of distractors was found to affect motor imagery-specific patterns in mu and beta power. However, the distractors did not significantly affect the BCI classification accuracy; across participants, the mean classification accuracy was 81.5 ± 14% for non-distractor trials, and 78.3 ± 17% for distractor trials. This minimal consequence suggests that the BCI was robust to distractor effects, despite motor imagery-related brain activity being attenuated amid distractors. A BCI system that mitigates distraction-related effects may improve the ease of its use and ultimately facilitate the effective translation of the technology from the lab to the home. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Artificial neural network detects human uncertainty

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Koronovskii, Alexey A.; Garcia-Prieto, Juan; Antón-Toro, Luis Fernando; Maestú, Fernando; Pisarchik, Alexander N.

2018-03-01

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

Global signal modulation of single-trial fMRI response variability: Effect on positive vs negative BOLD response relationship.

PubMed

Mayhew, S D; Mullinger, K J; Ostwald, D; Porcaro, C; Bowtell, R; Bagshaw, A P; Francis, S T

2016-06-01

In functional magnetic resonance imaging (fMRI), the relationship between positive BOLD responses (PBRs) and negative BOLD responses (NBRs) to stimulation is potentially informative about the balance of excitatory and inhibitory brain responses in sensory cortex. In this study, we performed three separate experiments delivering visual, motor or somatosensory stimulation unilaterally, to one side of the sensory field, to induce PBR and NBR in opposite brain hemispheres. We then assessed the relationship between the evoked amplitudes of contralateral PBR and ipsilateral NBR at the level of both single-trial and average responses. We measure single-trial PBR and NBR peak amplitudes from individual time-courses, and show that they were positively correlated in all experiments. In contrast, in the average response across trials the absolute magnitudes of both PBR and NBR increased with increasing stimulus intensity, resulting in a negative correlation between mean response amplitudes. Subsequent analysis showed that the amplitude of single-trial PBR was positively correlated with the BOLD response across all grey-matter voxels and was not specifically related to the ipsilateral sensory cortical response. We demonstrate that the global component of this single-trial response modulation could be fully explained by voxel-wise vascular reactivity, the BOLD signal standard deviation measured in a separate resting-state scan (resting state fluctuation amplitude, RSFA). However, bilateral positive correlation between PBR and NBR regions remained. We further report that modulations in the global brain fMRI signal cannot fully account for this positive PBR-NBR coupling and conclude that the local sensory network response reflects a combination of superimposed vascular and neuronal signals. More detailed quantification of physiological and noise contributions to the BOLD signal is required to fully understand the trial-by-trial PBR and NBR relationship compared with that of average responses. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

The effects of a rhythm and music-based therapy program and therapeutic riding in late recovery phase following stroke: a study protocol for a three-armed randomized controlled trial.

PubMed

Bunketorp Käll, Lina; Lundgren-Nilsson, Åsa; Blomstrand, Christian; Pekna, Marcela; Pekny, Milos; Nilsson, Michael

2012-11-21

Stroke represents one of the most costly and long-term disabling conditions in adulthood worldwide and there is a need to determine the effectiveness of rehabilitation programs in the late phase after stroke. Limited scientific support exists for training incorporating rhythm and music as well as therapeutic riding and well-designed trials to determine the effectiveness of these treatment modalities are warranted. A single blinded three-armed randomized controlled trial is described with the aim to evaluate whether it is possible to improve the overall health status and functioning of individuals in the late phase of stroke (1-5 years after stroke) through a rhythm and music-based therapy program or therapeutic riding. About 120 individuals will be consecutively and randomly allocated to one of three groups: (T1) rhythm and music-based therapy program; (T2) therapeutic riding; or (T3) control group receiving the T1 training program a year later. Evaluation is conducted prior to and after the 12-week long intervention as well as three and six months later. The evaluation comprises a comprehensive functional and cognitive assessment (both qualitative and quantitative), and questionnaires. Based on the International classification of functioning, disability, and health (ICF), the outcome measures are classified into six comprehensive domains, with participation as the primary outcome measure assessed by the Stroke Impact Scale (SIS, version 2.0.). The secondary outcome measures are grouped within the following domains: body function, activity, environmental factors and personal factors. Life satisfaction and health related quality of life constitute an additional domain. A total of 84 participants were randomised and have completed the intervention. Recruitment proceeds and follow-up is on-going, trial results are expected in early 2014. This study will ascertain whether any of the two intervention programs can improve overall health status and functioning in the late phase of stroke. A positive outcome would increase the scientific basis for the use of such interventions in the late phase after stroke. Clinical Trials.gov Identifier: NCT01372059.

Automated cell-type classification in intact tissues by single-cell molecular profiling

PubMed Central

2018-01-01

A major challenge in biology is identifying distinct cell classes and mapping their interactions in vivo. Tissue-dissociative technologies enable deep single cell molecular profiling but do not provide spatial information. We developed a proximity ligation in situ hybridization technology (PLISH) with exceptional signal strength, specificity, and sensitivity in tissue. Multiplexed data sets can be acquired using barcoded probes and rapid label-image-erase cycles, with automated calculation of single cell profiles, enabling clustering and anatomical re-mapping of cells. We apply PLISH to expression profile ~2900 cells in intact mouse lung, which identifies and localizes known cell types, including rare ones. Unsupervised classification of the cells indicates differential expression of ‘housekeeping’ genes between cell types, and re-mapping of two sub-classes of Club cells highlights their segregated spatial domains in terminal airways. By enabling single cell profiling of various RNA species in situ, PLISH can impact many areas of basic and medical research. PMID:29319504

Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy images.

PubMed

Wu, Allison Chia-Yi; Rifkin, Scott A

2015-03-27

Recent techniques for tagging and visualizing single molecules in fixed or living organisms and cell lines have been revolutionizing our understanding of the spatial and temporal dynamics of fundamental biological processes. However, fluorescence microscopy images are often noisy, and it can be difficult to distinguish a fluorescently labeled single molecule from background speckle. We present a computational pipeline to distinguish the true signal of fluorescently labeled molecules from background fluorescence and noise. We test our technique using the challenging case of wide-field, epifluorescence microscope image stacks from single molecule fluorescence in situ experiments on nematode embryos where there can be substantial out-of-focus light and structured noise. The software recognizes and classifies individual mRNA spots by measuring several features of local intensity maxima and classifying them with a supervised random forest classifier. A key innovation of this software is that, by estimating the probability that each local maximum is a true spot in a statistically principled way, it makes it possible to estimate the error introduced by image classification. This can be used to assess the quality of the data and to estimate a confidence interval for the molecule count estimate, all of which are important for quantitative interpretations of the results of single-molecule experiments. The software classifies spots in these images well, with >95% AUROC on realistic artificial data and outperforms other commonly used techniques on challenging real data. Its interval estimates provide a unique measure of the quality of an image and confidence in the classification.

Single, double or multiple-injection techniques for non-ultrasound guided axillary brachial plexus block in adults undergoing surgery of the lower arm.

PubMed

Chin, Ki Jinn; Alakkad, Husni; Cubillos, Javier E

2013-08-08

Regional anaesthesia comprising axillary block of the brachial plexus is a common anaesthetic technique for distal upper limb surgery. This is an update of a review first published in 2006 and updated in 2011. To compare the relative effects (benefits and harms) of three injection techniques (single, double and multiple) of axillary block of the brachial plexus for distal upper extremity surgery. We considered these effects primarily in terms of anaesthetic effectiveness; the complication rate (neurological and vascular); and pain and discomfort caused by performance of the block. We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library), MEDLINE, EMBASE and reference lists of trials. We contacted trial authors. The date of the last search was March 2013 (updated from March 2011). We included randomized controlled trials that compared double with single-injection techniques, multiple with single-injection techniques, or multiple with double-injection techniques for axillary block in adults undergoing surgery of the distal upper limb. We excluded trials using ultrasound-guided techniques. Independent study selection, risk of bias assessment and data extraction were performed by at least two investigators. We undertook meta-analysis. The 21 included trials involved a total of 2148 participants who received regional anaesthesia for hand, wrist, forearm or elbow surgery. Risk of bias assessment indicated that trial design and conduct were generally adequate; the most common areas of weakness were in blinding and allocation concealment.Eight trials comparing double versus single injections showed a statistically significant decrease in primary anaesthesia failure (risk ratio (RR 0.51), 95% confidence interval (CI) 0.30 to 0.85). Subgroup analysis by method of nerve location showed that the effect size was greater when neurostimulation was used rather than the transarterial technique.Eight trials comparing multiple with single injections showed a statistically significant decrease in primary anaesthesia failure (RR 0.25, 95% CI 0.14 to 0.44) and of incomplete motor block (RR 0.61, 95% CI 0.39 to 0.96) in the multiple injection group.Eleven trials comparing multiple with double injections showed a statistically significant decrease in primary anaesthesia failure (RR 0.28, 95% CI 0.20 to 0.40) and of incomplete motor block (RR 0.55, 95% CI 0.36 to 0.85) in the multiple injection group.Tourniquet pain was significantly reduced with multiple injections compared with double injections (RR 0.53, 95% CI 0.33 to 0.84). Otherwise there were no statistically significant differences between groups in any of the three comparisons on secondary analgesia failure, complications and patient discomfort. The time for block performance was significantly shorter for single and double injections compared with multiple injections. This review provides evidence that multiple-injection techniques using nerve stimulation for axillary plexus block produce more effective anaesthesia than either double or single-injection techniques. However, there was insufficient evidence for a significant difference in other outcomes, including safety.

High-Performance Single-Photon Sources via Spatial Multiplexing

DTIC Science & Technology

2014-01-01

ingredient for tasks such as quantum cryptography , quantum repeater, quantum teleportation, quantum computing, and truly-random number generation. Recently...SECURITY CLASSIFICATION OF: Single photons sources are desired for many potential quantum information applications. One common method to produce...photons sources are desired for many potential quantum information applications. One common method to produce single photons is based on a “heralding

Sensitivity subgroup analysis based on single-center vs. multi-center trial status when interpreting meta-analyses pooled estimates: the logical way forward.

PubMed

Alexander, Paul E; Bonner, Ashley J; Agarwal, Arnav; Li, Shelly-Anne; Hariharan, Abishek; Izhar, Zain; Bhatnagar, Neera; Alba, Carolina; Akl, Elie A; Fei, Yutong; Guyatt, Gordon H; Beyene, Joseph

2016-06-01

Prior studies regarding whether single-center trial estimates are larger than multi-center are equivocal. We examined the extent to which single-center trials yield systematically larger effects than multi-center trials. We searched the 119 core clinical journals and the Cochrane Database of Systematic Reviews for meta-analyses (MAs) of randomized controlled trials (RCTs) published during 2012. In this meta-epidemiologic study, for binary variables, we computed the pooled ratio of ORs (RORs), and for continuous outcomes mean difference in standardized mean differences (SMDs), we conducted weighted random-effects meta-regression and random-effects MA modeling. Our primary analyses were restricted to MAs that included at least five RCTs and in which at least 25% of the studies used each of single trial center (SC) and more trial center (MC) designs. We identified 81 MAs for the odds ratio (OR) and 43 for the SMD outcome measures. Based on our analytic plan, our primary analysis (core) is based on 25 MAs/241 RCTs (binary outcome) and 18 MAs/173 RCTs (continuous outcome). Based on the core analysis, we found no difference in magnitude of effect between SC and MC for binary outcomes [RORs: 1.02; 95% confidence interval (CI): 0.83, 1.24; I(2) 20.2%]. Effect sizes were systematically larger for SC than MC for the continuous outcome measure (mean difference in SMDs: -0.13; 95% CI: -0.21, -0.05; I(2) 0%). Our results do not support prior findings of larger effects in SC than MC trials addressing binary outcomes but show a very similar small increase in effect in SC than MC trials addressing continuous outcomes. Authors of systematic reviews would be wise to include all trials irrespective of SC vs. MC design and address SC vs. MC status as a possible explanation of heterogeneity (and consider sensitivity analyses). Copyright © 2015 Elsevier Inc. All rights reserved.

Comparison of different Kalman filter approaches in deriving time varying connectivity from EEG data.

PubMed

Ghumare, Eshwar; Schrooten, Maarten; Vandenberghe, Rik; Dupont, Patrick

2015-08-01

Kalman filter approaches are widely applied to derive time varying effective connectivity from electroencephalographic (EEG) data. For multi-trial data, a classical Kalman filter (CKF) designed for the estimation of single trial data, can be implemented by trial-averaging the data or by averaging single trial estimates. A general linear Kalman filter (GLKF) provides an extension for multi-trial data. In this work, we studied the performance of the different Kalman filtering approaches for different values of signal-to-noise ratio (SNR), number of trials and number of EEG channels. We used a simulated model from which we calculated scalp recordings. From these recordings, we estimated cortical sources. Multivariate autoregressive model parameters and partial directed coherence was calculated for these estimated sources and compared with the ground-truth. The results showed an overall superior performance of GLKF except for low levels of SNR and number of trials.

Integrative image segmentation optimization and machine learning approach for high quality land-use and land-cover mapping using multisource remote sensing data

NASA Astrophysics Data System (ADS)

Gibril, Mohamed Barakat A.; Idrees, Mohammed Oludare; Yao, Kouame; Shafri, Helmi Zulhaidi Mohd

2018-01-01

The growing use of optimization for geographic object-based image analysis and the possibility to derive a wide range of information about the image in textual form makes machine learning (data mining) a versatile tool for information extraction from multiple data sources. This paper presents application of data mining for land-cover classification by fusing SPOT-6, RADARSAT-2, and derived dataset. First, the images and other derived indices (normalized difference vegetation index, normalized difference water index, and soil adjusted vegetation index) were combined and subjected to segmentation process with optimal segmentation parameters obtained using combination of spatial and Taguchi statistical optimization. The image objects, which carry all the attributes of the input datasets, were extracted and related to the target land-cover classes through data mining algorithms (decision tree) for classification. To evaluate the performance, the result was compared with two nonparametric classifiers: support vector machine (SVM) and random forest (RF). Furthermore, the decision tree classification result was evaluated against six unoptimized trials segmented using arbitrary parameter combinations. The result shows that the optimized process produces better land-use land-cover classification with overall classification accuracy of 91.79%, 87.25%, and 88.69% for SVM and RF, respectively, while the results of the six unoptimized classifications yield overall accuracy between 84.44% and 88.08%. Higher accuracy of the optimized data mining classification approach compared to the unoptimized results indicates that the optimization process has significant impact on the classification quality.

Performance of Four Frailty Classifications in Older Patients With Cancer: Prospective Elderly Cancer Patients Cohort Study.

PubMed

Ferrat, Emilie; Paillaud, Elena; Caillet, Philippe; Laurent, Marie; Tournigand, Christophe; Lagrange, Jean-Léon; Droz, Jean-Pierre; Balducci, Lodovico; Audureau, Etienne; Canouï-Poitrine, Florence; Bastuji-Garin, Sylvie

2017-03-01

Purpose Frailty classifications of older patients with cancer have been developed to assist physicians in selecting cancer treatments and geriatric interventions. They have not been compared, and their performance in predicting outcomes has not been assessed. Our objectives were to assess agreement among four classifications and to compare their predictive performance in a large cohort of in- and outpatients with various cancers. Patients and Methods We prospectively included 1,021 patients age 70 years or older who had solid or hematologic malignancies and underwent a geriatric assessment in one of two French teaching hospitals between 2007 and 2012. Among them, 763 were assessed using four classifications: Balducci, International Society of Geriatric Oncology (SIOG) 1, SIOG2, and a latent class typology. Agreement was assessed using the κ statistic. Outcomes were 1-year mortality and 6-month unscheduled admissions. Results All four classifications had good discrimination for 1-year mortality (C-index ≥ 0.70); discrimination was best with SIOG1. For 6-month unscheduled admissions, discrimination was good with all four classifications (C-index ≥ 0.70). For classification into three (fit, vulnerable, or frail) or two categories (fit v vulnerable or frail and fit or vulnerable v frail), agreement among the four classifications ranged from very poor (κ ≤ 0.20) to good (0.60 < κ ≤ 0.80). Agreement was best between SIOG1 and the latent class typology and between SIOG1 and Balducci. Conclusion These four frailty classifications have good prognostic performance among older in- and outpatients with various cancers. They may prove useful in decision making about cancer treatments and geriatric interventions and/or in stratifying older patients with cancer in clinical trials.

Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor

NASA Astrophysics Data System (ADS)

Rahman, Husna Abdul; Harun, Sulaiman Wadi; Arof, Hamzah; Irawati, Ninik; Musirin, Ismail; Ibrahim, Fatimah; Ahmad, Harith

2014-05-01

An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.

Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor.

PubMed

Rahman, Husna Abdul; Harun, Sulaiman Wadi; Arof, Hamzah; Irawati, Ninik; Musirin, Ismail; Ibrahim, Fatimah; Ahmad, Harith

2014-05-01

An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.

Automated simultaneous multiple feature classification of MTI data

NASA Astrophysics Data System (ADS)

Harvey, Neal R.; Theiler, James P.; Balick, Lee K.; Pope, Paul A.; Szymanski, John J.; Perkins, Simon J.; Porter, Reid B.; Brumby, Steven P.; Bloch, Jeffrey J.; David, Nancy A.; Galassi, Mark C.

2002-08-01

Los Alamos National Laboratory has developed and demonstrated a highly capable system, GENIE, for the two-class problem of detecting a single feature against a background of non-feature. In addition to the two-class case, however, a commonly encountered remote sensing task is the segmentation of multispectral image data into a larger number of distinct feature classes or land cover types. To this end we have extended our existing system to allow the simultaneous classification of multiple features/classes from multispectral data. The technique builds on previous work and its core continues to utilize a hybrid evolutionary-algorithm-based system capable of searching for image processing pipelines optimized for specific image feature extraction tasks. We describe the improvements made to the GENIE software to allow multiple-feature classification and describe the application of this system to the automatic simultaneous classification of multiple features from MTI image data. We show the application of the multiple-feature classification technique to the problem of classifying lava flows on Mauna Loa volcano, Hawaii, using MTI image data and compare the classification results with standard supervised multiple-feature classification techniques.

Application of LANDSAT system for improving methodology for inventory and classification of wetlands

NASA Technical Reports Server (NTRS)

Gilmer, D. S. (Principal Investigator)

1976-01-01

The author has identified the following significant results. A newly developed software system for generating statistics on surface water features was tested using LANDSAT data acquired previous to 1975. This software test provided a satisfactory evaluation of the system and also allowed expansion of data base on prairie water features. The software system recognizes water on the basis of a classification algorithm. This classification is accomplished by level thresholding a single near infrared data channel. After each pixel is classified as water or nonwater, the software system then recognizes ponds or lakes as sets of contiguous pixels or as single isolated pixels in the case of very small ponds. Pixels are considered to be contiguous if they are adjacent between successive scan lines. After delineating each water feature, the software system then assigns the feature a position based upon a geographic grid system and calculates the feature's planimetric area, its perimeter, and a parameter known as the shape factor.

Improving LUC estimation accuracy with multiple classification system for studying impact of urbanization on watershed flood

NASA Astrophysics Data System (ADS)

Dou, P.

2017-12-01

Guangzhou has experienced a rapid urbanization period called "small change in three years and big change in five years" since the reform of China, resulting in significant land use/cover changes(LUC). To overcome the disadvantages of single classifier for remote sensing image classification accuracy, a multiple classifier system (MCS) is proposed to improve the quality of remote sensing image classification. The new method combines advantages of different learning algorithms, and achieves higher accuracy (88.12%) than any single classifier did. With the proposed MCS, land use/cover (LUC) on Landsat images from 1987 to 2015 was obtained, and the LUCs were used on three watersheds (Shijing river, Chebei stream, and Shahe stream) to estimate the impact of urbanization on water flood. The results show that with the high accuracy LUC, the uncertainty in flood simulations are reduced effectively (for Shijing river, Chebei stream, and Shahe stream, the uncertainty reduced 15.5%, 17.3% and 19.8% respectively).

Multiple signal classification algorithm for super-resolution fluorescence microscopy

PubMed Central

Agarwal, Krishna; Macháň, Radek

2016-01-01

Single-molecule localization techniques are restricted by long acquisition and computational times, or the need of special fluorophores or biologically toxic photochemical environments. Here we propose a statistical super-resolution technique of wide-field fluorescence microscopy we call the multiple signal classification algorithm which has several advantages. It provides resolution down to at least 50 nm, requires fewer frames and lower excitation power and works even at high fluorophore concentrations. Further, it works with any fluorophore that exhibits blinking on the timescale of the recording. The multiple signal classification algorithm shows comparable or better performance in comparison with single-molecule localization techniques and four contemporary statistical super-resolution methods for experiments of in vitro actin filaments and other independently acquired experimental data sets. We also demonstrate super-resolution at timescales of 245 ms (using 49 frames acquired at 200 frames per second) in samples of live-cell microtubules and live-cell actin filaments imaged without imaging buffers. PMID:27934858

Evaluation of spatial filtering on the accuracy of wheat area estimate

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Moreira, M. A.; Chen, S. C.; Delima, A. M.

1982-01-01

A 3 x 3 pixel spatial filter for postclassification was used for wheat classification to evaluate the effects of this procedure on the accuracy of area estimation using LANDSAT digital data obtained from a single pass. Quantitative analyses were carried out in five test sites (approx 40 sq km each) and t tests showed that filtering with threshold values significantly decreased errors of commission and omission. In area estimation filtering improved the overestimate of 4.5% to 2.7% and the root-mean-square error decreased from 126.18 ha to 107.02 ha. Extrapolating the same procedure of automatic classification using spatial filtering for postclassification to the whole study area, the accuracy in area estimate was improved from the overestimate of 10.9% to 9.7%. It is concluded that when single pass LANDSAT data is used for crop identification and area estimation the postclassification procedure using a spatial filter provides a more accurate area estimate by reducing classification errors.

Integration of subclassification strategies in randomised controlled clinical trials evaluating manual therapy treatment and exercise therapy for non-specific chronic low back pain: a systematic review.

PubMed

Fersum, K V; Dankaerts, W; O'Sullivan, P B; Maes, J; Skouen, J S; Bjordal, J M; Kvåle, A

2010-11-01

There is lack of evidence for specific treatment interventions for patients with non-specific chronic low back pain (NSCLBP) despite the substantial amount of randomised controlled clinical trials evaluating treatment outcome for this disorder. It has been hypothesised that this vacuum of evidence is caused by the lack of subclassification of the heterogeneous population of patients with chronic low back pain for outcome research. A systematic review. A systematic review with a meta-analysis was undertaken to determine the integration of subclassification strategies with matched interventions in randomised controlled clinical trials evaluating manual therapy treatment and exercise therapy for NSCLBP. A structured search for relevant studies in Embase, Cinahl, Medline, PEDro and the Cochrane Trials Register database, followed by hand searching all relevant studies in English up to December 2008. Only 5 of 68 studies (7.4%) subclassified patients beyond applying general inclusion and exclusion criteria. In the few studies where classification and matched interventions have been used, our meta-analysis showed a statistical difference in favour of the classification-based intervention for reductions in pain (p=0.004) and disability (p=0.0005), both for short-term and long-term reduction in pain (p=0.001). Effect sizes ranged from moderate (0.43) for short term to minimal (0.14) for long term. A better integration of subclassification strategies in NSCLBP outcome research is needed. We propose the development of explicit recommendations for the use of subclassification strategies and evaluation of targeted interventions in future research evaluating NSCLBP.

Crowdsourcing as a screening tool to detect clinical features of glaucomatous optic neuropathy from digital photography.

PubMed

Mitry, Danny; Peto, Tunde; Hayat, Shabina; Blows, Peter; Morgan, James; Khaw, Kay-Tee; Foster, Paul J

2015-01-01

Crowdsourcing is the process of simplifying and outsourcing numerous tasks to many untrained individuals. Our aim was to assess the performance and repeatability of crowdsourcing in the classification of normal and glaucomatous discs from optic disc images. Optic disc images (N = 127) with pre-determined disease status were selected by consensus agreement from grading experts from a large cohort study. After reading brief illustrative instructions, we requested that knowledge workers (KWs) from a crowdsourcing platform (Amazon MTurk) classified each image as normal or abnormal. Each image was classified 20 times by different KWs. Two study designs were examined to assess the effect of varying KW experience and both study designs were conducted twice for consistency. Performance was assessed by comparing the sensitivity, specificity and area under the receiver operating characteristic curve (AUC). Overall, 2,540 classifications were received in under 24 hours at minimal cost. The sensitivity ranged between 83-88% across both trials and study designs, however the specificity was poor, ranging between 35-43%. In trial 1, the highest AUC (95%CI) was 0.64(0.62-0.66) and in trial 2 it was 0.63(0.61-0.65). There were no significant differences between study design or trials conducted. Crowdsourcing represents a cost-effective method of image analysis which demonstrates good repeatability and a high sensitivity. Optimisation of variables such as reward schemes, mode of image presentation, expanded response options and incorporation of training modules should be examined to determine their effect on the accuracy and reliability of this technique in retinal image analysis.

Evaluation of the Retrieval of Nuclear Science Document References Using the Universal Decimal Classification as the Indexing Language for a Computer-Based System

ERIC Educational Resources Information Center

Atherton, Pauline; And Others

A single issue of Nuclear Science Abstracts, containing about 2,300 abstracts, was indexed by Universal Decimal Classification (UDC) using the Special Subject Edition of UDC for Nuclear Science and Technology. The descriptive cataloging and UDC-indexing records formed a computer-stored data base. A systematic random sample of 500 additional…

Behavioral Variability, Learning Processes, and Creativity

DTIC Science & Technology

1990-09-01

Nursery Schools . ~ 9-10 y.o. subjects, at the concrete operative stage and coming from Primary Schools . - 14-15 y.o. subjects, at the formal thought...stage and coming from General Secondary Schools (no Technical School subject has been considered). - Adults, students at the University. Cognitive...classifications combine in a single situation, the operations of seiation and of classification, as approached in the classical Piaget’s procedures

Estimation and classification by sigmoids based on mutual information

NASA Technical Reports Server (NTRS)

Baram, Yoram

1994-01-01

An estimate of the probability density function of a random vector is obtained by maximizing the mutual information between the input and the output of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's s method, applied to an estimated density, yields a recursive maximum likelihood estimator, consisting of a single internal layer of sigmoids, for a random variable or a random sequence. Applications to the diamond classification and to the prediction of a sun-spot process are demonstrated.

Sexual Assault and Sexual Harassment in the U.S. Military: Annex to Volume 2. Tabular Results from the 2014 RAND Military Workplace Study for Department of Defense Service Members

DTIC Science & Technology

2015-01-01

and OB items as described in the report. For respondents with multiple assaults, classification is based on what happened in the most serious assault...respondents with a single assault, classification is based on answers to SA1–SA6, PF items, and OB items as described in the report. For respondents with...answers to SA1–SA6, PF items, and OB items as described in the report. For respondents with multiple assaults, classification is based on what happened

The Mistreatment of Major Depressive Disorder

PubMed Central

Paris, Joel

2014-01-01

Objective: To examine the effects of classification on treatment in major depressive disorder (MDD). Method: This is a narrative review. Results: MDD is a highly heterogeneous category, leading to problems in classification and in specificity of treatment. Current models classify all depressions within a single category. However, the construct of MDD obscures important differences between severe disorders that require pharmacotherapy, and mild-to-moderate disorders that can respond to psychotherapy or remit spontaneously. Patients with mild-to-moderate MDD are being treated with routine or overly aggressive pharmacotherapy. Conclusions: The current classification fails to address the heterogeneity of depression, leading to mistreatment. PMID:24881163

Staging Lung Cancer: Metastasis.

PubMed

Shroff, Girish S; Viswanathan, Chitra; Carter, Brett W; Benveniste, Marcelo F; Truong, Mylene T; Sabloff, Bradley S

2018-05-01

The updated eighth edition of the tumor, node, metastasis (TNM) classification for lung cancer includes revisions to T and M descriptors. In terms of the M descriptor, the classification of intrathoracic metastatic disease as M1a is unchanged from TNM-7. Extrathoracic metastatic disease, which was classified as M1b in TNM-7, is now subdivided into M1b (single metastasis, single organ) and M1c (multiple metastases in one or multiple organs) descriptors. In this article, the rationale for changes in the M descriptors, the utility of preoperative staging with PET/computed tomography, and the treatment options available for patients with oligometastatic disease are discussed. Copyright © 2018 Elsevier Inc. All rights reserved.

Classification of Magnetic Nanoparticle Systems—Synthesis, Standardization and Analysis Methods in the NanoMag Project

PubMed Central

Bogren, Sara; Fornara, Andrea; Ludwig, Frank; del Puerto Morales, Maria; Steinhoff, Uwe; Fougt Hansen, Mikkel; Kazakova, Olga; Johansson, Christer

2015-01-01

This study presents classification of different magnetic single- and multi-core particle systems using their measured dynamic magnetic properties together with their nanocrystal and particle sizes. The dynamic magnetic properties are measured with AC (dynamical) susceptometry and magnetorelaxometry and the size parameters are determined from electron microscopy and dynamic light scattering. Using these methods, we also show that the nanocrystal size and particle morphology determines the dynamic magnetic properties for both single- and multi-core particles. The presented results are obtained from the four year EU NMP FP7 project, NanoMag, which is focused on standardization of analysis methods for magnetic nanoparticles. PMID:26343639

The science of stakeholder engagement in research: classification, implementation, and evaluation.

PubMed

Goodman, Melody S; Sanders Thompson, Vetta L

2017-09-01

In this commentary, we discuss the science of stakeholder engagement in research. We propose a classification system with definitions to determine where projects lie on the stakeholder engagement continuum. We discuss the key elements of implementation and evaluation of stakeholder engagement in research posing key questions to consider when doing this work. We commend and critique the work of Hamilton et al. in their multilevel stakeholder engagement in a VA implementation trial of evidence-based quality improvement in women's health primary care. We also discuss the need for more work in this area to enhance the science of stakeholder engagement in research.

A Generalized Mixture Framework for Multi-label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We develop a novel probabilistic ensemble framework for multi-label classification that is based on the mixtures-of-experts architecture. In this framework, we combine multi-label classification models in the classifier chains family that decompose the class posterior distribution P(Y1, …, Yd|X) using a product of posterior distributions over components of the output space. Our approach captures different input–output and output–output relations that tend to change across data. As a result, we can recover a rich set of dependency relations among inputs and outputs that a single multi-label classification model cannot capture due to its modeling simplifications. We develop and present algorithms for learning the mixtures-of-experts models from data and for performing multi-label predictions on unseen data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods. PMID:26613069

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

NASA Astrophysics Data System (ADS)

Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

2010-12-01

We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

A highly sensitive search strategy for clinical trials in Literatura Latino Americana e do Caribe em Ciências da Saúde (LILACS) was developed.

PubMed

Manríquez, Juan J

2008-04-01

Systematic reviews should include as many articles as possible. However, many systematic reviews use only databases with high English language content as sources of trials. Literatura Latino Americana e do Caribe em Ciências da Saúde (LILACS) is an underused source of trials, and there is not a validated strategy for searching clinical trials to be used in this database. The objective of this study was to develop a sensitive search strategy for clinical trials in LILACS. An analytical survey was performed. Several single and multiple-term search strategies were tested for their ability to retrieve clinical trials in LILACS. Sensitivity, specificity, and accuracy of each single and multiple-term strategy were calculated using the results of a hand-search of 44 Chilean journals as gold standard. After combining the most sensitive, specific, and accurate single and multiple-term search strategy, a strategy with a sensitivity of 97.75% (95% confidence interval [CI]=95.98-99.53) and a specificity of 61.85 (95% CI=61.19-62.51) was obtained. LILACS is a source of trials that could improve systematic reviews. A new highly sensitive search strategy for clinical trials in LILACS has been developed. It is hoped this search strategy will improve and increase the utilization of LILACS in future systematic reviews.

Single-particle cryo-EM using alignment by classification (ABC): the structure of Lumbricus terrestris haemoglobin.

PubMed

Afanasyev, Pavel; Seer-Linnemayr, Charlotte; Ravelli, Raimond B G; Matadeen, Rishi; De Carlo, Sacha; Alewijnse, Bart; Portugal, Rodrigo V; Pannu, Navraj S; Schatz, Michael; van Heel, Marin

2017-09-01

Single-particle cryogenic electron microscopy (cryo-EM) can now yield near-atomic resolution structures of biological complexes. However, the reference-based alignment algorithms commonly used in cryo-EM suffer from reference bias, limiting their applicability (also known as the 'Einstein from random noise' problem). Low-dose cryo-EM therefore requires robust and objective approaches to reveal the structural information contained in the extremely noisy data, especially when dealing with small structures. A reference-free pipeline is presented for obtaining near-atomic resolution three-dimensional reconstructions from heterogeneous ('four-dimensional') cryo-EM data sets. The methodologies integrated in this pipeline include a posteriori camera correction, movie-based full-data-set contrast transfer function determination, movie-alignment algorithms, (Fourier-space) multivariate statistical data compression and unsupervised classification, 'random-startup' three-dimensional reconstructions, four-dimensional structural refinements and Fourier shell correlation criteria for evaluating anisotropic resolution. The procedures exclusively use information emerging from the data set itself, without external 'starting models'. Euler-angle assignments are performed by angular reconstitution rather than by the inherently slower projection-matching approaches. The comprehensive 'ABC-4D' pipeline is based on the two-dimensional reference-free 'alignment by classification' (ABC) approach, where similar images in similar orientations are grouped by unsupervised classification. Some fundamental differences between X-ray crystallography versus single-particle cryo-EM data collection and data processing are discussed. The structure of the giant haemoglobin from Lumbricus terrestris at a global resolution of ∼3.8 Å is presented as an example of the use of the ABC-4D procedure.

A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates.

PubMed

Dimitriadis, Stavros I; Salis, Christos; Linden, David

2018-04-01

Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels have long been recognized. Manual staging is resource intensive and time consuming, and thus considerable effort must be spent to ensure inter-rater reliability. As a result, there is a great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC). In this paper, we present a single-EEG-sensor ASSC technique based on the dynamic reconfiguration of different aspects of cross-frequency coupling (CFC) estimated between predefined frequency pairs over 5 s epoch lengths. The proposed analytic scheme is demonstrated using the PhysioNet Sleep European Data Format (EDF) Database with repeat recordings from 20 healthy young adults. We validate our methodology in a second sleep dataset. We achieved very high classification sensitivity, specificity and accuracy of 96.2 ± 2.2%, 94.2 ± 2.3%, and 94.4 ± 2.2% across 20 folds, respectively, and also a high mean F1 score (92%, range 90-94%) when a multi-class Naive Bayes classifier was applied. High classification performance has been achieved also in the second sleep dataset. Our method outperformed the accuracy of previous studies not only on different datasets but also on the same database. Single-sensor ASSC makes the entire methodology appropriate for longitudinal monitoring using wearable EEG in real-world and laboratory-oriented environments. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

A novel single neuron perceptron with universal approximation and XOR computation properties.

PubMed

Lotfi, Ehsan; Akbarzadeh-T, M-R

2014-01-01

We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain's nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine) pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP) with gradient decent backpropagation (GDBP) learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification.

Trial latencies estimation of event-related potentials in EEG by means of genetic algorithms

NASA Astrophysics Data System (ADS)

Da Pelo, P.; De Tommaso, M.; Monaco, A.; Stramaglia, S.; Bellotti, R.; Tangaro, S.

2018-04-01

Objective. Event-related potentials (ERPs) are usually obtained by averaging thus neglecting the trial-to-trial latency variability in cognitive electroencephalography (EEG) responses. As a consequence the shape and the peak amplitude of the averaged ERP are smeared and reduced, respectively, when the single-trial latencies show a relevant variability. To date, the majority of the methodologies for single-trial latencies inference are iterative schemes providing suboptimal solutions, the most commonly used being the Woody’s algorithm. Approach. In this study, a global approach is developed by introducing a fitness function whose global maximum corresponds to the set of latencies which renders the trial signals most aligned as possible. A suitable genetic algorithm has been implemented to solve the optimization problem, characterized by new genetic operators tailored to the present problem. Main results. The results, on simulated trials, showed that the proposed algorithm performs better than Woody’s algorithm in all conditions, at the cost of an increased computational complexity (justified by the improved quality of the solution). Application of the proposed approach on real data trials, resulted in an increased correlation between latencies and reaction times w.r.t. the output from RIDE method. Significance. The above mentioned results on simulated and real data indicate that the proposed method, providing a better estimate of single-trial latencies, will open the way to more accurate study of neural responses as well as to the issue of relating the variability of latencies to the proper cognitive and behavioural correlates.

Genomic Selection in Multi-environment Crop Trials.

PubMed

Oakey, Helena; Cullis, Brian; Thompson, Robin; Comadran, Jordi; Halpin, Claire; Waugh, Robbie

2016-05-03

Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based design terms. It considers marker, and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and nonmarker residual genetic variation (i.e., additive and nonadditive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from trials, in 2 different years, of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analyzing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment trial (MET) model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to a single-year standard model run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers. Copyright © 2016 Oakey et al.

Optimal weighted averaging of event related activity from acquisitions with artifacts.

PubMed

Vollero, Luca; Petrichella, Sara; Innello, Giulio

2016-08-01

In several biomedical applications that require the signal processing of biological data, the starting procedure for noise reduction is the ensemble averaging of multiple repeated acquisitions (trials). This method is based on the assumption that each trial is composed of two additive components: (i) a time-locked activity related to some sensitive/stimulation phenomenon (ERA, Event Related Activity in the following) and (ii) a sum of several other non time-locked background activities. The averaging aims at estimating the ERA activity under very low Signal to Noise and Interference Ratio (SNIR). Although averaging is a well established tool, its performance can be improved in the presence of high-power disturbances (artifacts) by a trials classification and removal stage. In this paper we propose, model and evaluate a new approach that avoids trials removal, managing trials classified as artifact-free and artifact-prone with two different weights. Based on the model, a weights tuning is possible and through modeling and simulations we show that, when optimally configured, the proposed solution outperforms classical approaches.

Automatic Selection of Clinical Trials Based on A Semantic Web Approach.

PubMed

Cuggia, Marc; Campillo-Gimenez, Boris; Bouzille, Guillaume; Besana, Paolo; Jouini, Wassim; Dufour, Jean-Charles; Zekri, Oussama; Gibaud, Isabelle; Garde, Cyril; Duvauferier, Regis

2015-01-01

Recruitment of patients in clinical trials is nowadays preoccupying, as the inclusion rate is particularly low. The main identified factors are the multiplicity of open clinical trials, the high number and complexity of eligibility criteria, and the additional workload that a systematic search of the clinical trials a patient could be enrolled in for a physician. The principal objective of the ASTEC project is to automate the prescreening phase during multidisciplinary meetings (MDM). This paper presents the evaluation of a computerized recruitment support systems (CRSS) based on semantic web approach. The evaluation of the system was based on data collected retrospectively from a 6 month period of MDM in Urology and on 4 clinical trials of prostate cancer. The classification performance of the ASTEC system had a precision of 21%, recall of 93%, and an error rate equal to 37%. Missing data was the main issue encountered. The system was designed to be both scalable to other clinical domains and usable during MDM process.

Preprocessing and meta-classification for brain-computer interfaces.

PubMed

Hammon, Paul S; de Sa, Virginia R

2007-03-01

A brain-computer interface (BCI) is a system which allows direct translation of brain states into actions, bypassing the usual muscular pathways. A BCI system works by extracting user brain signals, applying machine learning algorithms to classify the user's brain state, and performing a computer-controlled action. Our goal is to improve brain state classification. Perhaps the most obvious way to improve classification performance is the selection of an advanced learning algorithm. However, it is now well known in the BCI community that careful selection of preprocessing steps is crucial to the success of any classification scheme. Furthermore, recent work indicates that combining the output of multiple classifiers (meta-classification) leads to improved classification rates relative to single classifiers (Dornhege et al., 2004). In this paper, we develop an automated approach which systematically analyzes the relative contributions of different preprocessing and meta-classification approaches. We apply this procedure to three data sets drawn from BCI Competition 2003 (Blankertz et al., 2004) and BCI Competition III (Blankertz et al., 2006), each of which exhibit very different characteristics. Our final classification results compare favorably with those from past BCI competitions. Additionally, we analyze the relative contributions of individual preprocessing and meta-classification choices and discuss which types of BCI data benefit most from specific algorithms.

Ice/water Classification of Sentinel-1 Images

NASA Astrophysics Data System (ADS)

Korosov, Anton; Zakhvatkina, Natalia; Muckenhuber, Stefan

2015-04-01

Sea Ice monitoring and classification relies heavily on synthetic aperture radar (SAR) imagery. These sensors record data either only at horizontal polarization (RADARSAT-1) or vertically polarized (ERS-1 and ERS-2) or at dual polarization (Radarsat-2, Sentinel-1). Many algorithms have been developed to discriminate sea ice types and open water using single polarization images. Ice type classification, however, is still ambiguous in some cases. Sea ice classification in single polarization SAR images has been attempted using various methods since the beginning of the ERS programme. The robust classification using only SAR images that can provide useful results under varying sea ice types and open water tend to be not generally applicable in operational regime. The new generation SAR satellites have capability to deliver images in several polarizations. This gives improved possibility to develop sea ice classification algorithms. In this study we use data from Sentinel-1 at dual-polarization, i.e. HH (horizontally transmitted and horizontally received) and HV (horizontally transmitted, vertically received). This mode assembles wide SAR image from several narrower SAR beams, resulting to an image of 500 x 500 km with 50 m resolution. A non-linear scheme for classification of Sentinel-1 data has been developed. The processing allows to identify three classes: ice, calm water and rough water at 1 km spatial resolution. The raw sigma0 data in HH and HV polarization are first corrected for thermal and random noise by extracting the background thermal noise level and smoothing the image with several filters. At the next step texture characteristics are computed in a moving window using a Gray Level Co-occurence Matrix (GLCM). A neural network is applied at the last step for processing array of the most informative texture characteristics and ice/water classification. The main results are: * the most informative texture characteristics to be used for sea ice classification were revealed; * the best set of parameters including the window size, number of levels of quantization of sigma0 values and co-occurence distance was found; * a support vector machine (SVM) was trained on results of visual classification of 30 Sentinel-1 images. Despite the general high accuracy of the neural network (95% of true positive classification) problems with classification of young newly formed ice and rough water arise due to the similar average backscatter and texture. Other methods of smoothing and computation of texture characteristics (e.g. computation of GLCM from a variable size window) is assessed. The developed scheme will be utilized in NRT processing of Sentinel-1 data at NERSC within the MyOcean2 project.

Evaluation of the ACR and SLICC classification criteria in juvenile-onset systemic lupus erythematosus: a longitudinal analysis.

PubMed

Lythgoe, H; Morgan, T; Heaf, E; Lloyd, O; Al-Abadi, E; Armon, K; Bailey, K; Davidson, J; Friswell, M; Gardner-Medwin, J; Haslam, K; Ioannou, Y; Leahy, A; Leone, V; Pilkington, C; Rangaraj, S; Riley, P; Tizard, E J; Wilkinson, N; Beresford, M W

2017-10-01

Objectives The Systemic Lupus International Collaborating Clinics (SLICC) group proposed revised classification criteria for systemic lupus erythematosus (SLICC-2012 criteria). This study aimed to compare these criteria with the well-established American College of Rheumatology classification criteria (ACR-1997 criteria) in a national cohort of juvenile-onset systemic lupus erythematosus (JSLE) patients and evaluate how patients' classification criteria evolved over time. Methods Data from patients in the UK JSLE Cohort Study with a senior clinician diagnosis of probable evolving, or definite JSLE, were analyzed. Patients were assessed using both classification criteria within 1 year of diagnosis and at latest follow up (following a minimum 12-month follow-up period). Results A total of 226 patients were included. The SLICC-2012 was more sensitive than ACR-1997 at diagnosis (92.9% versus 84.1% p < 0.001) and after follow up (100% versus 92.0% p < 0.001). Most patients meeting the SLICC-2012 criteria and not the ACR-1997 met more than one additional criterion on the SLICC-2012. Conclusions The SLICC-2012 was better able to classify patients with JSLE than the ACR-1997 and did so at an earlier stage in their disease course. SLICC-2012 should be considered for classification of JSLE patients in observational studies and clinical trial eligibility.

Photon antibunching from a single lithographically defined InGaAs/GaAs quantum dot.

PubMed

Verma, V B; Stevens, Martin J; Silverman, K L; Dias, N L; Garg, A; Coleman, J J; Mirin, R P

2011-02-28

We demonstrate photon antibunching from a single lithographically defined quantum dot fabricated by electron beam lithography, wet chemical etching, and overgrowth of the barrier layers by metalorganic chemical vapor deposition. Measurement of the second-order autocorrelation function indicates g(2)(0)=0.395±0.030, below the 0.5 limit necessary for classification as a single photon source.

Probing Enzyme-Surface Interactions via Protein Engineering and Single-Molecule Techniques

DTIC Science & Technology

2017-06-26

SECURITY CLASSIFICATION OF: The overall objective of this research was to exploit protein engineering and fluorescence single-molecule methods to... Engineering and Single-Molecule Techniques The views, opinions and/or findings contained in this report are those of the author(s) and should not...Status: Technology Transfer: Report Date: 1 FINAL REPORT Project Title: Probing Enzyme-Surface Interactions via Protein Engineering and

Family Ophioviridae: classification and features

USDA-ARS?s Scientific Manuscript database

The Ophioviridae is a family of filamentous plant viruses, with a single stranded negative, and possibly ambisense, RNA genome of 11.3–12.5 kb divided into 3–4 segments, each encapsidated by a single coat protein. Virions are naked filamentous nucleocapsids, forming kinked circles of at least two d...

39 CFR Appendix A to Subpart A of... - Mail Classification Schedule

Code of Federal Regulations, 2012 CFR

2012-07-01

... Density and Saturation Letters High Density and Saturation Flats/Parcels Carrier Route Letters Flats Not... Package Services Single-Piece Parcel Post Inbound Surface Parcel Post (at UPU rates) Bound Printed Matter... Single-Piece First-Class Mail International Standard Mail (Regular and Nonprofit) High Density and...

Chelation therapy to treat atherosclerosis, particularly in diabetes: Is it time to reconsider?

PubMed Central

Lamas, Gervasio A; Ergui, Ian

2016-01-01

Summary Reports and case series have suggested a possible beneficial effect of chelation therapy in patients with atherosclerotic disease. Small randomized trials conducted in patients with angina or peripheral artery disease, however, were not sufficiently powered to provide conclusive evidence on clinical outcomes. The Trial to Assess Chelation Therapy (TACT) was the first randomized trial adequately powered to detect the effects of chelation therapy on clinical endpoints. Chelation reduced adverse cardiovascular events in a post myocardial infarction (MI) population. Patients with diabetes demonstrated even greater benefit, with a number needed to treat of 6.5 patients to prevent a cardiac event over 5 years. These results led to the revision of the ACC/AHA guideline recommendations for chelation therapy, changing its classification from class III to class IIb. TACT2, a replicative trial, will assess the effects of chelation therapy on cardiovascular outcomes in diabetic patients with a prior myocardial infarction. PMID:27149141

Multiple Component Event-Related Potential (mcERP) Estimation

NASA Technical Reports Server (NTRS)

Knuth, K. H.; Clanton, S. T.; Shah, A. S.; Truccolo, W. A.; Ding, M.; Bressler, S. L.; Trejo, L. J.; Schroeder, C. E.; Clancy, Daniel (Technical Monitor)

2002-01-01

We show how model-based estimation of the neural sources responsible for transient neuroelectric signals can be improved by the analysis of single trial data. Previously, we showed that a multiple component event-related potential (mcERP) algorithm can extract the responses of individual sources from recordings of a mixture of multiple, possibly interacting, neural ensembles. McERP also estimated single-trial amplitudes and onset latencies, thus allowing more accurate estimation of ongoing neural activity during an experimental trial. The mcERP algorithm is related to informax independent component analysis (ICA); however, the underlying signal model is more physiologically realistic in that a component is modeled as a stereotypic waveshape varying both in amplitude and onset latency from trial to trial. The result is a model that reflects quantities of interest to the neuroscientist. Here we demonstrate that the mcERP algorithm provides more accurate results than more traditional methods such as factor analysis and the more recent ICA. Whereas factor analysis assumes the sources are orthogonal and ICA assumes the sources are statistically independent, the mcERP algorithm makes no such assumptions thus allowing investigators to examine interactions among components by estimating the properties of single-trial responses.

Fixed-dose combinations of drugs versus single-drug formulations for treating pulmonary tuberculosis

PubMed Central

Gallardo, Carmen R; Rigau Comas, David; Valderrama Rodríguez, Angélica; Roqué i Figuls, Marta; Parker, Lucy Anne; Caylà, Joan; Bonfill Cosp, Xavier

2016-01-01

Background People who are newly diagnosed with pulmonary tuberculosis (TB) typically receive a standard first-line treatment regimen that consists of two months of isoniazid, rifampicin, pyrazinamide, and ethambutol followed by four months of isoniazid and rifampicin. Fixed-dose combinations (FDCs) of these drugs are widely recommended. Objectives To compare the efficacy, safety, and acceptability of anti-tuberculosis regimens given as fixed-dose combinations compared to single-drug formulations for treating people with newly diagnosed pulmonary tuberculosis. Search methods We searched the Cochrane Infectious Disease Group Specialized Register; the Cochrane Central Register of Controlled Trials (CENTRAL, published in the Cochrane Library, Issue 11 2015); MEDLINE (1966 to 20 November 2015); EMBASE (1980 to 20 November 2015); LILACS (1982 to 20 November 2015); the metaRegister of Controlled Trials; and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP), without language restrictions, up to 20 November 2015. Selection criteria Randomized controlled trials that compared the use of FDCs with single-drug formulations in adults (aged 15 years or more) newly diagnosed with pulmonary TB. Data collection and analysis Two review authors independently assessed studies for inclusion, and assessed the risk of bias and extracted data from the included trials. We used risk ratios (RRs) for dichotomous data and mean differences (MDs) for continuous data with 95% confidence intervals (CIs). We attempted to assess the effect of treatment for time-to-event measures with hazard ratios and their 95% CIs. We used the Cochrane 'Risk of bias' assessment tool to determine the risk of bias in included trials. We used the fixed-effect model when there was little heterogeneity and the random-effects model with moderate heterogeneity. We used an I² statistic value of 75% or greater to denote significant heterogeneity, in which case we did not perform a meta-analysis. We assessed the quality of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Main results We included 13 randomized controlled trials (RCTs) in the review, which enrolled 5824 participants. Trials were published between 1987 and 2015 and included participants in treatment with newly diagnosed pulmonary TB in countries with high TB prevalence. Only two trials reported the HIV status of included participants. Overall there is little or no difference detected between FDCs and single-drug formulations for most outcomes reported. We did not detect a difference in treatment failure between FDCs compared with single-drug formulations (RR 1.28, 95% CI 0.82 to 2.00; 3606 participants, seven trials, moderate quality evidence). Relapse may be more frequent in people treated with FDCs compared to single-drug formulations, although the confidence interval (CI) includes no difference (RR 1.28, 95% CI 1.00 to 1.64; 3621 participants, 10 trials, low quality evidence). We did not detect any difference in death between fixed-dose and single-drug formulation groups (RR 0.96, 95% CI 0.67 to 1.39; 4800 participants, 11 trials, moderate quality evidence). When we compared FDCs with single-drug formulations we found little or no difference for sputum smear or culture conversion at the end of treatment (RR 0.99, 95% CI 0.96 to 1.02; 2319 participants, seven trials, high quality evidence), for serious adverse events (RR 1.45, 95% CI 0.90 to 2.33; 3388 participants, six trials, moderate quality evidence), and for adverse events that led to discontinuation of therapy (RR 0.96, 95% CI 0.56 to 1.66; 5530 participants, 13 trials, low quality evidence). We conducted a sensitivity analysis excluding studies at high risk of bias and this did not alter the review findings. Authors' conclusions Fixed-dose combinations and single-drug formulations probably have similar effects for treating people with newly diagnosed pulmonary TB. PLAIN LANGUAGE SUMMARY Fixed-dose combinations for treating pulmonary tuberculosis What are fixed-dose combinations and how might they improve care of people with tuberculosis Tuberculosis (TB) is an important health problem, especially in developing countries. The treatment for pulmonary TB in new patients includes four oral medicines taken for six months, sometimes as fixed-dose combinations (FDCs) that are combined in one tablet, or taken separately as single-drug formulations. The World Health Organization recommends prescribers use fixed-dose combinations to reduce the number of tablets that people take. On the supply side, this might reduce prescribing errors and improve drug supply efficiency; on the patient's side, FDCS simplify treatment and improve adherence. We conducted a review to assess the efficacy, safety, and acceptability of FDCs compared with single-drug formulations for treating people with newly diagnosed pulmonary TB. What the research says We searched for relevant trials up to 20 November 2015, and included 13 randomized controlled trials that enrolled 5824 people. Trials were published between 1987 and 2015 and included participants in treatment with newly diagnosed pulmonary TB in countries with high TB prevalence. Only two trials reported the HIV status of included participants. There is probably little or no difference in FDCs compared to single-drug formulations for treatment failure (moderate quality evidence); relapse may be more frequent (low quality evidence); and the number of deaths were similar (moderate quality evidence). There is little or no difference in sputum smear or culture conversion (high quality evidence), and no difference was shown for serious adverse events (moderate quality evidence) or adverse events that led to discontinuation of therapy (low quality evidence). Authors' conclusions We concluded that fixed-dose combinations have similar efficacy to single-drug formulations for treating people with newly diagnosed pulmonary TB. PMID:27186634

Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase

NASA Astrophysics Data System (ADS)

Zink, Rob; Hunyadi, Borbála; Van Huffel, Sabine; De Vos, Maarten

2016-04-01

Objective. One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. Approach. We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. Main results. The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. Significance. The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.

Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase.

PubMed

Zink, Rob; Hunyadi, Borbála; Huffel, Sabine Van; Vos, Maarten De

2016-04-01

One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.

Effects of Recent Exposure to a Conditioned Stimulus on Extinction of Pavlovian Fear Conditioning

ERIC Educational Resources Information Center

Chan, Wan Yee Macy; Leung, Hiu T.; Westbrook, R. Frederick; McNally, Gavan P.

2010-01-01

In six experiments we studied the effects of a single re-exposure to a conditioned stimulus (CS; "retrieval trial") prior to extinction training (extinction-reconsolidation boundary) on the development of and recovery from fear extinction. A single retrieval trial prior to extinction training significantly augmented the renewal and reinstatement…

Neural Prediction of Multidimensional Decisions in Monkey Superior Colliculus

NASA Astrophysics Data System (ADS)

Hasegawa, Ryohei P.; Hasegawa, Yukako T.; Segraves, Mark A.

To examine the function of the superior colliculus (SC) in decision-making processes and the application of its single trial activity for “neural mind reading,” we recorded from SC deep layers while two monkeys performed oculomotor go/no-go tasks. We have recently focused on monitoring single trial activities in single SC neurons, and designed a virtual decision function (VDF) to provide a good estimation of single-dimensional decisions (go/no-go decisions for a cue presented at a specific visual field, a response field of each neuron). In this study, we used two VDFs for multidimensional decisions (go/no-go decisions at two cue locations) with the ensemble activity which was simultaneously recorded from a small group (4 to 6) of neurons at both sides of the SC. VDFs predicted cue locations as well as go/no-go decisions. These results suggest that monitoring of ensemble SC activity had sufficient capacity to predict multidimensional decisions on a trial-by-trial basis, which is an ideal candidate to serve for cognitive brain-machine interfaces (BMI) such as two-dimensional word spellers.

Classification of Kiwifruit Grades Based on Fruit Shape Using a Single Camera

PubMed Central

Fu, Longsheng; Sun, Shipeng; Li, Rui; Wang, Shaojin

2016-01-01

This study aims to demonstrate the feasibility for classifying kiwifruit into shape grades by adding a single camera to current Chinese sorting lines equipped with weight sensors. Image processing methods are employed to calculate fruit length, maximum diameter of the equatorial section, and projected area. A stepwise multiple linear regression method is applied to select significant variables for predicting minimum diameter of the equatorial section and volume and to establish corresponding estimation models. Results show that length, maximum diameter of the equatorial section and weight are selected to predict the minimum diameter of the equatorial section, with the coefficient of determination of only 0.82 when compared to manual measurements. Weight and length are then selected to estimate the volume, which is in good agreement with the measured one with the coefficient of determination of 0.98. Fruit classification based on the estimated minimum diameter of the equatorial section achieves a low success rate of 84.6%, which is significantly improved using a linear combination of the length/maximum diameter of the equatorial section and projected area/length ratios, reaching 98.3%. Thus, it is possible for Chinese kiwifruit sorting lines to reach international standards of grading kiwifruit on fruit shape classification by adding a single camera. PMID:27376292

Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection

PubMed Central

Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

2014-01-01

Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices. PMID:25177107

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning

PubMed Central

Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi

2017-01-01

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization. PMID:28786986

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

PubMed

Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi; Mao, Youdong

2017-01-01

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection.

PubMed

Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

2014-11-01

Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices.

Trifactorial classification system for osteotome sinus floor elevation based on an observational retrospective analysis of 926 implants followed up to 10 years.

PubMed

French, David; Nadji, Nabil; Liu, Shawn X; Larjava, Hannu

2015-06-01

A novel osteotome trifactorial classification system is proposed for transcrestal osteotome-mediated sinus floor elevation (OSFE) sites that includes residual bone height (RBH), sinus floor anatomy (contour), and multiple versus single sites OSFE (tenting). An analysis of RBH, contour, and tenting was retrospectively applied to a cohort of 926 implants placed using OSFE without added bone graft and followed up to 10 years. RBH was divided into three groups: high (RBH > 6 mm), mid (RBH = 4.1 to 6 mm), and low (RBH = 2 to 4 mm). The sinus "contour" was divided into four groups: flat, concave, angle, and septa. For "tenting", single versus multiple adjacent OSFE sites were compared. The prevalence of flat sinus floors increased as RBH decreased. RBH was a significant predictor of failure with rates as follows: low- RBH = 5.1%, mid-RBH = 1.5%, and high-RBH = 0.4%. Flat sinus floors and single sites as compared to multiple sites had higher observed failure rates but neither achieved statistical significance; however, the power of the study was limited by low numbers of failures. The osteotome trifactorial classification system as proposed can assist planning OSFE cases and may allow better comparison of future OSFE studies.

An ensemble predictive modeling framework for breast cancer classification.

PubMed

Nagarajan, Radhakrishnan; Upreti, Meenakshi

2017-12-01

Molecular changes often precede clinical presentation of diseases and can be useful surrogates with potential to assist in informed clinical decision making. Recent studies have demonstrated the usefulness of modeling approaches such as classification that can predict the clinical outcomes from molecular expression profiles. While useful, a majority of these approaches implicitly use all molecular markers as features in the classification process often resulting in sparse high-dimensional projection of the samples often comparable to that of the sample size. In this study, a variant of the recently proposed ensemble classification approach is used for predicting good and poor-prognosis breast cancer samples from their molecular expression profiles. In contrast to traditional single and ensemble classifiers, the proposed approach uses multiple base classifiers with varying feature sets obtained from two-dimensional projection of the samples in conjunction with a majority voting strategy for predicting the class labels. In contrast to our earlier implementation, base classifiers in the ensembles are chosen based on maximal sensitivity and minimal redundancy by choosing only those with low average cosine distance. The resulting ensemble sets are subsequently modeled as undirected graphs. Performance of four different classification algorithms is shown to be better within the proposed ensemble framework in contrast to using them as traditional single classifier systems. Significance of a subset of genes with high-degree centrality in the network abstractions across the poor-prognosis samples is also discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

Glomerular disease: why is there a dearth of high quality clinical trials?

PubMed

Leaf, David E; Appel, Gerald B; Radhakrishnan, Jai

2010-08-01

There is a paucity of high quality clinical trials in glomerular disease, particularly in non-diabetic kidney disease. The aims of this review include quantifying the extent of this problem and exploring reasons for the scarcity of such trials in primary glomerular disease, with an emphasis on immunoglobulin A nephropathy, minimal change disease, focal segmental glomerulosclerosis, and membranous nephropathy in comparison with the more common diseases of diabetic nephropathy and lupus nephritis. Reasons for the dearth of high quality clinical trials in primary glomerular disease include (1) low prevalence of disease; (2) variability in clinical presentation; (3) variability in treatment response; (4) lack of consensus in definitions; (5) difficulty in recruiting patients; (6) high costs of randomized controlled trials; and (7) lack of collaborative efforts. To facilitate greater numbers of high quality clinical trials in glomerular disease, practice guidelines should establish common classification systems of disease and common clinical end points, industry and non-industry sponsored research should find common ground and work together toward advancing science, and national registries should be created to encourage collaborations across institutions and across nations.

A Classification of Lifts in Dance: Terminology and Biomechanical Principles

ERIC Educational Resources Information Center

Lafortune, Sylvain

2008-01-01

Despite the importance of lifts in Western theatrical dance, few reports have been published on the subject and few techniques established as good practice. Dancers usually learn partnering by trial and error, an approach that elicits both spectacular and inefficient results. To establish safer partnering practices, more efficient use of rehearsal…

Effects of Representative Status and Decision Style on Cooperation in the Prisoner's Dilemma.

ERIC Educational Resources Information Center

Hermann, Margaret G.; Kogan, Nathan

Level of cooperation in the Prisoner's Dilemma (PD) is examined for opponents acting in their own behalf or as members of a reference group consisting of strangers or friends. This subject classification interacted with trials, representatives of friend groups manifesting a consistently high level of cooperation throughout, and representatives of…

Retention of the "first-trial effect" in gait-slip among community-living older adults.

PubMed

Liu, Xuan; Bhatt, Tanvi; Wang, Shuaijie; Yang, Feng; Pai, Yi-Chung Clive

2017-02-01

"First-trial effect" characterizes the rapid adaptive behavior that changes the performance outcome (from fall to non-fall) after merely a single exposure to postural disturbance. The purpose of this study was to investigate how long the first-trial effect could last. Seventy-five (≥ 65 years) community-dwelling older adults, who were protected by an overhead full body harness system, were retested for a single slip 6-12 months after their initial exposure to a single gait-slip. Subjects' body kinematics that was used to compute their proactive (feedforward) and reactive (feedback) control of stability was recorded by an eight-camera motion analysis system. We found the laboratory falls of subjects on their retest slip were significantly lower than that on the novel initial slip, and the reactive stability of these subjects was also significantly improved. However, the proactive stability of subjects remains unchanged between their initial slip and retest slip. The fall rates and stability control had no difference among the 6-, 9-, and 12-month retest groups, which indicated a maximum retention on 12 months after a single slip in the laboratory. These results highlighted the importance of the "first-trial effect" and suggested that perturbation training is effective for fall prevention, with lower trial doses for a long period (up to 1 year). Therefore, single slip training might benefit those older adults who could not tolerate larger doses in reality.