A deep convolutional neural network model to classify heartbeats.

PubMed

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adam, Muhammad; Gertych, Arkadiusz; Tan, Ru San

2017-10-01

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats. Copyright © 2017 Elsevier Ltd. All rights reserved.

Electromagnetic Interference of Wireless Local Area Network on Electrocardiogram Monitoring System: A Case Report

PubMed Central

Chung, Seungmin; Yi, Joohee

2013-01-01

Electromagnetic interference (EMI) can affect various medical devices. Herein, we report the case of EMI from wireless local area network (WLAN) on an electrocardiogram (ECG) monitoring system. A patient who had a prior myocardial infarction participated in the cardiac rehabilitation program in the sports medicine center of our hospital under the wireless ECG monitoring system. After WLAN was installed, wireless ECG monitoring system failed to show a proper ECG signal. ECG signal was distorted when WLAN was turned on, but it was normalized after turning off the WLAN. PMID:23613696

Design of Secure ECG-Based Biometric Authentication in Body Area Sensor Networks

PubMed Central

Peter, Steffen; Pratap Reddy, Bhanu; Momtaz, Farshad; Givargis, Tony

2016-01-01

Body area sensor networks (BANs) utilize wireless communicating sensor nodes attached to a human body for convenience, safety, and health applications. Physiological characteristics of the body, such as the heart rate or Electrocardiogram (ECG) signals, are promising means to simplify the setup process and to improve security of BANs. This paper describes the design and implementation steps required to realize an ECG-based authentication protocol to identify sensor nodes attached to the same human body. Therefore, the first part of the paper addresses the design of a body-area sensor system, including the hardware setup, analogue and digital signal processing, and required ECG feature detection techniques. A model-based design flow is applied, and strengths and limitations of each design step are discussed. Real-world measured data originating from the implemented sensor system are then used to set up and parametrize a novel physiological authentication protocol for BANs. The authentication protocol utilizes statistical properties of expected and detected deviations to limit the number of false positive and false negative authentication attempts. The result of the described holistic design effort is the first practical implementation of biometric authentication in BANs that reflects timing and data uncertainties in the physical and cyber parts of the system. PMID:27110785

Design of Secure ECG-Based Biometric Authentication in Body Area Sensor Networks.

PubMed

Peter, Steffen; Reddy, Bhanu Pratap; Momtaz, Farshad; Givargis, Tony

2016-04-22

Body area sensor networks (BANs) utilize wireless communicating sensor nodes attached to a human body for convenience, safety, and health applications. Physiological characteristics of the body, such as the heart rate or Electrocardiogram (ECG) signals, are promising means to simplify the setup process and to improve security of BANs. This paper describes the design and implementation steps required to realize an ECG-based authentication protocol to identify sensor nodes attached to the same human body. Therefore, the first part of the paper addresses the design of a body-area sensor system, including the hardware setup, analogue and digital signal processing, and required ECG feature detection techniques. A model-based design flow is applied, and strengths and limitations of each design step are discussed. Real-world measured data originating from the implemented sensor system are then used to set up and parametrize a novel physiological authentication protocol for BANs. The authentication protocol utilizes statistical properties of expected and detected deviations to limit the number of false positive and false negative authentication attempts. The result of the described holistic design effort is the first practical implementation of biometric authentication in BANs that reflects timing and data uncertainties in the physical and cyber parts of the system.

Performance study of the wearable one-lead wireless electrocardiographic monitoring system.

PubMed

Hong, Sungyoup; Yang, Yougmo; Kim, Seunghwan; Shin, Seungcheol; Lee, Inbum; Jang, Yongwon; Kim, Kiseong; Yi, Hwayeon

2009-03-01

This study attempts to compare and assess the performance of a wearable electrocardiogram (ECG) using a sensing fabric electrode and a Bluetooth network with a conventional ECG. A one-lead ECG examination was performed using Bioshirt and an iWorx 214 while walking or running at 3, 6, and 9 km per hour. A correlation coefficient of a heart rate variability (HRV) between these two devices was higher than 0.96 and power spectral density of HRV measured also showed an excellent agreement. Thus, both of these two ECG devices showed similar detection capability for R peaks. The measured values for wave duration and intervals of both devices concur with each other. The intensity of noise is controversial. The ECG device using a sensing fabric electrode and a wireless network showed an ECG signal detection and transmission capability similar to that of a conventional ECG device.

Steganography in arrhythmic electrocardiogram signal.

PubMed

Edward Jero, S; Ramu, Palaniappan; Ramakrishnan, S

2015-08-01

Security and privacy of patient data is a vital requirement during exchange/storage of medical information over communication network. Steganography method hides patient data into a cover signal to prevent unauthenticated accesses during data transfer. This study evaluates the performance of ECG steganography to ensure secured transmission of patient data where an abnormal ECG signal is used as cover signal. The novelty of this work is to hide patient data into two dimensional matrix of an abnormal ECG signal using Discrete Wavelet Transform and Singular Value Decomposition based steganography method. A 2D ECG is constructed according to Tompkins QRS detection algorithm. The missed R peaks are computed using RR interval during 2D conversion. The abnormal ECG signals are obtained from the MIT-BIH arrhythmia database. Metrics such as Peak Signal to Noise Ratio, Percentage Residual Difference, Kullback-Leibler distance and Bit Error Rate are used to evaluate the performance of the proposed approach.

[Low-power Wireless Micro Ambulatory Electrocardiogram Node].

PubMed

Cai, Zhipeng; Luo, Kan; Li, Jianqing

2016-02-01

Ambulatory electrocardiogram (ECG) monitoring can effectively reduce the risk and death rate of patients with cardiovascular diseases (CVDs). The Body Sensor Network (BSN) based ECG monitoring is a new and efficien method to protect the CVDs patients. To meet the challenges of miniaturization, low power and high signal quality of the node, we proposed a novel 50 mmX 50 mmX 10 mm, 30 g wireless ECG node, which includes the single-chip an alog front-end AD8232, ultra-low power microprocessor MSP430F1611 and Bluetooth module HM-11. The ECG signal quality is guaranteed by the on-line digital filtering. The difference threshold algorithm results in accuracy of R-wave detection and heart rate. Experiments were carried out to test the node and the results showed that the pro posed node reached the design target, and it has great potential in application of wireless ECG monitoring.

ECG Based Heart Arrhythmia Detection Using Wavelet Coherence and Bat Algorithm

NASA Astrophysics Data System (ADS)

Kora, Padmavathi; Sri Rama Krishna, K.

2016-12-01

Atrial fibrillation (AF) is a type of heart abnormality, during the AF electrical discharges in the atrium are rapid, results in abnormal heart beat. The morphology of ECG changes due to the abnormalities in the heart. This paper consists of three major steps for the detection of heart diseases: signal pre-processing, feature extraction and classification. Feature extraction is the key process in detecting the heart abnormality. Most of the ECG detection systems depend on the time domain features for cardiac signal classification. In this paper we proposed a wavelet coherence (WTC) technique for ECG signal analysis. The WTC calculates the similarity between two waveforms in frequency domain. Parameters extracted from WTC function is used as the features of the ECG signal. These features are optimized using Bat algorithm. The Levenberg Marquardt neural network classifier is used to classify the optimized features. The performance of the classifier can be improved with the optimized features.

A regional prehospital electrocardiogram network with a single telecardiology "hub" for public emergency medical service: technical requirements, logistics, manpower, and preliminary results.

PubMed

Brunetti, Natale Daniele; De Gennaro, Luisa; Dellegrottaglie, Giulia; Amoruso, Daniele; Antonelli, Gianfranco; Di Biase, Matteo

2011-11-01

In patients with a major cardiac event, the first priority is to minimize time-to-treatment. For many patients, the first and fastest contact with the health system is through emergency medical services (EMS). However, delay to treatment is still significant in developed countries, and international guidelines therefore recommend that EMS use prehospital electrocardiogram (ECG). Many communities are implementing prehospital ECG programs, with different technical solutions. We report on a region-wide prehospital ECG telecardiology program that involved 233,657 patients from all over Apulia (4 million inhabitants), Italy, who called the public regional free EMS telephone number "118." Prehospital ECG was transmitted by mobile phone to a single regional telecardiology "hub" where a cardiologist available 24/7 promptly reported the ECG, having a briefing with on-scene EMS personnel and EMS district central; patients were then directed to fibrinolysis or primary percutaneous coronary intervention (PCI) as appropriate. Patients were >70 years in 51% of cases, and 55% of prehospital ECGs were unremarkable; the remaining 45% showed signs suggesting acute coronary syndrome (ACS) in 18%, arrhythmias in 20%, and minor findings in 62%. In cases of suspected ACS (chest pain), ECG findings were normal in 77% of patients; 74% of subjects with suspected ACS were screened within 30' from the onset of symptoms. A regional single telecardiology hub providing prehospital ECG for a sole regional public EMS provides an example of a prehospital ECG network optimizing quality of ECG report and uniformity of EMS assistance in a large region-wide network.

Novel technical solutions for wireless ECG transmission & analysis in the age of the internet cloud.

PubMed

Al-Zaiti, Salah S; Shusterman, Vladimir; Carey, Mary G

2013-01-01

Current guidelines recommend early reperfusion therapy for ST-elevation myocardial infarction (STEMI) within 90 min of first medical encounter. Telecardiology entails the use of advanced communication technologies to transmit the prehospital 12-lead electrocardiogram (ECG) to offsite cardiologists for early triage to the cath lab; which has been shown to dramatically reduce door-to-balloon time and total mortality. However, hospitals often find adopting ECG transmission technologies very challenging. The current review identifies seven major technical challenges of prehospital ECG transmission, including: paramedics inconvenience and transport delay; signal noise and interpretation errors; equipment malfunction and transmission failure; reliability of mobile phone networks; lack of compliance with the standards of digital ECG formats; poor integration with electronic medical records; and costly hardware and software pre-requisite installation. Current and potential solutions to address each of these technical challenges are discussed in details and include: automated ECG transmission protocols; annotatable waveform-based ECGs; optimal routing solutions; and the use of cloud computing systems rather than vendor-specific processing stations. Nevertheless, strategies to monitor transmission effectiveness and patient outcomes are essential to sustain initial gains of implementing ECG transmission technologies. © 2013.

Hardware Prototyping of Neural Network based Fetal Electrocardiogram Extraction

NASA Astrophysics Data System (ADS)

Hasan, M. A.; Reaz, M. B. I.

2012-01-01

The aim of this paper is to model the algorithm for Fetal ECG (FECG) extraction from composite abdominal ECG (AECG) using VHDL (Very High Speed Integrated Circuit Hardware Description Language) for FPGA (Field Programmable Gate Array) implementation. Artificial Neural Network that provides efficient and effective ways of separating FECG signal from composite AECG signal has been designed. The proposed method gives an accuracy of 93.7% for R-peak detection in FHR monitoring. The designed VHDL model is synthesized and fitted into Altera's Stratix II EP2S15F484C3 using the Quartus II version 8.0 Web Edition for FPGA implementation.

Low-power wireless ECG acquisition and classification system for body sensor networks.

PubMed

Lee, Shuenn-Yuh; Hong, Jia-Hua; Hsieh, Cheng-Han; Liang, Ming-Chun; Chang Chien, Shih-Yu; Lin, Kuang-Hao

2015-01-01

A low-power biosignal acquisition and classification system for body sensor networks is proposed. The proposed system consists of three main parts: 1) a high-pass sigma delta modulator-based biosignal processor (BSP) for signal acquisition and digitization, 2) a low-power, super-regenerative on-off keying transceiver for short-range wireless transmission, and 3) a digital signal processor (DSP) for electrocardiogram (ECG) classification. The BSP and transmitter circuits, which are the body-end circuits, can be operated for over 80 days using two 605 mAH zinc-air batteries as the power supply; the power consumption is 586.5 μW. As for the radio frequency receiver and DSP, which are the receiving-end circuits that can be integrated in smartphones or personal computers, power consumption is less than 1 mW. With a wavelet transform-based digital signal processing circuit and a diagnosis control by cardiologists, the accuracy of beat detection and ECG classification are close to 99.44% and 97.25%, respectively. All chips are fabricated in TSMC 0.18-μm standard CMOS process.

[Rapid Identification of Epicarpium Citri Grandis via Infrared Spectroscopy and Fluorescence Spectrum Imaging Technology Combined with Neural Network].

PubMed

Pan, Sha-sha; Huang, Fu-rong; Xiao, Chi; Xian, Rui-yi; Ma, Zhi-guo

2015-10-01

To explore rapid reliable methods for detection of Epicarpium citri grandis (ECG), the experiment using Fourier Transform Attenuated Total Reflection Infrared Spectroscopy (FTIR/ATR) and Fluorescence Spectrum Imaging Technology combined with Multilayer Perceptron (MLP) Neural Network pattern recognition, for the identification of ECG, and the two methods are compared. Infrared spectra and fluorescence spectral images of 118 samples, 81 ECG and 37 other kinds of ECG, are collected. According to the differences in tspectrum, the spectra data in the 550-1 800 cm(-1) wavenumber range and 400-720 nm wavelength are regarded as the study objects of discriminant analysis. Then principal component analysis (PCA) is applied to reduce the dimension of spectroscopic data of ECG and MLP Neural Network is used in combination to classify them. During the experiment were compared the effects of different methods of data preprocessing on the model: multiplicative scatter correction (MSC), standard normal variable correction (SNV), first-order derivative(FD), second-order derivative(SD) and Savitzky-Golay (SG). The results showed that: after the infrared spectra data via the Savitzky-Golay (SG) pretreatment through the MLP Neural Network with the hidden layer function as sigmoid, we can get the best discrimination of ECG, the correct percent of training set and testing set are both 100%. Using fluorescence spectral imaging technology, corrected by the multiple scattering (MSC) results in the pretreatment is the most ideal. After data preprocessing, the three layers of the MLP Neural Network of the hidden layer function as sigmoid function can get 100% correct percent of training set and 96.7% correct percent of testing set. It was shown that the FTIR/ATR and fluorescent spectral imaging technology combined with MLP Neural Network can be used for the identification study of ECG and has the advantages of rapid, reliable effect.

An optimized compression algorithm for real-time ECG data transmission in wireless network of medical information systems.

PubMed

Cho, Gyoun-Yon; Lee, Seo-Joon; Lee, Tae-Ro

2015-01-01

Recent medical information systems are striving towards real-time monitoring models to care patients anytime and anywhere through ECG signals. However, there are several limitations such as data distortion and limited bandwidth in wireless communications. In order to overcome such limitations, this research focuses on compression. Few researches have been made to develop a specialized compression algorithm for ECG data transmission in real-time monitoring wireless network. Not only that, recent researches' algorithm is not appropriate for ECG signals. Therefore this paper presents a more developed algorithm EDLZW for efficient ECG data transmission. Results actually showed that the EDLZW compression ratio was 8.66, which was a performance that was 4 times better than any other recent compression method widely used today.

Low-power analog integrated circuits for wireless ECG acquisition systems.

PubMed

Tsai, Tsung-Heng; Hong, Jia-Hua; Wang, Liang-Hung; Lee, Shuenn-Yuh

2012-09-01

This paper presents low-power analog ICs for wireless ECG acquisition systems. Considering the power-efficient communication in the body sensor network, the required low-power analog ICs are developed for a healthcare system through miniaturization and system integration. To acquire the ECG signal, a low-power analog front-end system, including an ECG signal acquisition board, an on-chip low-pass filter, and an on-chip successive-approximation analog-to-digital converter for portable ECG detection devices is presented. A quadrature CMOS voltage-controlled oscillator and a 2.4 GHz direct-conversion transmitter with a power amplifier and upconversion mixer are also developed to transmit the ECG signal through wireless communication. In the receiver, a 2.4 GHz fully integrated CMOS RF front end with a low-noise amplifier, differential power splitter, and quadrature mixer based on current-reused folded architecture is proposed. The circuits have been implemented to meet the specifications of the IEEE 802.15.4 2.4 GHz standard. The low-power ICs of the wireless ECG acquisition systems have been fabricated using a 0.18 μm Taiwan Semiconductor Manufacturing Company (TSMC) CMOS standard process. The measured results on the human body reveal that ECG signals can be acquired effectively by the proposed low-power analog front-end ICs.

Automatic QRS complex detection using two-level convolutional neural network.

PubMed

Xiang, Yande; Lin, Zhitao; Meng, Jianyi

2018-01-29

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

Using ordinal partition transition networks to analyze ECG data

NASA Astrophysics Data System (ADS)

Kulp, Christopher W.; Chobot, Jeremy M.; Freitas, Helena R.; Sprechini, Gene D.

2016-07-01

Electrocardiogram (ECG) data from patients with a variety of heart conditions are studied using ordinal pattern partition networks. The ordinal pattern partition networks are formed from the ECG time series by symbolizing the data into ordinal patterns. The ordinal patterns form the nodes of the network and edges are defined through the time ordering of the ordinal patterns in the symbolized time series. A network measure, called the mean degree, is computed from each time series-generated network. In addition, the entropy and number of non-occurring ordinal patterns (NFP) is computed for each series. The distribution of mean degrees, entropies, and NFPs for each heart condition studied is compared. A statistically significant difference between healthy patients and several groups of unhealthy patients with varying heart conditions is found for the distributions of the mean degrees, unlike for any of the distributions of the entropies or NFPs.

Architecture design of the multi-functional wavelet-based ECG microprocessor for realtime detection of abnormal cardiac events.

PubMed

Cheng, Li-Fang; Chen, Tung-Chien; Chen, Liang-Gee

2012-01-01

Most of the abnormal cardiac events such as myocardial ischemia, acute myocardial infarction (AMI) and fatal arrhythmia can be diagnosed through continuous electrocardiogram (ECG) analysis. According to recent clinical research, early detection and alarming of such cardiac events can reduce the time delay to the hospital, and the clinical outcomes of these individuals can be greatly improved. Therefore, it would be helpful if there is a long-term ECG monitoring system with the ability to identify abnormal cardiac events and provide realtime warning for the users. The combination of the wireless body area sensor network (BASN) and the on-sensor ECG processor is a possible solution for this application. In this paper, we aim to design and implement a digital signal processor that is suitable for continuous ECG monitoring and alarming based on the continuous wavelet transform (CWT) through the proposed architectures--using both programmable RISC processor and application specific integrated circuits (ASIC) for performance optimization. According to the implementation results, the power consumption of the proposed processor integrated with an ASIC for CWT computation is only 79.4 mW. Compared with the single-RISC processor, about 91.6% of the power reduction is achieved.

Classification of cardiac patient states using artificial neural networks

PubMed Central

Kannathal, N; Acharya, U Rajendra; Lim, Choo Min; Sadasivan, PK; Krishnan, SM

2003-01-01

Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier. PMID:19649222

Autoadaptivity and optimization in distributed ECG interpretation.

PubMed

Augustyniak, Piotr

2010-03-01

This paper addresses principal issues of the ECG interpretation adaptivity in a distributed surveillance network. In the age of pervasive access to wireless digital communication, distributed biosignal interpretation networks may not only optimally solve difficult medical cases, but also adapt the data acquisition, interpretation, and transmission to the variable patient's status and availability of technical resources. The background of such adaptivity is the innovative use of results from the automatic ECG analysis to the seamless remote modification of the interpreting software. Since the medical relevance of issued diagnostic data depends on the patient's status, the interpretation adaptivity implies the flexibility of report content and frequency. Proposed solutions are based on the research on human experts behavior, procedures reliability, and usage statistics. Despite the limited scale of our prototype client-server application, the tests yielded very promising results: the transmission channel occupation was reduced by 2.6 to 5.6 times comparing to the rigid reporting mode and the improvement of the remotely computed diagnostic outcome was achieved in case of over 80% of software adaptation attempts.

Mobile messaging services-based personal electrocardiogram monitoring system.

PubMed

Tahat, Ashraf A

2009-01-01

A mobile monitoring system utilizing Bluetooth and mobile messaging services (MMS/SMSs) with low-cost hardware equipment is proposed. A proof of concept prototype has been developed and implemented to enable transmission of an Electrocardiogram (ECG) signal and body temperature of a patient, which can be expanded to include other vital signs. Communication between a mobile smart-phone and the ECG and temperature acquisition apparatus is implemented using the popular personal area network standard specification Bluetooth. When utilizing MMS for transmission, the mobile phone plots the received ECG signal and displays the temperature using special application software running on the client mobile phone itself, where the plot can be captured and saved as an image before transmission. Alternatively, SMS can be selected as a transmission means, where in this scenario, dedicated application software is required at the receiving device. The experimental setup can be operated for monitoring from anywhere in the globe covered by a cellular network that offers data services.

Mobile Messaging Services-Based Personal Electrocardiogram Monitoring System

PubMed Central

Tahat, Ashraf A.

2009-01-01

A mobile monitoring system utilizing Bluetooth and mobile messaging services (MMS/SMSs) with low-cost hardware equipment is proposed. A proof of concept prototype has been developed and implemented to enable transmission of an Electrocardiogram (ECG) signal and body temperature of a patient, which can be expanded to include other vital signs. Communication between a mobile smart-phone and the ECG and temperature acquisition apparatus is implemented using the popular personal area network standard specification Bluetooth. When utilizing MMS for transmission, the mobile phone plots the received ECG signal and displays the temperature using special application software running on the client mobile phone itself, where the plot can be captured and saved as an image before transmission. Alternatively, SMS can be selected as a transmission means, where in this scenario, dedicated application software is required at the receiving device. The experimental setup can be operated for monitoring from anywhere in the globe covered by a cellular network that offers data services. PMID:19707531

Detecting atrial fibrillation by deep convolutional neural networks.

PubMed

Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

2018-02-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Block sparsity-based joint compressed sensing recovery of multi-channel ECG signals.

PubMed

Singh, Anurag; Dandapat, Samarendra

2017-04-01

In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance. However, most of the existing CS-based works exploit either of the correlations, which results in a suboptimal performance. In this work, within a CS framework, the authors propose to exploit both types of correlations simultaneously using a sparse Bayesian learning-based approach. A spatiotemporal sparse model is employed for joint compression/reconstruction of MECG signals. Discrete wavelets transform domain block sparsity of MECG signals is exploited for simultaneous reconstruction of all the channels. Performance evaluations using Physikalisch-Technische Bundesanstalt MECG diagnostic database show a significant gain in the diagnostic reconstruction quality of the MECG signals compared with the state-of-the art techniques at reduced number of measurements. Low measurement requirement may lead to significant savings in the energy-cost of the existing CS-based WBAN systems.

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm.

PubMed

Pandit, Diptangshu; Zhang, Li; Liu, Chengyu; Chattopadhyay, Samiran; Aslam, Nauman; Lim, Chee Peng

2017-06-01

Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. The proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation. In comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal. Copyright © 2017 Elsevier B.V. All rights reserved.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks.

PubMed

Savalia, Shalin; Emamian, Vahid

2018-05-04

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

T-wave morphology can distinguish healthy controls from LQTS patients.

PubMed

Immanuel, S A; Sadrieh, A; Baumert, M; Couderc, J P; Zareba, W; Hill, A P; Vandenberg, J I

2016-09-01

Long QT syndrome (LQTS) is an inherited disorder associated with prolongation of the QT/QTc interval on the surface electrocardiogram (ECG) and a markedly increased risk of sudden cardiac death due to cardiac arrhythmias. Up to 25% of genotype-positive LQTS patients have QT/QTc intervals in the normal range. These patients are, however, still at increased risk of life-threatening events compared to their genotype-negative siblings. Previous studies have shown that analysis of T-wave morphology may enhance discrimination between control and LQTS patients. In this study we tested the hypothesis that automated analysis of T-wave morphology from Holter ECG recordings could distinguish between control and LQTS patients with QTc values in the range 400-450 ms. Holter ECGs were obtained from the Telemetric and Holter ECG Warehouse (THEW) database. Frequency binned averaged ECG waveforms were obtained and extracted T-waves were fitted with a combination of 3 sigmoid functions (upslope, downslope and switch) or two 9th order polynomial functions (upslope and downslope). Neural network classifiers, based on parameters obtained from the sigmoid or polynomial fits to the 1 Hz and 1.3 Hz ECG waveforms, were able to achieve up to 92% discrimination between control and LQTS patients and 88% discrimination between LQTS1 and LQTS2 patients. When we analysed a subgroup of subjects with normal QT intervals (400-450 ms, 67 controls and 61 LQTS), T-wave morphology based parameters enabled 90% discrimination between control and LQTS patients, compared to only 71% when the groups were classified based on QTc alone. In summary, our Holter ECG analysis algorithms demonstrate the feasibility of using automated analysis of T-wave morphology to distinguish LQTS patients, even those with normal QTc, from healthy controls.

False alarm reduction in BSN-based cardiac monitoring using signal quality and activity type information.

PubMed

Tanantong, Tanatorn; Nantajeewarawat, Ekawit; Thiemjarus, Surapa

2015-02-09

False alarms in cardiac monitoring affect the quality of medical care, impacting on both patients and healthcare providers. In continuous cardiac monitoring using wireless Body Sensor Networks (BSNs), the quality of ECG signals can be deteriorated owing to several factors, e.g., noises, low battery power, and network transmission problems, often resulting in high false alarm rates. In addition, body movements occurring from activities of daily living (ADLs) can also create false alarms. This paper presents a two-phase framework for false arrhythmia alarm reduction in continuous cardiac monitoring, using signals from an ECG sensor and a 3D accelerometer. In the first phase, classification models constructed using machine learning algorithms are used for labeling input signals. ECG signals are labeled with heartbeat types and signal quality levels, while 3D acceleration signals are labeled with ADL types. In the second phase, a rule-based expert system is used for combining classification results in order to determine whether arrhythmia alarms should be accepted or suppressed. The proposed framework was validated on datasets acquired using BSNs and the MIT-BIH arrhythmia database. For the BSN dataset, acceleration and ECG signals were collected from 10 young and 10 elderly subjects while they were performing ADLs. The framework reduced the false alarm rate from 9.58% to 1.43% in our experimental study, showing that it can potentially assist physicians in diagnosing a vast amount of data acquired from wireless sensors and enhance the performance of continuous cardiac monitoring.

Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

PubMed

Hwang, Bosun; You, Jiwoo; Vaessen, Thomas; Myin-Germeys, Inez; Park, Cheolsoo; Zhang, Byoung-Tak

2018-02-08

Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

Energy-efficient ECG compression on wireless biosensors via minimal coherence sensing and weighted ℓ₁ minimization reconstruction.

PubMed

Zhang, Jun; Gu, Zhenghui; Yu, Zhu Liang; Li, Yuanqing

2015-03-01

Low energy consumption is crucial for body area networks (BANs). In BAN-enabled ECG monitoring, the continuous monitoring entails the need of the sensor nodes to transmit a huge data to the sink node, which leads to excessive energy consumption. To reduce airtime over energy-hungry wireless links, this paper presents an energy-efficient compressed sensing (CS)-based approach for on-node ECG compression. At first, an algorithm called minimal mutual coherence pursuit is proposed to construct sparse binary measurement matrices, which can be used to encode the ECG signals with superior performance and extremely low complexity. Second, in order to minimize the data rate required for faithful reconstruction, a weighted ℓ1 minimization model is derived by exploring the multisource prior knowledge in wavelet domain. Experimental results on MIT-BIH arrhythmia database reveals that the proposed approach can obtain higher compression ratio than the state-of-the-art CS-based methods. Together with its low encoding complexity, our approach can achieve significant energy saving in both encoding process and wireless transmission.

Predictable and reliable ECG monitoring over IEEE 802.11 WLANs within a hospital.

PubMed

Park, Juyoung; Kang, Kyungtae

2014-09-01

Telecardiology provides mobility for patients who require constant electrocardiogram (ECG) monitoring. However, its safety is dependent on the predictability and robustness of data delivery, which must overcome errors in the wireless channel through which the ECG data are transmitted. We report here a framework that can be used to gauge the applicability of IEEE 802.11 wireless local area network (WLAN) technology to ECG monitoring systems in terms of delay constraints and transmission reliability. For this purpose, a medical-grade WLAN architecture achieved predictable delay through the combination of a medium access control mechanism based on the point coordination function provided by IEEE 802.11 and an error control scheme based on Reed-Solomon coding and block interleaving. The size of the jitter buffer needed was determined by this architecture to avoid service dropout caused by buffer underrun, through analysis of variations in transmission delay. Finally, we assessed this architecture in terms of service latency and reliability by modeling the transmission of uncompressed two-lead electrocardiogram data from the MIT-BIH Arrhythmia Database and highlight the applicability of this wireless technology to telecardiology.

Fast clustering algorithm for large ECG data sets based on CS theory in combination with PCA and K-NN methods.

PubMed

Balouchestani, Mohammadreza; Krishnan, Sridhar

2014-01-01

Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) they suffer from huge energy consumption and load of sampling. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for establishing low power long-term ECG recording. In this paper, we present an advanced K-means clustering algorithm based on Compressed Sensing (CS) theory as a random sampling procedure. Then, two dimensionality reduction methods: Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) followed by sorting the data using the K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers are applied to the proposed algorithm. We show our algorithm based on PCA features in combination with K-NN classifier shows better performance than other methods. The proposed algorithm outperforms existing algorithms by increasing 11% classification accuracy. In addition, the proposed algorithm illustrates classification accuracy for K-NN and PNN classifiers, and a Receiver Operating Characteristics (ROC) area of 99.98%, 99.83%, and 99.75% respectively.

Classification of cardiac arrhythmias using competitive networks.

PubMed

Leite, Cicilia R M; Martin, Daniel L; Sizilio, Glaucia R A; Dos Santos, Keylly E A; de Araujo, Bruno G; Valentim, Ricardo A M; Neto, Adriao D D; de Melo, Jorge D; Guerreiro, Ana M G

2010-01-01

Information generated by sensors that collect a patient's vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case the electrocardiogram (ECG). Processing uses a neural classifier, Kohonen competitive neural networks, detecting if the ECG shows any cardiac arrhythmia. In fact, it is possible to classify an ECG signal and thereby detect if it is exhibiting or not any alteration, according to normality.

Toward personal eHealth in cardiology. Results from the EPI-MEDICS telemedicine project.

PubMed

Rubel, Paul; Fayn, Jocelyne; Nollo, Giandomenico; Assanelli, Deodato; Li, Bo; Restier, Lioara; Adami, Stefano; Arod, Sébastien; Atoui, Hussein; Ohlsson, Mattias; Simon-Chautemps, Lucas; Télisson, David; Malossi, Cesare; Ziliani, Gian-Luca; Galassi, Alfredo; Edenbrandt, Lars; Chevalier, Philippe

2005-10-01

Despite many attempts to improve the management of acute myocardial infarction, only small trends to shorter time intervals before treatment have been reported. The self-care solution developed by the European EPI-MEDICS project (2001-2004) is a novel, very affordable, easy-to-use, portable, and intelligent Personal ECG Monitor (PEM) for the early detection of cardiac ischemia and arrhythmia that is able to record a professional-quality, 3-lead electrocardiogram (ECG) based on leads I, II, and V2; derive the missing leads of the standard 12-lead ECG (thanks to either a generic or a patient-specific transform), compare each ECG with a reference ECG by means of advanced neural network-based decision-making methods taking into account the serial ECG measurements and the patient risk factors and clinical data; and generate different levels of alarms and forward the alarm messages with the recorded ECGs and the patient's Personal electronic Health Record (PHR) to the relevant health care providers by means of a standard Bluetooth-enabled, GSM/GPRS-compatible mobile phone. The ECG records are SCP-ECG encoded and stored with the PHR on a secure personal SD Card embedded in the PEM device. The alarm messages and the PHR are XML encoded. Major alarm messages are automatically transmitted to the nearest emergency call center. Medium or minor alarms are sent on demand to a central PEM Alarm Web Server. Health professionals are informed by a Short Message Service. The PEM embeds itself a Web server to facilitate the reviewing and/or update of the PHR during a routine visit at the office of the general physician or cardiologist. Eighty PEM prototypes have been finalized and tested for several weeks on 697 citizens/patients in different clinical and self-care situations involving end users (188 patients), general physicians (10), and cardiologists (9). The clinical evaluation indicates that the EPI-MEDICS concept may save lives and is very valuable for prehospitalization triage.

Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification.

PubMed

Arvanaghi, Roghayyeh; Daneshvar, Sabalan; Seyedarabi, Hadi; Goshvarpour, Atefeh

2017-11-01

Each of Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals contain information of cardiac status. This information can be used for diagnosis and monitoring of diseases. The majority of previously proposed methods rely only on ECG signal to classify heart rhythms. In this paper, ECG and ABP were used to classify five different types of heart rhythms. To this end, two mentioned signals (ECG and ABP) have been fused. These physiological signals have been used from MINIC physioNet database. ECG and ABP signals have been fused together on the basis of the proposed Discrete Wavelet Transformation fusion technique. Then, some frequency features were extracted from the fused signal. To classify the different types of cardiac arrhythmias, these features were given to a multi-layer perceptron neural network. In this study, the best results for the proposed fusion algorithm were obtained. In this case, the accuracy rates of 96.6%, 96.9%, 95.6% and 93.9% were achieved for two, three, four and five classes, respectively. However, the maximum classification rate of 89% was obtained for two classes on the basis of ECG features. It has been found that the higher accuracy rates were acquired by using the proposed fusion technique. The results confirmed the importance of fusing features from different physiological signals to gain more accurate assessments. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification of ECG beats using deep belief network and active learning.

PubMed

G, Sayantan; T, Kien P; V, Kadambari K

2018-04-12

A new semi-supervised approach based on deep learning and active learning for classification of electrocardiogram signals (ECG) is proposed. The objective of the proposed work is to model a scientific method for classification of cardiac irregularities using electrocardiogram beats. The model follows the Association for the Advancement of medical instrumentation (AAMI) standards and consists of three phases. In phase I, feature representation of ECG is learnt using Gaussian-Bernoulli deep belief network followed by a linear support vector machine (SVM) training in the consecutive phase. It yields three deep models which are based on AAMI-defined classes, namely N, V, S, and F. In the last phase, a query generator is introduced to interact with the expert to label few beats to improve accuracy and sensitivity. The proposed approach depicts significant improvement in accuracy with minimal queries posed to the expert and fast online training as tested on the MIT-BIH Arrhythmia Database and the MIT-BIH Supra-ventricular Arrhythmia Database (SVDB). With 100 queries labeled by the expert in phase III, the method achieves an accuracy of 99.5% in "S" versus all classifications (SVEB) and 99.4% accuracy in "V " versus all classifications (VEB) on MIT-BIH Arrhythmia Database. In a similar manner, it is attributed that an accuracy of 97.5% for SVEB and 98.6% for VEB on SVDB database is achieved respectively. Graphical Abstract Reply- Deep belief network augmented by active learning for efficient prediction of arrhythmia.

Noninvasive fetal QRS detection using an echo state network and dynamic programming.

PubMed

Lukoševičius, Mantas; Marozas, Vaidotas

2014-08-01

We address a classical fetal QRS detection problem from abdominal ECG recordings with a data-driven statistical machine learning approach. Our goal is to have a powerful, yet conceptually clean, solution. There are two novel key components at the heart of our approach: an echo state recurrent neural network that is trained to indicate fetal QRS complexes, and several increasingly sophisticated versions of statistics-based dynamic programming algorithms, which are derived from and rooted in probability theory. We also employ a standard technique for preprocessing and removing maternal ECG complexes from the signals, but do not take this as the main focus of this work. The proposed approach is quite generic and can be extended to other types of signals and annotations. Open-source code is provided.

DexterNet: An Open Platform for Heterogeneous Body Sensor Networks and Its Applications

DTIC Science & Technology

2008-12-19

motion, ECG PC, PDA 802.15.4 No No ALARM-NET pulse oximetry STARGATE Bluetooth No Yes [19] motion, ECG PDA, PC 802.11 (temperature, light, PIR) DexterNet...motion, ECG PDA 802.15.4 Yes Possible via SPINE EIP, GPS PC (e.g., air pollution sensor) MICAz, SHIMMER uses MICAz sensors and STARGATE to relay the

Audit of primary care electrocardiograms sent as emergency to a telehealth service - the Telehealth Network of Minas Gerais, Brazil.

PubMed

Marcolino, Milena S; Carvalho, Bárbara C; Lucena, Aline M; França, Ana Luiza N; Pessoa, Cristiane G; Neves, Daniel S; Alkmim, Maria Beatriz M

2015-01-01

The Telehealth Network of Minas Gerais (TNMG) is a public telehealth service in Brazil that has performed electrocardiogram (ECG) analysis since 2005. From February to March 2014, 28% of ECGs were classified as "emergency" by the primary care tele-health sites. This quasi-experimental study aimed to investigate the reasons behind the high number of emergency ECGs being sent in, the implementation of corrective actions, and an assessment of the impact of these actions. In the 1st phase, primary care units that sent >70% of ECGs as emergency from February to March 2014 were selected. The 2nd phase consisted of the intervention. In the 3rd phase, the proportion of ECGs sent as an emergency during the 1st and 2nd months post intervention were assessed. Of the 63 cities selected during the 1st phase, 50% of the practitioners did not know the proper definition of emergency. After the intervention, 67% of the cities had a significant reduction in the proportion of ECGs sent as an emergency during the 1st month, and 17% had a significant reduction during the 2nd month.

Predicting Electrocardiogram and Arterial Blood Pressure Waveforms with Different Echo State Network Architectures

DTIC Science & Technology

2014-11-01

networks were trained to predict an individual’s electrocardiogram (ECG) and arterial blood pressure ( ABP ) waveform data, which can potentially help...various ESN architectures for prediction tasks, and establishes the benefits of using ESN architecture designs for predicting ECG and ABP waveforms...arterial blood pressure ( ABP ) waveforms immediately prior to the machine generated alarms. When tested, the algorithm suppressed approximately 59.7

Primary prevention of sudden cardiac death of the young athlete: the controversy about the screening electrocardiogram and its innovative artificial intelligence solution.

PubMed

Chang, Anthony C

2012-03-01

The preparticipation screening for athlete participation in sports typically entails a comprehensive medical and family history and a complete physical examination. A 12-lead electrocardiogram (ECG) can increase the likelihood of detecting cardiac diagnoses such as hypertrophic cardiomyopathy, but this diagnostic test as part of the screening process has engendered considerable controversy. The pro position is supported by argument that international screening protocols support its use, positive diagnosis has multiple benefits, history and physical examination are inadequate, primary prevention is essential, and the cost effectiveness is justified. Although the aforementioned myriad of justifications for routine ECG screening of young athletes can be persuasive, several valid contentions oppose supporting such a policy, namely, that the sudden death incidence is very (too) low, the ECG screening will be too costly, the false-positive rate is too high, resources will be allocated away from other diseases, and manpower is insufficient for its execution. Clinicians, including pediatric cardiologists, have an understandable proclivity for avoiding this prodigious national endeavor. The controversy, however, should not be focused on whether an inexpensive, noninvasive test such as an ECG should be mandated but should instead be directed at just how these tests for young athletes can be performed in the clinical imbroglio of these disease states (with variable genetic penetrance and phenotypic expression) with concomitant fiscal accountability and logistical expediency in this era of economic restraint. This monumental endeavor in any city or region requires two crucial elements well known to business scholars: implementation and execution. The eventual solution for the screening ECG dilemma requires a truly innovative and systematic approach that will liberate us from inadequate conventional solutions. Artificial intelligence, specifically the process termed "machine learning" and "neural networking," involves complex algorithms that allow computers to improve the decision-making process based on repeated input of empirical data (e.g., databases and ECGs). These elements all can be improved with a national database, evidence-based medicine, and in the near future, innovation that entails a Kurzweilian artificial intelligence infrastructure with machine learning and neural networking that will construct the ultimate clinical decision-making algorithm.

ST-Segment Analysis Using Wireless Technology in Acute Myocardial Infarction (STAT-MI) trial.

PubMed

Dhruva, Vivek N; Abdelhadi, Samir I; Anis, Ather; Gluckman, William; Hom, David; Dougan, William; Kaluski, Edo; Haider, Bunyad; Klapholz, Marc

2007-08-07

Our goal was to examine the effects of implementing a fully automated wireless network to reduce door-to-intervention times (D2I) in ST-segment elevation myocardial infarction (STEMI). Wireless technologies used to transmit prehospital electrocardiograms (ECGs) have helped to decrease D2I times but have unrealized potential. A fully automated wireless network that facilitates simultaneous 12-lead ECG transmission from emergency medical services (EMS) personnel in the field to the emergency department (ED) and offsite cardiologists via smartphones was developed. The system is composed of preconfigured Bluetooth devices, preprogrammed receiving/transmitting stations, dedicated e-mail servers, and smartphones. The network facilitates direct communication between offsite cardiologists and EMS personnel, allowing for patient triage directly to the cardiac catheterization laboratory from the field. Demographic, laboratory, and time interval data were prospectively collected and compared with calendar year 2005 data. From June to December 2006, 80 ECGs with suspected STEMI were transmitted via the network. Twenty patients with ECGs consistent with STEMI were triaged to the catheterization laboratory. Improvement was seen in mean door-to-cardiologist notification (-14.6 vs. 61.4 min, p < 0.001), door-to-arterial access (47.6 vs. 108.1 min, p < 0.001), time-to-first angiographic injection (52.8 vs. 119.2 min, p < 0.001), and D2I times (80.1 vs. 145.6 min, p < 0.001) compared with 2005 data. A fully automated wireless network that transmits ECGs simultaneously to the ED and offsite cardiologists for the early evaluation and triage of patients with suspected STEMI can decrease D2I times to <90 min and has the potential to be broadly applied in clinical practice.

Comprehensive electrocardiogram-to-device time for primary percutaneous coronary intervention in ST-segment elevation myocardial infarction: A report from the American Heart Association mission: Lifeline program.

PubMed

Shavadia, Jay S; French, William; Hellkamp, Anne S; Thomas, Laine; Bates, Eric R; Manoukian, Steven V; Kontos, Michael C; Suter, Robert; Henry, Timothy D; Dauerman, Harold L; Roe, Matthew T

2018-03-01

Assessing hospital-related network-level primary percutaneous coronary intervention (PCI) performance for ST-segment elevation myocardial infarction (STEMI) is challenging due to differential time-to-treatment metrics based on location of diagnostic electrocardiogram (ECG) for STEMI. STEMI patients undergoing primary PCI at 588 PCI-capable hospitals in AHA Mission: Lifeline (2008-2013) were categorized by initial STEMI identification location: PCI-capable hospitals (Group 1); pre-hospital setting (Group 2); and non-PCI-capable hospitals (Group 3). Patient-specific time-to-treatment categories were converted to minutes ahead of or behind their group-specific mean; average time-to-treatment difference for all patients at a given hospital was termed comprehensive ECG-to-device time. Hospitals were then stratified into tertiles based on their comprehensive ECG-to-device times with negative values below the mean representing shorter (faster) time intervals. Of 117,857 patients, the proportion in Groups 1, 2, and 3 were 42%, 33%, and 25%, respectively. Lower rates of heart failure and cardiac arrest at presentation are noted within patients presenting to high-performing hospitals. Median comprehensive ECG-to-device time was shortest at -9 minutes (25th, 75th percentiles: -13, -6) for the high-performing hospital tertile, 1 minute (-1, 3) for middle-performing, and 11 minutes (7, 16) for low-performing. Unadjusted rates of in-hospital mortality were 2.3%, 2.6%, and 2.7%, respectively, but the adjusted risk of in-hospital mortality was similar across tertiles. Comprehensive ECG-to-device time provides an integrated hospital-related network-level assessment of reperfusion timing metrics for primary PCI, regardless of the location for STEMI identification; further validation will delineate how this metric can be used to facilitate STEMI care improvements. Copyright © 2017 Elsevier Inc. All rights reserved.

ECG-cryptography and authentication in body area networks.

PubMed

Zhang, Zhaoyang; Wang, Honggang; Vasilakos, Athanasios V; Fang, Hua

2012-11-01

Wireless body area networks (BANs) have drawn much attention from research community and industry in recent years. Multimedia healthcare services provided by BANs can be available to anyone, anywhere, and anytime seamlessly. A critical issue in BANs is how to preserve the integrity and privacy of a person's medical data over wireless environments in a resource efficient manner. This paper presents a novel key agreement scheme that allows neighboring nodes in BANs to share a common key generated by electrocardiogram (ECG) signals. The improved Jules Sudan (IJS) algorithm is proposed to set up the key agreement for the message authentication. The proposed ECG-IJS key agreement can secure data communications over BANs in a plug-n-play manner without any key distribution overheads. Both the simulation and experimental results are presented, which demonstrate that the proposed ECG-IJS scheme can achieve better security performance in terms of serval performance metrics such as false acceptance rate (FAR) and false rejection rate (FRR) than other existing approaches. In addition, the power consumption analysis also shows that the proposed ECG-IJS scheme can achieve energy efficiency for BANs.

Deployment of an Advanced Electrocardiographic Analysis (A-ECG) to Detect Cardiovascular Risk in Career Firefighters

NASA Technical Reports Server (NTRS)

Dolezal, B. A.; Storer, T. W.; Abrazado, M.; Watne, R.; Schlegel, T. T.; Batalin, M.; Kaiser, W.; Smith, D. L.; Cooper, C. B.

2011-01-01

INTRODUCTION: Sudden cardiac death is the leading cause of line of duty death among firefighters, accounting for approximately 45% of fatalities annually. Firefighters perform strenuous muscular work while wearing heavy, encapsulating personal protective equipment in high ambient temperatures, under chaotic and emotionally stressful conditions. These factors can precipitate sudden cardiac events like myocardial infarction, serious dysrhythmias, or cerebrovascular accidents in firefighters with underlying cardiovascular disease. PURPOSE: The purpose of this study was to deploy and then evaluate the contribution of resting advanced ECG (A-ECG) in addition to other screening tools (family history, lipid profiles, and cardiopulmonary exercise tests, XT) in assessment of an individual fs cardiac risk profile. METHODS: Forty-four career firefighters were recruited to perform comprehensive baseline assessments including tests of aerobic performance, fasting lipids and glucose. Five-min resting 12-lead A-ECGs were obtained in a subset of firefighters (n=21) and transmitted over a secure networked system to a NASA physician collaborator. Using myocardial perfusion and other imaging as the gold standard, A-ECG scoring has been proven useful in accurately identifying a number of cardiac pathologies including coronary artery disease (CAD), left ventricular hypertrophy, hypertrophic cardiomyopathy, and non-ischemic and ischemic cardiomyopathy. RESULTS: Subjects f mean (SD) age was 43 (8) years, weight 91 (13) kg, and BMI 28 (3) kg/m2. Fifty-one percent of subjects had .3 cardiovascular risk factors. One subject had ST depression on XT ECG, at least one positive A-ECG score for CAD, and documented CAD based on cardiology referral. While all other subjects, including those with fewer risk factors, higher aerobic fitness, and normal exercise ECGs, were classified as healthy by A-ECG, there was no trend for association between risk factors and any of 20 A-ECG parameters in the grouped data.

A Deep Learning Approach to Examine Ischemic ST Changes in Ambulatory ECG Recordings.

PubMed

Xiao, Ran; Xu, Yuan; Pelter, Michele M; Mortara, David W; Hu, Xiao

2018-01-01

Patients with suspected acute coronary syndrome (ACS) are at risk of transient myocardial ischemia (TMI), which could lead to serious morbidity or even mortality. Early detection of myocardial ischemia can reduce damage to heart tissues and improve patient condition. Significant ST change in the electrocardiogram (ECG) is an important marker for detecting myocardial ischemia during the rule-out phase of potential ACS. However, current ECG monitoring software is vastly underused due to excessive false alarms. The present study aims to tackle this problem by combining a novel image-based approach with deep learning techniques to improve the detection accuracy of significant ST depression change. The obtained convolutional neural network (CNN) model yields an average area under the curve (AUC) at 89.6% from an independent testing set. At selected optimal cutoff thresholds, the proposed model yields a mean sensitivity at 84.4% while maintaining specificity at 84.9%.

Primary percutaneous coronary intervention for patients presenting with ST-segment elevation myocardial infarction: process improvement in a rural ST-segment elevation myocardial infarction receiving center.

PubMed

Niles, Nathaniel W; Conley, Sheila M; Yang, Rayson C; Vanichakarn, Pantila; Anderson, Tamara A; Butterly, John R; Robb, John F; Jayne, John E; Yanofsky, Norman N; Proehl, Jean A; Guadagni, Donald F; Brown, Jeremiah R

2010-01-01

Rural ST-segment elevation myocardial infarction (STEMI) care networks may be particularly disadvantaged in achieving a door-to-balloon time (D2B) of less than or equal to 90 minutes recommended in current guidelines. ST-ELEVATION MYOCARDIAL INFARCTION PROCESS UPGRADE PROJECT: A multidisciplinary STEMI process upgrade group at a rural percutaneous coronary intervention center implemented evidence-based strategies to reduce time to electrocardiogram (ECG) and D2B, including catheterization laboratory activation triggered by either a prehospital ECG demonstrating STEMI or an emergency department physician diagnosing STEMI, single-call catheterization laboratory activation, catheterization laboratory response time less than or equal to 30 minutes, and prompt data feedback. An ongoing regional STEMI registry was used to collect process time intervals, including time to ECG and D2B, in a consecutive series of STEMI patients presenting before (group 1) and after (group 2) strategy implementation. Significant reductions in time to first ECG in the emergency department and D2B were seen in group 2 compared with group 1. Important improvement in the process of acute STEMI patient care was accomplished in the rural percutaneous coronary intervention center setting by implementing evidence-based strategies. Copyright © 2010 Elsevier Inc. All rights reserved.

An ECG signals compression method and its validation using NNs.

PubMed

Fira, Catalina Monica; Goras, Liviu

2008-04-01

This paper presents a new algorithm for electrocardiogram (ECG) signal compression based on local extreme extraction, adaptive hysteretic filtering and Lempel-Ziv-Welch (LZW) coding. The algorithm has been verified using eight of the most frequent normal and pathological types of cardiac beats and an multi-layer perceptron (MLP) neural network trained with original cardiac patterns and tested with reconstructed ones. Aspects regarding the possibility of using the principal component analysis (PCA) to cardiac pattern classification have been investigated as well. A new compression measure called "quality score," which takes into account both the reconstruction errors and the compression ratio, is proposed.

A multichannel nonlinear adaptive noise canceller based on generalized FLANN for fetal ECG extraction

NASA Astrophysics Data System (ADS)

Ma, Yaping; Xiao, Yegui; Wei, Guo; Sun, Jinwei

2016-01-01

In this paper, a multichannel nonlinear adaptive noise canceller (ANC) based on the generalized functional link artificial neural network (FLANN, GFLANN) is proposed for fetal electrocardiogram (FECG) extraction. A FIR filter and a GFLANN are equipped in parallel in each reference channel to respectively approximate the linearity and nonlinearity between the maternal ECG (MECG) and the composite abdominal ECG (AECG). A fast scheme is also introduced to reduce the computational cost of the FLANN and the GFLANN. Two (2) sets of ECG time sequences, one synthetic and one real, are utilized to demonstrate the improved effectiveness of the proposed nonlinear ANC. The real dataset is derived from the Physionet non-invasive FECG database (PNIFECGDB) including 55 multichannel recordings taken from a pregnant woman. It contains two subdatasets that consist of 14 and 8 recordings, respectively, with each recording being 90 s long. Simulation results based on these two datasets reveal, on the whole, that the proposed ANC does enjoy higher capability to deal with nonlinearity between MECG and AECG as compared with previous ANCs in terms of fetal QRS (FQRS)-related statistics and morphology of the extracted FECG waveforms. In particular, for the second real subdataset, the F1-measure results produced by the PCA-based template subtraction (TSpca) technique and six (6) single-reference channel ANCs using LMS- and RLS-based FIR filters, Volterra filter, FLANN, GFLANN, and adaptive echo state neural network (ESN a ) are 92.47%, 93.70%, 94.07%, 94.22%, 94.90%, 94.90%, and 95.46%, respectively. The same F1-measure statistical results from five (5) multi-reference channel ANCs (LMS- and RLS-based FIR filters, Volterra filter, FLANN, and GFLANN) for the second real subdataset turn out to be 94.08%, 94.29%, 94.68%, 94.91%, and 94.96%, respectively. These results indicate that the ESN a and GFLANN perform best, with the ESN a being slightly better than the GFLANN but about four times more computationally expensive than the GFLANN, which makes the GFLANN a good alternative for NI-FECG extraction.

Mobile GPU-based implementation of automatic analysis method for long-term ECG.

PubMed

Fan, Xiaomao; Yao, Qihang; Li, Ye; Chen, Runge; Cai, Yunpeng

2018-05-03

Long-term electrocardiogram (ECG) is one of the important diagnostic assistant approaches in capturing intermittent cardiac arrhythmias. Combination of miniaturized wearable holters and healthcare platforms enable people to have their cardiac condition monitored at home. The high computational burden created by concurrent processing of numerous holter data poses a serious challenge to the healthcare platform. An alternative solution is to shift the analysis tasks from healthcare platforms to the mobile computing devices. However, long-term ECG data processing is quite time consuming due to the limited computation power of the mobile central unit processor (CPU). This paper aimed to propose a novel parallel automatic ECG analysis algorithm which exploited the mobile graphics processing unit (GPU) to reduce the response time for processing long-term ECG data. By studying the architecture of the sequential automatic ECG analysis algorithm, we parallelized the time-consuming parts and reorganized the entire pipeline in the parallel algorithm to fully utilize the heterogeneous computing resources of CPU and GPU. The experimental results showed that the average executing time of the proposed algorithm on a clinical long-term ECG dataset (duration 23.0 ± 1.0 h per signal) is 1.215 ± 0.140 s, which achieved an average speedup of 5.81 ± 0.39× without compromising analysis accuracy, comparing with the sequential algorithm. Meanwhile, the battery energy consumption of the automatic ECG analysis algorithm was reduced by 64.16%. Excluding energy consumption from data loading, 79.44% of the energy consumption could be saved, which alleviated the problem of limited battery working hours for mobile devices. The reduction of response time and battery energy consumption in ECG analysis not only bring better quality of experience to holter users, but also make it possible to use mobile devices as ECG terminals for healthcare professions such as physicians and health advisers, enabling them to inspect patient ECG recordings onsite efficiently without the need of a high-quality wide-area network environment.

A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks

PubMed Central

Hu, Sheng; Wei, Hongxing; Chen, Youdong; Tan, Jindong

2012-01-01

Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, the wearable sensor node is able to monitor the patient's ECG and motion signal in an unobstructive way. To realize the real-time medical analysis, the ECG is digitalized and transmitted to a smart phone via Bluetooth. On the smart phone, the ECG waveform is visualized and a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Experimental results demonstrate that the clean and reliable ECG waveform can be captured in multiple stressed conditions and the real-time classification on cardiac arrhythmia is competent to other workbenches. PMID:23112746

A Remote Health Monitoring System for the Elderly Based on Smart Home Gateway

PubMed Central

Shao, Minggang

2017-01-01

This paper proposed a remote health monitoring system for the elderly based on smart home gateway. The proposed system consists of three parts: the smart clothing, the smart home gateway, and the health care server. The smart clothing collects the elderly's electrocardiogram (ECG) and motion signals. The home gateway is used for data transmission. The health care server provides services of data storage and user information management; it is constructed on the Windows-Apache-MySQL-PHP (WAMP) platform and is tested on the Ali Cloud platform. To resolve the issues of data overload and network congestion of the home gateway, an ECG compression algorithm is applied. System demonstration shows that the ECG signals and motion signals of the elderly can be monitored. Evaluation of the compression algorithm shows that it has a high compression ratio and low distortion and consumes little time, which is suitable for home gateways. The proposed system has good scalability, and it is simple to operate. It has the potential to provide long-term and continuous home health monitoring services for the elderly. PMID:29204258

A Remote Health Monitoring System for the Elderly Based on Smart Home Gateway.

PubMed

Guan, Kai; Shao, Minggang; Wu, Shuicai

2017-01-01

This paper proposed a remote health monitoring system for the elderly based on smart home gateway. The proposed system consists of three parts: the smart clothing, the smart home gateway, and the health care server. The smart clothing collects the elderly's electrocardiogram (ECG) and motion signals. The home gateway is used for data transmission. The health care server provides services of data storage and user information management; it is constructed on the Windows-Apache-MySQL-PHP (WAMP) platform and is tested on the Ali Cloud platform. To resolve the issues of data overload and network congestion of the home gateway, an ECG compression algorithm is applied. System demonstration shows that the ECG signals and motion signals of the elderly can be monitored. Evaluation of the compression algorithm shows that it has a high compression ratio and low distortion and consumes little time, which is suitable for home gateways. The proposed system has good scalability, and it is simple to operate. It has the potential to provide long-term and continuous home health monitoring services for the elderly.

Design and implementation of a 3-lead ECG wireless remote monitoring system

NASA Astrophysics Data System (ADS)

Zhang, Shi; Jia, Xiaonan; Shang, Shuai

2006-11-01

Cardiovascular disease is one of the main diseases that menaces human health. It is necessary to monitor the patient's real-time electrocardiograph (ECG) for a long time to realize diagnosis and salvage. Remote ECG monitoring system is the solution. This paper introduces the design and implement of a 3-lead ECG wireless remote monitoring system. It collects, stores and transmits user's ECG which can be received by hospital and diagnosed by doctors. The development of the whole system contains three parts, the hardware and embedded software implementation of MONITOR, software of the MONITORING CENTER, and the routing software of NETWORK CENTER. According to the clinic experimentation, this system has high reliability and utility. There will be great social and economic benefit if this system is put into use.

Telecardiology for effective healthcare services.

PubMed

Saxena, S C; Kumar, V; Giri, V K

2003-01-01

Continuous monitoring of the electrocardiogram (ECG) and other signals related to current heart activity are necessary for patients who are suffering from cardiac diseases. This paper deals with the work that has been carried out to transmit ECGs from remote sites. Software has been developed which enables the uploading of ECG data from a patient so that the physician can monitor the state of the patient from a distance and at the same time may consult other experts for a second opinion. Records can only be examined by the authorized physician after proper registration and diagnosis or prescription may be sent back to the referring site. Further consultation with the patient through a 'chat' facility is also possible. The suitability of the system over transport control protocol (TCP), internet protocol (IP), local area network (LAN), wide area network (WAN) and World Wide Web (WWW) has been assessed. The bandwidth, latency, availability, security and ubiquity have also been discussed. A study has also been undertaken in order to make the system compatible with available bandwidths and to find out which one out of a number of available techniques is most efficient for ECG data compression. The results indicate that the scheme is suitable for telecardiology and can form part of an overall telemedicine system in a health care network.

Implementation of a WAP-based telemedicine system for patient monitoring.

PubMed

Hung, Kevin; Zhang, Yuan-Ting

2003-06-01

Many parties have already demonstrated telemedicine applications that use cellular phones and the Internet. A current trend in telecommunication is the convergence of wireless communication and computer network technologies, and the emergence of wireless application protocol (WAP) devices is an example. Since WAP will also be a common feature found in future mobile communication devices, it is worthwhile to investigate its use in telemedicine. This paper describes the implementation and experiences with a WAP-based telemedicine system for patient-monitoring that has been developed in our laboratory. It utilizes WAP devices as mobile access terminals for general inquiry and patient-monitoring services. Authorized users can browse the patients' general data, monitored blood pressure (BP), and electrocardiogram (ECG) on WAP devices in store-and-forward mode. The applications, written in wireless markup language (WML), WMLScript, and Perl, resided in a content server. A MySQL relational database system was set up to store the BP readings, ECG data, patient records, clinic and hospital information, and doctors' appointments with patients. A wireless ECG subsystem was built for recording ambulatory ECG in an indoor environment and for storing ECG data into the database. For testing, a WAP phone compliant with WAP 1.1 was used at GSM 1800 MHz by circuit-switched data (CSD) to connect to the content server through a WAP gateway, which was provided by a mobile phone service provider in Hong Kong. Data were successfully retrieved from the database and displayed on the WAP phone. The system shows how WAP can be feasible in remote patient-monitoring and patient data retrieval.

Compressed sensing of ECG signal for wireless system with new fast iterative method.

PubMed

Tawfic, Israa; Kayhan, Sema

2015-12-01

Recent experiments in wireless body area network (WBAN) show that compressive sensing (CS) is a promising tool to compress the Electrocardiogram signal ECG signal. The performance of CS is based on algorithms use to reconstruct exactly or approximately the original signal. In this paper, we present two methods work with absence and presence of noise, these methods are Least Support Orthogonal Matching Pursuit (LS-OMP) and Least Support Denoising-Orthogonal Matching Pursuit (LSD-OMP). The algorithms achieve correct support recovery without requiring sparsity knowledge. We derive an improved restricted isometry property (RIP) based conditions over the best known results. The basic procedures are done by observational and analytical of a different Electrocardiogram signal downloaded them from PhysioBankATM. Experimental results show that significant performance in term of reconstruction quality and compression rate can be obtained by these two new proposed algorithms, and help the specialist gathering the necessary information from the patient in less time if we use Magnetic Resonance Imaging (MRI) application, or reconstructed the patient data after sending it through the network. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

The Zigbee wireless ECG measurement system design with a motion artifact remove algorithm by using adaptive filter and moving weighted factor

NASA Astrophysics Data System (ADS)

Kwon, Hyeokjun; Oh, Sechang; Varadan, Vijay K.

2012-04-01

The Electrocardiogram(ECG) signal is one of the bio-signals to check body status. Traditionally, the ECG signal was checked in the hospital. In these days, as the number of people who is interesting with periodic their health check increase, the requirement of self-diagnosis system development is being increased as well. Ubiquitous concept is one of the solutions of the self-diagnosis system. Zigbee wireless sensor network concept is a suitable technology to satisfy the ubiquitous concept. In measuring ECG signal, there are several kinds of methods in attaching electrode on the body called as Lead I, II, III, etc. In addition, several noise components occurred by different measurement situation such as experimenter's respiration, sensor's contact point movement, and the wire movement attached on sensor are included in pure ECG signal. Therefore, this paper is based on the two kinds of development concept. The first is the Zibee wireless communication technology, which can provide convenience and simpleness, and the second is motion artifact remove algorithm, which can detect clear ECG signal from measurement subject. The motion artifact created by measurement subject's movement or even respiration action influences to distort ECG signal, and the frequency distribution of the noises is around from 0.2Hz to even 30Hz. The frequencies are duplicated in actual ECG signal frequency, so it is impossible to remove the artifact without any distortion of ECG signal just by using low-pass filter or high-pass filter. The suggested algorithm in this paper has two kinds of main parts to extract clear ECG signal from measured original signal through an electrode. The first part is to extract motion noise signal from measured signal, and the second part is to extract clear ECG by using extracted motion noise signal and measured original signal. The paper suggests several techniques in order to extract motion noise signal such as predictability estimation theory, low pass filter, a filter including a moving weighted factor, peak to peak detection, and interpolation techniques. In addition, this paper introduces an adaptive filter in order to extract clear ECG signal by using extracted baseline noise signal and measured signal from sensor.

Bivariate empirical mode decomposition for ECG-based biometric identification with emotional data.

PubMed

Ferdinando, Hany; Seppanen, Tapio; Alasaarela, Esko

2017-07-01

Emotions modulate ECG signals such that they might affect ECG-based biometric identification in real life application. It motivated in finding good feature extraction methods where the emotional state of the subjects has minimum impacts. This paper evaluates feature extraction based on bivariate empirical mode decomposition (BEMD) for biometric identification when emotion is considered. Using the ECG signal from the Mahnob-HCI database for affect recognition, the features were statistical distributions of dominant frequency after applying BEMD analysis to ECG signals. The achieved accuracy was 99.5% with high consistency using kNN classifier in 10-fold cross validation to identify 26 subjects when the emotional states of the subjects were ignored. When the emotional states of the subject were considered, the proposed method also delivered high accuracy, around 99.4%. We concluded that the proposed method offers emotion-independent features for ECG-based biometric identification. The proposed method needs more evaluation related to testing with other classifier and variation in ECG signals, e.g. normal ECG vs. ECG with arrhythmias, ECG from various ages, and ECG from other affective databases.

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment

PubMed Central

Rundo, Francesco; Ortis, Alessandro

2018-01-01

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG “combo” pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting “clean” PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach. PMID:29385774

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment.

PubMed

Rundo, Francesco; Conoci, Sabrina; Ortis, Alessandro; Battiato, Sebastiano

2018-01-30

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG "combo" pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting "clean" PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.

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.

Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout.

PubMed

Das, Anup; Pradhapan, Paruthi; Groenendaal, Willemijn; Adiraju, Prathyusha; Rajan, Raj Thilak; Catthoor, Francky; Schaafsma, Siebren; Krichmar, Jeffrey L; Dutt, Nikil; Van Hoof, Chris

2018-03-01

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine learning technique to estimate heart-rate from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery-life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects is considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices. Copyright © 2018 Elsevier Ltd. All rights reserved.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal

PubMed Central

Ramkumar, Barathram; Sabarimalai Manikandan, M.

2017-01-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal. PMID:28529758

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

PubMed

Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

2017-02-01

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

Novel Tool for Complete Digitization of Paper Electrocardiography Data.

PubMed

Ravichandran, Lakshminarayan; Harless, Chris; Shah, Amit J; Wick, Carson A; Mcclellan, James H; Tridandapani, Srini

We present a Matlab-based tool to convert electrocardiography (ECG) information from paper charts into digital ECG signals. The tool can be used for long-term retrospective studies of cardiac patients to study the evolving features with prognostic value. To perform the conversion, we: 1) detect the graphical grid on ECG charts using grayscale thresholding; 2) digitize the ECG signal based on its contour using a column-wise pixel scan; and 3) use template-based optical character recognition to extract patient demographic information from the paper ECG in order to interface the data with the patients' medical record. To validate the digitization technique: 1) correlation between the digital signals and signals digitized from paper ECG are performed and 2) clinically significant ECG parameters are measured and compared from both the paper-based ECG signals and the digitized ECG. The validation demonstrates a correlation value of 0.85-0.9 between the digital ECG signal and the signal digitized from the paper ECG. There is a high correlation in the clinical parameters between the ECG information from the paper charts and digitized signal, with intra-observer and inter-observer correlations of 0.8-0.9 (p < 0.05), and kappa statistics ranging from 0.85 (inter-observer) to 1.00 (intra-observer). The important features of the ECG signal, especially the QRST complex and the associated intervals, are preserved by obtaining the contour from the paper ECG. The differences between the measures of clinically important features extracted from the original signal and the reconstructed signal are insignificant, thus highlighting the accuracy of this technique. Using this type of ECG digitization tool to carry out retrospective studies on large databases, which rely on paper ECG records, studies of emerging ECG features can be performed. In addition, this tool can be used to potentially integrate digitized ECG information with digital ECG analysis programs and with the patient's electronic medical record.

Automated Agatston score computation in non-ECG gated CT scans using deep learning

NASA Astrophysics Data System (ADS)

Cano-Espinosa, Carlos; González, Germán.; Washko, George R.; Cazorla, Miguel; San José Estépar, Raúl

2018-03-01

Introduction: The Agatston score is a well-established metric of cardiovascular disease related to clinical outcomes. It is computed from CT scans by a) measuring the volume and intensity of the atherosclerotic plaques and b) aggregating such information in an index. Objective: To generate a convolutional neural network that inputs a non-contrast chest CT scan and outputs the Agatston score associated with it directly, without a prior segmentation of Coronary Artery Calcifications (CAC). Materials and methods: We use a database of 5973 non-contrast non-ECG gated chest CT scans where the Agatston score has been manually computed. The heart of each scan is cropped automatically using an object detector. The database is split in 4973 cases for training and 1000 for testing. We train a 3D deep convolutional neural network to regress the Agatston score directly from the extracted hearts. Results: The proposed method yields a Pearson correlation coefficient of r = 0.93; p <= 0.0001 against manual reference standard in the 1000 test cases. It further stratifies correctly 72.6% of the cases with respect to standard risk groups. This compares to more complex state-of-the-art methods based on prior segmentations of the CACs, which achieve r = 0.94 in ECG-gated pulmonary CT. Conclusions: A convolutional neural network can regress the Agatston score from the image of the heart directly, without a prior segmentation of the CACs. This is a new and simpler paradigm in the Agatston score computation that yields similar results to the state-of-the-art literature.

Ubiquitous wireless ECG recording: a powerful tool physicians should embrace.

PubMed

Saxon, Leslie A

2013-04-01

The use of smart phones has increased dramatically and there are nearly a billion users on 3G and 4G networks worldwide. Nearly 60% of the U.S. population uses smart phones to access the internet, and smart phone sales now surpass those of desktop and laptop computers. The speed of wireless communication technology on 3G and 4G networks and the widespread adoption and use of iOS equipped smart phones (Apple Inc., Cupertino, CA, USA) provide infrastructure for the transmission of wireless biomedical data, including ECG data. These technologies provide an unprecedented opportunity for physicians to continually access data that can be used to detect issues before symptoms occur or to have definitive data when symptoms are present. The technology also greatly empowers and enables the possibility for unprecedented patient participation in their own medical education and health status as well as that of their social network. As patient advocates, physicians and particularly cardiac electrophysiologists should embrace the future and promise of wireless ECG recording, a technology solution that can truly scale across the global population. © 2013 Wiley Periodicals, Inc.

Electrocardiographic Characteristics of Potential Organ Donors and Associations with Cardiac Allograft Utilization

PubMed Central

Khush, Kiran K.; Menza, Rebecca; Nguyen, John; Goldstein, Benjamin A.; Zaroff, Jonathan G.; Drew, Barbara J.

2012-01-01

Background Current regulations require that all cardiac allograft offers for transplantation must include an interpreted 12-lead electrocardiogram (ECG). However, little is known about the expected ECG findings in potential organ donors, or the clinical significance of any identified abnormalities in terms of cardiac allograft function and suitability for transplantation. Methods and Results A single experienced reviewer interpreted the first ECG obtained after brainstem herniation in 980 potential organ donors managed by the California Transplant Donor Network from 2002-2007. ECG abnormalities were summarized, and associations between specific ECG findings and cardiac allograft utilization for transplantation were studied. ECG abnormalities were present in 51% of all cases reviewed. The most common abnormalities included voltage criteria for left ventricular hypertrophy (LVH), prolongation of the corrected QT interval (QTc), and repolarization changes (ST/T wave abnormalities). Fifty seven percent of potential cardiac allografts in this cohort were accepted for transplantation. LVH on ECG was a strong predictor of allograft non-utilization. No significant associations were seen between QTc prolongation, repolarization changes and allograft utilization for transplantation, after adjusting for donor clinical variables and echocardiographic findings. Conclusions We have performed the first comprehensive study of ECG findings in potential donors for cardiac transplantation. Many of the common ECG abnormalities seen in organ donors may result from the heightened state of sympathetic activation that occurs after brainstem herniation, and are not associated with allograft utilization for transplantation. PMID:22615333

Matched Filtering for Heart Rate Estimation on Compressive Sensing ECG Measurements.

PubMed

Da Poian, Giulia; Rozell, Christopher J; Bernardini, Riccardo; Rinaldo, Roberto; Clifford, Gari D

2017-09-14

Compressive Sensing (CS) has recently been applied as a low complexity compression framework for long-term monitoring of electrocardiogram signals using Wireless Body Sensor Networks. Long-term recording of ECG signals can be useful for diagnostic purposes and to monitor the evolution of several widespread diseases. In particular, beat to beat intervals provide important clinical information, and these can be derived from the ECG signal by computing the distance between QRS complexes (R-peaks). Numerous methods for R-peak detection are available for uncompressed ECG. However, in case of compressed sensed data, signal reconstruction can be performed with relatively complex optimisation algorithms, which may require significant energy consumption. This article addresses the problem of hearth rate estimation from compressive sensing electrocardiogram (ECG) recordings, avoiding the reconstruction of the entire signal. We consider a framework where the ECG signals are represented under the form of CS linear measurements. The QRS locations are estimated in the compressed domain by computing the correlation of the compressed ECG and a known QRS template. Experiments on actual ECG signals show that our novel solution is competitive with methods applied to the reconstructed signals. Avoiding the reconstruction procedure, the proposed method proves to be very convenient for real-time, low-power applications.

Sleep versus wake classification from heart rate variability using computational intelligence: consideration of rejection in classification models.

PubMed

Lewicke, Aaron; Sazonov, Edward; Corwin, Michael J; Neuman, Michael; Schuckers, Stephanie

2008-01-01

Reliability of classification performance is important for many biomedical applications. A classification model which considers reliability in the development of the model such that unreliable segments are rejected would be useful, particularly, in large biomedical data sets. This approach is demonstrated in the development of a technique to reliably determine sleep and wake using only the electrocardiogram (ECG) of infants. Typically, sleep state scoring is a time consuming task in which sleep states are manually derived from many physiological signals. The method was tested with simultaneous 8-h ECG and polysomnogram (PSG) determined sleep scores from 190 infants enrolled in the collaborative home infant monitoring evaluation (CHIME) study. Learning vector quantization (LVQ) neural network, multilayer perceptron (MLP) neural network, and support vector machines (SVMs) are tested as the classifiers. After systematic rejection of difficult to classify segments, the models can achieve 85%-87% correct classification while rejecting only 30% of the data. This corresponds to a Kappa statistic of 0.65-0.68. With rejection, accuracy improves by about 8% over a model without rejection. Additionally, the impact of the PSG scored indeterminate state epochs is analyzed. The advantages of a reliable sleep/wake classifier based only on ECG include high accuracy, simplicity of use, and low intrusiveness. Reliability of the classification can be built directly in the model, such that unreliable segments are rejected.

Novel Tool for Complete Digitization of Paper Electrocardiography Data

PubMed Central

Harless, Chris; Shah, Amit J.; Wick, Carson A.; Mcclellan, James H.

2013-01-01

Objective: We present a Matlab-based tool to convert electrocardiography (ECG) information from paper charts into digital ECG signals. The tool can be used for long-term retrospective studies of cardiac patients to study the evolving features with prognostic value. Methods and procedures: To perform the conversion, we: 1) detect the graphical grid on ECG charts using grayscale thresholding; 2) digitize the ECG signal based on its contour using a column-wise pixel scan; and 3) use template-based optical character recognition to extract patient demographic information from the paper ECG in order to interface the data with the patients' medical record. To validate the digitization technique: 1) correlation between the digital signals and signals digitized from paper ECG are performed and 2) clinically significant ECG parameters are measured and compared from both the paper-based ECG signals and the digitized ECG. Results: The validation demonstrates a correlation value of 0.85–0.9 between the digital ECG signal and the signal digitized from the paper ECG. There is a high correlation in the clinical parameters between the ECG information from the paper charts and digitized signal, with intra-observer and inter-observer correlations of 0.8–0.9 \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$({\\rm p}<{0.05})$\\end{document}, and kappa statistics ranging from 0.85 (inter-observer) to 1.00 (intra-observer). Conclusion: The important features of the ECG signal, especially the QRST complex and the associated intervals, are preserved by obtaining the contour from the paper ECG. The differences between the measures of clinically important features extracted from the original signal and the reconstructed signal are insignificant, thus highlighting the accuracy of this technique. Clinical impact: Using this type of ECG digitization tool to carry out retrospective studies on large databases, which rely on paper ECG records, studies of emerging ECG features can be performed. In addition, this tool can be used to potentially integrate digitized ECG information with digital ECG analysis programs and with the patient's electronic medical record. PMID:26594601

Patient-Specific Deep Architectural Model for ECG Classification

PubMed Central

Luo, Kan; Cuschieri, Alfred

2017-01-01

Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition. PMID:29065597

Mobile cloud-computing-based healthcare service by noncontact ECG monitoring.

PubMed

Fong, Ee-May; Chung, Wan-Young

2013-12-02

Noncontact electrocardiogram (ECG) measurement technique has gained popularity these days owing to its noninvasive features and convenience in daily life use. This paper presents mobile cloud computing for a healthcare system where a noncontact ECG measurement method is employed to capture biomedical signals from users. Healthcare service is provided to continuously collect biomedical signals from multiple locations. To observe and analyze the ECG signals in real time, a mobile device is used as a mobile monitoring terminal. In addition, a personalized healthcare assistant is installed on the mobile device; several healthcare features such as health status summaries, medication QR code scanning, and reminders are integrated into the mobile application. Health data are being synchronized into the healthcare cloud computing service (Web server system and Web server dataset) to ensure a seamless healthcare monitoring system and anytime and anywhere coverage of network connection is available. Together with a Web page application, medical data are easily accessed by medical professionals or family members. Web page performance evaluation was conducted to ensure minimal Web server latency. The system demonstrates better availability of off-site and up-to-the-minute patient data, which can help detect health problems early and keep elderly patients out of the emergency room, thus providing a better and more comprehensive healthcare cloud computing service.

Mobile Cloud-Computing-Based Healthcare Service by Noncontact ECG Monitoring

PubMed Central

Fong, Ee-May; Chung, Wan-Young

2013-01-01

Noncontact electrocardiogram (ECG) measurement technique has gained popularity these days owing to its noninvasive features and convenience in daily life use. This paper presents mobile cloud computing for a healthcare system where a noncontact ECG measurement method is employed to capture biomedical signals from users. Healthcare service is provided to continuously collect biomedical signals from multiple locations. To observe and analyze the ECG signals in real time, a mobile device is used as a mobile monitoring terminal. In addition, a personalized healthcare assistant is installed on the mobile device; several healthcare features such as health status summaries, medication QR code scanning, and reminders are integrated into the mobile application. Health data are being synchronized into the healthcare cloud computing service (Web server system and Web server dataset) to ensure a seamless healthcare monitoring system and anytime and anywhere coverage of network connection is available. Together with a Web page application, medical data are easily accessed by medical professionals or family members. Web page performance evaluation was conducted to ensure minimal Web server latency. The system demonstrates better availability of off-site and up-to-the-minute patient data, which can help detect health problems early and keep elderly patients out of the emergency room, thus providing a better and more comprehensive healthcare cloud computing service. PMID:24316562

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

PubMed Central

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-01-01

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

A sub-nJ CMOS ECG classifier for wireless smart sensor.

PubMed

Chollet, Paul; Pallas, Remi; Lahuec, Cyril; Arzel, Matthieu; Seguin, Fabrice

2017-07-01

Body area sensor networks hold the promise of more efficient and cheaper medical care services through the constant monitoring of physiological markers such as heart beats. Continuously transmitting the electrocardiogram (ECG) signal requires most of the wireless ECG sensor energy budget. This paper presents the analog implantation of a classifier for ECG signals that can be embedded onto a sensor. The classifier is a sparse neural associative memory. It is implemented using the ST 65 nm CMOS technology and requires only 234 pJ per classification while achieving a 93.6% classification accuracy. The energy requirement is 6 orders of magnitude lower than a digital accelerator that performs a similar task. The lifespan of the resulting sensor is 191 times as large as that of a sensor sending all the data.

Physics-driven Spatiotemporal Regularization for High-dimensional Predictive Modeling: A Novel Approach to Solve the Inverse ECG Problem

NASA Astrophysics Data System (ADS)

Yao, Bing; Yang, Hui

2016-12-01

This paper presents a novel physics-driven spatiotemporal regularization (STRE) method for high-dimensional predictive modeling in complex healthcare systems. This model not only captures the physics-based interrelationship between time-varying explanatory and response variables that are distributed in the space, but also addresses the spatial and temporal regularizations to improve the prediction performance. The STRE model is implemented to predict the time-varying distribution of electric potentials on the heart surface based on the electrocardiogram (ECG) data from the distributed sensor network placed on the body surface. The model performance is evaluated and validated in both a simulated two-sphere geometry and a realistic torso-heart geometry. Experimental results show that the STRE model significantly outperforms other regularization models that are widely used in current practice such as Tikhonov zero-order, Tikhonov first-order and L1 first-order regularization methods.

Adaptive Fourier decomposition based ECG denoising.

PubMed

Wang, Ze; Wan, Feng; Wong, Chi Man; Zhang, Liming

2016-10-01

A novel ECG denoising method is proposed based on the adaptive Fourier decomposition (AFD). The AFD decomposes a signal according to its energy distribution, thereby making this algorithm suitable for separating pure ECG signal and noise with overlapping frequency ranges but different energy distributions. A stop criterion for the iterative decomposition process in the AFD is calculated on the basis of the estimated signal-to-noise ratio (SNR) of the noisy signal. The proposed AFD-based method is validated by the synthetic ECG signal using an ECG model and also real ECG signals from the MIT-BIH Arrhythmia Database both with additive Gaussian white noise. Simulation results of the proposed method show better performance on the denoising and the QRS detection in comparing with major ECG denoising schemes based on the wavelet transform, the Stockwell transform, the empirical mode decomposition, and the ensemble empirical mode decomposition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Design of a smart ECG garment based on conductive textile electrode and flexible printed circuit board.

PubMed

Cai, Zhipeng; Luo, Kan; Liu, Chengyu; Li, Jianqing

2017-08-09

A smart electrocardiogram (ECG) garment system was designed for continuous, non-invasive and comfortable ECG monitoring, which mainly consists of four components: Conductive textile electrode, garment, flexible printed circuit board (FPCB)-based ECG processing module and android application program. Conductive textile electrode and FPCB-based ECG processing module (6.8 g, 55 mm × 53 mm × 5 mm) are identified as two key techniques to improve the system's comfort and flexibility. Preliminary experimental results verified that the textile electrodes with circle shape, 40 mm size in diameter, and 5 mm thickness sponge are best suited for the long-term ECG monitoring application. The tests on the whole system confirmed that the designed smart garment can obtain long-term ECG recordings with high signal quality.

Coronary Artery Diagnosis Aided by Neural Network

NASA Astrophysics Data System (ADS)

Stefko, Kamil

2007-01-01

Coronary artery disease is due to atheromatous narrowing and subsequent occlusion of the coronary vessel. Application of optimised feed forward multi-layer back propagation neural network (MLBP) for detection of narrowing in coronary artery vessels is presented in this paper. The research was performed using 580 data records from traditional ECG exercise test confirmed by coronary arteriography results. Each record of training database included description of the state of a patient providing input data for the neural network. Level and slope of ST segment of a 12 lead ECG signal recorded at rest and after effort (48 floating point values) was the main component of input data for neural network was. Coronary arteriography results (verified the existence or absence of more than 50% stenosis of the particular coronary vessels) were used as a correct neural network training output pattern. More than 96% of cases were correctly recognised by especially optimised and a thoroughly verified neural network. Leave one out method was used for neural network verification so 580 data records could be used for training as well as for verification of neural network.

ECG Sensor Card with Evolving RBP Algorithms for Human Verification.

PubMed

Tseng, Kuo-Kun; Huang, Huang-Nan; Zeng, Fufu; Tu, Shu-Yi

2015-08-21

It is known that cardiac and respiratory rhythms in electrocardiograms (ECGs) are highly nonlinear and non-stationary. As a result, most traditional time-domain algorithms are inadequate for characterizing the complex dynamics of the ECG. This paper proposes a new ECG sensor card and a statistical-based ECG algorithm, with the aid of a reduced binary pattern (RBP), with the aim of achieving faster ECG human identity recognition with high accuracy. The proposed algorithm has one advantage that previous ECG algorithms lack-the waveform complex information and de-noising preprocessing can be bypassed; therefore, it is more suitable for non-stationary ECG signals. Experimental results tested on two public ECG databases (MIT-BIH) from MIT University confirm that the proposed scheme is feasible with excellent accuracy, low complexity, and speedy processing. To be more specific, the advanced RBP algorithm achieves high accuracy in human identity recognition and is executed at least nine times faster than previous algorithms. Moreover, based on the test results from a long-term ECG database, the evolving RBP algorithm also demonstrates superior capability in handling long-term and non-stationary ECG signals.

Object-oriented analysis and design of an ECG storage and retrieval system integrated with an HIS.

PubMed

Wang, C; Ohe, K; Sakurai, T; Nagase, T; Kaihara, S

1996-03-01

For a hospital information system, object-oriented methodology plays an increasingly important role, especially for the management of digitized data, e.g., the electrocardiogram, electroencephalogram, electromyogram, spirogram, X-ray, CT and histopathological images, which are not yet computerized in most hospitals. As a first step in an object-oriented approach to hospital information management and storing medical data in an object-oriented database, we connected electrocardiographs to a hospital network and established the integration of ECG storage and retrieval systems with a hospital information system. In this paper, the object-oriented analysis and design of the ECG storage and retrieval systems is reported.

A cloud computing based 12-lead ECG telemedicine service

PubMed Central

2012-01-01

Background Due to the great variability of 12-lead ECG instruments and medical specialists’ interpretation skills, it remains a challenge to deliver rapid and accurate 12-lead ECG reports with senior cardiologists’ decision making support in emergency telecardiology. Methods We create a new cloud and pervasive computing based 12-lead Electrocardiography (ECG) service to realize ubiquitous 12-lead ECG tele-diagnosis. Results This developed service enables ECG to be transmitted and interpreted via mobile phones. That is, tele-consultation can take place while the patient is on the ambulance, between the onsite clinicians and the off-site senior cardiologists, or among hospitals. Most importantly, this developed service is convenient, efficient, and inexpensive. Conclusions This cloud computing based ECG tele-consultation service expands the traditional 12-lead ECG applications onto the collaboration of clinicians at different locations or among hospitals. In short, this service can greatly improve medical service quality and efficiency, especially for patients in rural areas. This service has been evaluated and proved to be useful by cardiologists in Taiwan. PMID:22838382

A cloud computing based 12-lead ECG telemedicine service.

PubMed

Hsieh, Jui-Chien; Hsu, Meng-Wei

2012-07-28

Due to the great variability of 12-lead ECG instruments and medical specialists' interpretation skills, it remains a challenge to deliver rapid and accurate 12-lead ECG reports with senior cardiologists' decision making support in emergency telecardiology. We create a new cloud and pervasive computing based 12-lead Electrocardiography (ECG) service to realize ubiquitous 12-lead ECG tele-diagnosis. This developed service enables ECG to be transmitted and interpreted via mobile phones. That is, tele-consultation can take place while the patient is on the ambulance, between the onsite clinicians and the off-site senior cardiologists, or among hospitals. Most importantly, this developed service is convenient, efficient, and inexpensive. This cloud computing based ECG tele-consultation service expands the traditional 12-lead ECG applications onto the collaboration of clinicians at different locations or among hospitals. In short, this service can greatly improve medical service quality and efficiency, especially for patients in rural areas. This service has been evaluated and proved to be useful by cardiologists in Taiwan.

The Development of a Portable ECG Monitor Based on DSP

NASA Astrophysics Data System (ADS)

Nan, CHI Jian; Tao, YAN Yan; Meng Chen, LIU; Li, YANG

With the advent of global information, researches of Smart Home system are in the ascendant, the ECG real-time detection, and wireless transmission of ECG become more useful. In order to achieve the purpose we developed a portable ECG monitor which achieves the purpose of cardiac disease remote monitoring, and will be used in the physical and psychological disease surveillance in smart home system, we developed this portable ECG Monitor, based on the analysis of existing ECG Monitor, using TMS320F2812 as the core controller, which complete the signal collection, storage, processing, waveform display and transmission.

A reliable transmission protocol for ZigBee-based wireless patient monitoring.

PubMed

Chen, Shyr-Kuen; Kao, Tsair; Chan, Chia-Tai; Huang, Chih-Ning; Chiang, Chih-Yen; Lai, Chin-Yu; Tung, Tse-Hua; Wang, Pi-Chung

2012-01-01

Patient monitoring systems are gaining their importance as the fast-growing global elderly population increases demands for caretaking. These systems use wireless technologies to transmit vital signs for medical evaluation. In a multihop ZigBee network, the existing systems usually use broadcast or multicast schemes to increase the reliability of signals transmission; however, both the schemes lead to significantly higher network traffic and end-to-end transmission delay. In this paper, we present a reliable transmission protocol based on anycast routing for wireless patient monitoring. Our scheme automatically selects the closest data receiver in an anycast group as a destination to reduce the transmission latency as well as the control overhead. The new protocol also shortens the latency of path recovery by initiating route recovery from the intermediate routers of the original path. On the basis of a reliable transmission scheme, we implement a ZigBee device for fall monitoring, which integrates fall detection, indoor positioning, and ECG monitoring. When the triaxial accelerometer of the device detects a fall, the current position of the patient is transmitted to an emergency center through a ZigBee network. In order to clarify the situation of the fallen patient, 4-s ECG signals are also transmitted. Our transmission scheme ensures the successful transmission of these critical messages. The experimental results show that our scheme is fast and reliable. We also demonstrate that our devices can seamlessly integrate with the next generation technology of wireless wide area network, worldwide interoperability for microwave access, to achieve real-time patient monitoring.

Matrix-Inversion-Free Compressed Sensing With Variable Orthogonal Multi-Matching Pursuit Based on Prior Information for ECG Signals.

PubMed

Cheng, Yih-Chun; Tsai, Pei-Yun; Huang, Ming-Hao

2016-05-19

Low-complexity compressed sensing (CS) techniques for monitoring electrocardiogram (ECG) signals in wireless body sensor network (WBSN) are presented. The prior probability of ECG sparsity in the wavelet domain is first exploited. Then, variable orthogonal multi-matching pursuit (vOMMP) algorithm that consists of two phases is proposed. In the first phase, orthogonal matching pursuit (OMP) algorithm is adopted to effectively augment the support set with reliable indices and in the second phase, the orthogonal multi-matching pursuit (OMMP) is employed to rescue the missing indices. The reconstruction performance is thus enhanced with the prior information and the vOMMP algorithm. Furthermore, the computation-intensive pseudo-inverse operation is simplified by the matrix-inversion-free (MIF) technique based on QR decomposition. The vOMMP-MIF CS decoder is then implemented in 90 nm CMOS technology. The QR decomposition is accomplished by two systolic arrays working in parallel. The implementation supports three settings for obtaining 40, 44, and 48 coefficients in the sparse vector. From the measurement result, the power consumption is 11.7 mW at 0.9 V and 12 MHz. Compared to prior chip implementations, our design shows good hardware efficiency and is suitable for low-energy applications.

IEEE-802.15.4-based low-power body sensor node with RF energy harvester.

PubMed

Tran, Thang Viet; Chung, Wan-Young

2014-01-01

This paper proposes the design and implementation of a low-voltage and low-power body sensor node based on the IEEE 802.15.4 standard to collect electrocardiography (ECG) and photoplethysmography (PPG) signals. To achieve compact size, low supply voltage, and low power consumption, the proposed platform is integrated into a ZigBee mote, which contains a DC-DC booster, a PPG sensor interface module, and an ECG front-end circuit that has ultra-low current consumption. The input voltage of the proposed node is very low and has a wide range, from 0.65 V to 3.3 V. An RF energy harvester is also designed to charge the battery during the working mode or standby mode of the node. The power consumption of the proposed node reaches 14 mW in working mode to prolong the battery lifetime. The software is supported by the nesC language under the TinyOS environment, which enables the proposed node to be easily configured to function as an individual health monitoring node or a node in a wireless body sensor network (BSN). The proposed node is used to set up a wireless BSN that can simultaneously collect ECG and PPG signals and monitor the results on the personal computer.

Extraction of ECG signal with adaptive filter for hearth abnormalities detection

NASA Astrophysics Data System (ADS)

Turnip, Mardi; Saragih, Rijois. I. E.; Dharma, Abdi; Esti Kusumandari, Dwi; Turnip, Arjon; Sitanggang, Delima; Aisyah, Siti

2018-04-01

This paper demonstrates an adaptive filter method for extraction ofelectrocardiogram (ECG) feature in hearth abnormalities detection. In particular, electrocardiogram (ECG) is a recording of the heart's electrical activity by capturing a tracingof cardiac electrical impulse as it moves from the atrium to the ventricles. The applied algorithm is to evaluate and analyze ECG signals for abnormalities detection based on P, Q, R and S peaks. In the first phase, the real-time ECG data is acquired and pre-processed. In the second phase, the procured ECG signal is subjected to feature extraction process. The extracted features detect abnormal peaks present in the waveform. Thus the normal and abnormal ECG signal could be differentiated based on the features extracted.

[Lossless ECG compression algorithm with anti- electromagnetic interference].

PubMed

Guan, Shu-An

2005-03-01

Based on the study of ECG signal features, a new lossless ECG compression algorithm is put forward here. We apply second-order difference operation with anti- electromagnetic interference to original ECG signals and then, compress the result by the escape-based coding model. In spite of serious 50Hz-interference, the algorithm is still capable of obtaining a high compression ratio.

Artifacts and noise removal in electrocardiograms using independent component analysis.

PubMed

Chawla, M P S; Verma, H K; Kumar, Vinod

2008-09-26

Independent component analysis (ICA) is a novel technique capable of separating independent components from electrocardiogram (ECG) complex signals. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG recordings. ICA is applied to remove artifacts and noise in ECG segments of either an individual ECG CSE data base file or all files. The reconstructed ECGs are compared with the original ECG signal. For the four special cases discussed, the R-Peak magnitudes of the CSE data base ECG waveforms before and after applying ICA are also found. In the results, it is shown that in most of the cases, the percentage error in reconstruction is very small. The results show that there is a significant improvement in signal quality, i.e. SNR. All the ECG recording cases dealt showed an improved ECG appearance after the use of ICA. This establishes the efficacy of ICA in elimination of noise and artifacts in electrocardiograms.

Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network

PubMed Central

Li, Meina; Kwak, Keun-Chang; Kim, Youn Tae

2016-01-01

Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN) for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR) and movement index (MI) monitoring. The embedded incremental network includes linear regression (LR) and RBFNN based on context-based fuzzy c-means (CFCM) clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model. PMID:27669249

Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview

NASA Astrophysics Data System (ADS)

Han, G.; Lin, B.; Xu, Z.

2017-03-01

Electrocardiogram (ECG) signal is nonlinear and non-stationary weak signal which reflects whether the heart is functioning normally or abnormally. ECG signal is susceptible to various kinds of noises such as high/low frequency noises, powerline interference and baseline wander. Hence, the removal of noises from ECG signal becomes a vital link in the ECG signal processing and plays a significant role in the detection and diagnosis of heart diseases. The review will describe the recent developments of ECG signal denoising based on Empirical Mode Decomposition (EMD) technique including high frequency noise removal, powerline interference separation, baseline wander correction, the combining of EMD and Other Methods, EEMD technique. EMD technique is a quite potential and prospective but not perfect method in the application of processing nonlinear and non-stationary signal like ECG signal. The EMD combined with other algorithms is a good solution to improve the performance of noise cancellation. The pros and cons of EMD technique in ECG signal denoising are discussed in detail. Finally, the future work and challenges in ECG signal denoising based on EMD technique are clarified.

A novel biometric authentication approach using ECG and EMG signals.

PubMed

Belgacem, Noureddine; Fournier, Régis; Nait-Ali, Amine; Bereksi-Reguig, Fethi

2015-05-01

Security biometrics is a secure alternative to traditional methods of identity verification of individuals, such as authentication systems based on user name and password. Recently, it has been found that the electrocardiogram (ECG) signal formed by five successive waves (P, Q, R, S and T) is unique to each individual. In fact, better than any other biometrics' measures, it delivers proof of subject's being alive as extra information which other biometrics cannot deliver. The main purpose of this work is to present a low-cost method for online acquisition and processing of ECG signals for person authentication and to study the possibility of providing additional information and retrieve personal data from an electrocardiogram signal to yield a reliable decision. This study explores the effectiveness of a novel biometric system resulting from the fusion of information and knowledge provided by ECG and EMG (Electromyogram) physiological recordings. It is shown that biometrics based on these ECG/EMG signals offers a novel way to robustly authenticate subjects. Five ECG databases (MIT-BIH, ST-T, NSR, PTB and ECG-ID) and several ECG signals collected in-house from volunteers were exploited. A palm-based ECG biometric system was developed where the signals are collected from the palm of the subject through a minimally intrusive one-lead ECG set-up. A total of 3750 ECG beats were used in this work. Feature extraction was performed on ECG signals using Fourier descriptors (spectral coefficients). Optimum-Path Forest classifier was used to calculate the degree of similarity between individuals. The obtained results from the proposed approach look promising for individuals' authentication.

ECG interpretation in Emergency Department residents: an update and e-learning as a resource to improve skills.

PubMed

Barthelemy, Francois X; Segard, Julien; Fradin, Philippe; Hourdin, Nicolas; Batard, Eric; Pottier, Pierre; Potel, Gilles; Montassier, Emmanuel

2017-04-01

ECG interpretation is a pivotal skill to acquire during residency, especially for Emergency Department (ED) residents. Previous studies reported that ECG interpretation competency among residents was rather low. However, the optimal resource to improve ECG interpretation skills remains unclear. The aim of our study was to compare two teaching modalities to improve the ECG interpretation skills of ED residents: e-learning and lecture-based courses. The participants were first-year and second-year ED residents, assigned randomly to the two groups. The ED residents were evaluated by means of a precourse test at the beginning of the study and a postcourse test after the e-learning and lecture-based courses. These evaluations consisted of the interpretation of 10 different ECGs. We included 39 ED residents from four different hospitals. The precourse test showed that the overall average score of ECG interpretation was 40%. Nineteen participants were then assigned to the e-learning course and 20 to the lecture-based course. Globally, there was a significant improvement in ECG interpretation skills (accuracy score=55%, P=0.0002). However, this difference was not significant between the two groups (P=0.14). Our findings showed that the ECG interpretation was not optimal and that our e-learning program may be an effective tool for enhancing ECG interpretation skills among ED residents. A large European study should be carried out to evaluate ECG interpretation skills among ED residents before the implementation of ECG learning, including e-learning strategies, during ED residency.

Deployment of an Advanced Electrocardiographic Analysis (A-ECG) to Detect Cardiovascular Risk in Career Firefighters

NASA Technical Reports Server (NTRS)

Dolezal, B. A.; Storer, T. W.; Abrazado, M.; Watne, R.; Schlegel, T. T.; Batalin, M.; Kaiser, W.; Smith, D. L.; Cooper, C. B.

2011-01-01

INTRODUCTION Sudden cardiac death is the leading cause of line of duty death among firefighters, accounting for approximately 45% of fatalities annually. Firefighters perform strenuous muscular work while wearing heavy, encapsulating personal protective equipment in high ambient temperatures, under chaotic and emotionally stressful conditions. These factors can precipitate sudden cardiac events like myocardial infarction, serious dysrhythmias, or cerebrovascular accidents in firefighters with underlying cardiovascular disease. Screening for cardiovascular risk factors is recommended but not always followed in this population. PHASER is a project charged with identifying and prioritizing risk factors in emergency responders. We have deployed an advanced ECG (A-ECG) system developed at NASA for improved sensitivity and specificity in the detection of cardiac risk. METHODS Forty-four professional firefighters were recruited to perform comprehensive baseline assessments including tests of aerobic performance and laboratory tests for fasting lipid profiles and glucose. Heart rate and conventional 12-lead ECG were obtained at rest and during incremental treadmill exercise testing (XT). In addition, a 5-min resting 12-lead A-ECG was obtained in a subset of firefighters (n=18) and transmitted over a secure networked system to a physician collaborator at NASA for advanced-ECG analysis. This A-ECG system has been proven, using myocardial perfusion and other imaging, to accurately identify a number of cardiac pathologies including coronary artery disease (CAD), left ventricular hypertrophy, hypertrophic cardiomyopathy, non-ischemic cardiomyopathy, and ischemic cardiomyopathy. RESULTS Subjects mean (SD) age was 43 (8) years, weight 91 (13) kg, and BMI of 28 (3) kg/square meter. Maximum oxygen uptake (VO2max) was 39 (9) ml/kg/min. This compares with the 45th %ile in healthy reference values and a recommended standard of 42 ml/kg/min for firefighters. The metabolic threshold (VO2Theta) above which lactate accumulates was 23 (8) ml/kg/min. The chronotropic index, a measure of cardiovascular strain during XT was 35 (8) /L compared with reference values for men of 40 /L. Total cholesterol, LDL-C and HDL-C were 202 (34),126 (29), and 55 (15) mg/dl, respectively. Fifty-one percent of subjects had .3 cardiovascular risk factors, 2 subjects had resting hypertension (BP.140/90), and 23 had pre-hypertension (.120/80 but <140/90). Seven had exaggerated exercise induced hypertension but only one had ST depression on XT ECG, at least one positive A-ECG score for CAD, and documented CAD based on cardiology referral. While all other subjects, including those with fewer risk factors, higher aerobic fitness, and normal exercise ECGs, were classified as healthy by A-ECG, there was no trend for association between risk factors and any of 20 A-ECG parameters in the grouped data. CONCLUSIONS A-ECG screening correctly identified the individual with CAD although there was no trend for A-ECG parameters to distinguish those with elevated BP or multiple risk factors but normal XT ECG. We have demonstrated that a new technology, advanced-ECG, can be introduced for remote firefighter risk assessment. This simple, time and cost-effective approach to risk identification that can be acquired remotely and transmitted securely can detect individuals potentially at risk for line-of-duty death. Additional research is needed to further document its value.

MS-QI: A Modulation Spectrum-Based ECG Quality Index for Telehealth Applications.

PubMed

Tobon V, Diana P; Falk, Tiago H; Maier, Martin

2016-08-01

As telehealth applications emerge, the need for accurate and reliable biosignal quality indices has increased. One typical modality used in remote patient monitoring is the electrocardiogram (ECG), which is inherently susceptible to several different noise sources, including environmental (e.g., powerline interference), experimental (e.g., movement artifacts), and physiological (e.g., muscle and breathing artifacts). Accurate measurement of ECG quality can allow for automated decision support systems to make intelligent decisions about patient conditions. This is particularly true for in-home monitoring applications, where the patient is mobile and the ECG signal can be severely corrupted by movement artifacts. In this paper, we propose an innovative ECG quality index based on the so-called modulation spectral signal representation. The representation quantifies the rate of change of ECG spectral components, which are shown to be different from the rate of change of typical ECG noise sources. The proposed modulation spectral-based quality index, MS-QI, was tested on 1) synthetic ECG signals corrupted by varying levels of noise, 2) single-lead recorded data using the Hexoskin garment during three activity levels (sitting, walking, running), 3) 12-lead recorded data using conventional ECG machines (Computing in Cardiology 2011 dataset), and 4) two-lead ambulatory ECG recorded from arrhythmia patients (MIT-BIH Arrhythmia Database). Experimental results showed the proposed index outperforming two conventional benchmark quality measures, particularly in the scenarios involving recorded data in real-world environments.

Fetal ECG extraction via Type-2 adaptive neuro-fuzzy inference systems.

PubMed

Ahmadieh, Hajar; Asl, Babak Mohammadzadeh

2017-04-01

We proposed a noninvasive method for separating the fetal ECG (FECG) from maternal ECG (MECG) by using Type-2 adaptive neuro-fuzzy inference systems. The method can extract FECG components from abdominal signal by using one abdominal channel, including maternal and fetal cardiac signals and other environmental noise signals, and one chest channel. The proposed algorithm detects the nonlinear dynamics of the mother's body. So, the components of the MECG are estimated from the abdominal signal. By subtracting estimated mother cardiac signal from abdominal signal, fetal cardiac signal can be extracted. This algorithm was applied on synthetic ECG signals generated based on the models developed by McSharry et al. and Behar et al. and also on DaISy real database. In environments with high uncertainty, our method performs better than the Type-1 fuzzy method. Specifically, in evaluation of the algorithm with the synthetic data based on McSharry model, for input signals with SNR of -5dB, the SNR of the extracted FECG was improved by 38.38% in comparison with the Type-1 fuzzy method. Also, the results show that increasing the uncertainty or decreasing the input SNR leads to increasing the percentage of the improvement in SNR of the extracted FECG. For instance, when the SNR of the input signal decreases to -30dB, our proposed algorithm improves the SNR of the extracted FECG by 71.06% with respect to the Type-1 fuzzy method. The same results were obtained on synthetic data based on Behar model. Our results on real database reflect the success of the proposed method to separate the maternal and fetal heart signals even if their waves overlap in time. Moreover, the proposed algorithm was applied to the simulated fetal ECG with ectopic beats and achieved good results in separating FECG from MECG. The results show the superiority of the proposed Type-2 neuro-fuzzy inference method over the Type-1 neuro-fuzzy inference and the polynomial networks methods, which is due to its capability to capture the nonlinearities of the model better. Copyright © 2017 Elsevier B.V. All rights reserved.

Multiprocessor Neural Network in Healthcare.

PubMed

Godó, Zoltán Attila; Kiss, Gábor; Kocsis, Dénes

2015-01-01

A possible way of creating a multiprocessor artificial neural network is by the use of microcontrollers. The RISC processors' high performance and the large number of I/O ports mean they are greatly suitable for creating such a system. During our research, we wanted to see if it is possible to efficiently create interaction between the artifical neural network and the natural nervous system. To achieve as much analogy to the living nervous system as possible, we created a frequency-modulated analog connection between the units. Our system is connected to the living nervous system through 128 microelectrodes. Two-way communication is provided through A/D transformation, which is even capable of testing psychopharmacons. The microcontroller-based analog artificial neural network can play a great role in medical singal processing, such as ECG, EEG etc.

Eyewitness to history: Landmarks in the development of computerized electrocardiography.

PubMed

Rautaharju, Pentti M

2016-01-01

The use of digital computers for ECG processing was pioneered in the early 1960s by two immigrants to the US, Hubert Pipberger, who initiated a collaborative VA project to collect an ECG-independent Frank lead data base, and Cesar Caceres at NIH who selected for his ECAN program standard 12-lead ECGs processed as single leads. Ray Bonner in the early 1970s placed his IBM 5880 program in a cart to print ECGs with interpretation, and computer-ECG programs were developed by Telemed, Marquette, HP-Philips and Mortara. The "Common Standards for quantitative Electrocardiography (CSE)" directed by Jos Willems evaluated nine ECG programs and eight cardiologists in clinically-defined categories. The total accuracy by a representative "average" cardiologist (75.5%) was 5.8% higher than that of the average program (69.7, p<0.001). Future comparisons of computer-based and expert reader performance are likely to show evolving results with continuing improvement of computer-ECG algorithms and changing expertise of ECG interpreters. Copyright © 2016 Elsevier Inc. All rights reserved.

A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram.

PubMed

Wu, Chung Kit; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei

2016-05-09

Globally, 1.2 million people die and 50 million people are injured annually due to traffic accidents. These traffic accidents cost $500 billion dollars. Drunk drivers are found in 40% of the traffic crashes. Existing drunk driving detection (DDD) systems do not provide accurate detection and pre-warning concurrently. Electrocardiogram (ECG) is a proven biosignal that accurately and simultaneously reflects human's biological status. In this letter, a classifier for DDD based on ECG is investigated in an attempt to reduce traffic accidents caused by drunk drivers. At this point, it appears that there is no known research or literature found on ECG classifier for DDD. To identify drunk syndromes, the ECG signals from drunk drivers are studied and analyzed. As such, a precise ECG-based DDD (ECG-DDD) using a weighted kernel is developed. From the measurements, 10 key features of ECG signals were identified. To incorporate the important features, the feature vectors are weighted in the customization of kernel functions. Four commonly adopted kernel functions are studied. Results reveal that weighted feature vectors improve the accuracy by 11% compared to the computation using the prime kernel. Evaluation shows that ECG-DDD improved the accuracy by 8% to 18% compared to prevailing methods.

Ubiquitous health monitoring system for multiple users using a ZigBee and WLAN dual-network.

PubMed

Cha, Yong Dae; Yoon, Gilwon

2009-11-01

A ubiquitous health monitoring system for multiple users was developed based on a ZigBee and wireless local area network (WLAN) dual-network. A compact biosignal monitoring unit (BMU) for measuring electrocardiogram (ECG), photoplethysmogram (PPG), and temperature was also developed. A single 8-bit microcontroller operated the BMU including most of digital filtering and wireless communication. The BMU with its case was reduced to 55 x 35 x 15 mm and 33 g. In routine use, vital signs of 6 bytes/sec (heart rate, temperature, pulse transit time) per each user were transmitted through a ZigBee module even though all the real-time data were recorded in a secure digital memory of the BMU. In an emergency or when need arises, a channel of a particular user was switched to another ZigBee module, called the emergency module, that sent all ECG and PPG waveforms in real time. Each emergency ZigBee module handled up to a few users. Data from multiple users were wirelessly received by the ZigBee receiver modules in a controller called ZigBee-WLAN gateway, where the ZigBee modules were connected to a WLAN module. This WLAN module sent all data wirelessly to a monitoring center. Operating the dual modes of ZigBee/WLAN utilized an advantage of ZigBee by handling multiple users with minimum power consumption, and overcame the ZigBee limitation of low data rate. This dual-network system for LAN is economically competitive and reliable.

Neural network and wavelet average framing percentage energy for atrial fibrillation classification.

PubMed

Daqrouq, K; Alkhateeb, A; Ajour, M N; Morfeq, A

2014-03-01

ECG signals are an important source of information in the diagnosis of atrial conduction pathology. Nevertheless, diagnosis by visual inspection is a difficult task. This work introduces a novel wavelet feature extraction method for atrial fibrillation derived from the average framing percentage energy (AFE) of terminal wavelet packet transform (WPT) sub signals. Probabilistic neural network (PNN) is used for classification. The presented method is shown to be a potentially effective discriminator in an automated diagnostic process. The ECG signals taken from the MIT-BIH database are used to classify different arrhythmias together with normal ECG. Several published methods were investigated for comparison. The best recognition rate selection was obtained for AFE. The classification performance achieved accuracy 97.92%. It was also suggested to analyze the presented system in an additive white Gaussian noise (AWGN) environment; 55.14% for 0dB and 92.53% for 5dB. It was concluded that the proposed approach of automating classification is worth pursuing with larger samples to validate and extend the present study. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

QRS classification and spatial combination for robust heart rate detection in low-quality fetal ECG recordings.

PubMed

Warmerdam, G; Vullings, R; Van Pul, C; Andriessen, P; Oei, S G; Wijn, P

2013-01-01

Non-invasive fetal electrocardiography (ECG) can be used for prolonged monitoring of the fetal heart rate (FHR). However, the signal-to-noise-ratio (SNR) of non-invasive ECG recordings is often insufficient for reliable detection of the FHR. To overcome this problem, source separation techniques can be used to enhance the fetal ECG. This study uses a physiology-based source separation (PBSS) technique that has already been demonstrated to outperform widely used blind source separation techniques. Despite the relatively good performance of PBSS in enhancing the fetal ECG, PBSS is still susceptible to artifacts. In this study an augmented PBSS technique is developed to reduce the influence of artifacts. The performance of the developed method is compared to PBSS on multi-channel non-invasive fetal ECG recordings. Based on this comparison, the developed method is shown to outperform PBSS for the enhancement of the fetal ECG.

[A telemedicine electrocardiography system based on the component-architecture soft].

PubMed

Potapov, I V; Selishchev, S V

2004-01-01

The paper deals with a universal component-oriented architecture for creating the telemedicine applications. The worked-out system ensures the ECG reading, pressure measurements and pulsometry. The system design comprises a central database server and a client telemedicine module. Data can be transmitted via different interfaces--from an ordinary local network to digital satellite phones. The data protection is guaranteed by microchip charts that were used to realize the authentication 3DES algorithm.

Detection of heart rate and rhythm with a smartphone-based electrocardiograph versus a reference standard electrocardiograph in dogs and cats.

PubMed

Kraus, Marc S; Gelzer, Anna R; Rishniw, Mark

2016-07-15

OBJECTIVE To evaluate the diagnostic utility of ECGs acquired with a smartphone-based device, compared with reference 6-lead ECGs, for identification of heart rate and rhythm in dogs and cats. DESIGN Prospective study. ANIMALS 51 client-owned dogs and 27 client-owned cats. PROCEDURES Patients examined by a small animal referral cardiology service between April 2012 and January 2013 were enrolled consecutively. In each patient, a 30-second ECG was simultaneously acquired with a smartphone-based device (a bipolar, single-lead recorder coupled to a smartphone with an ECG application) and a standard 6-lead ECG machine. Recordings were evaluated by 3 board-certified cardiologists, and intra- and interobserver agreement were evaluated for both rhythm diagnosis and QRS polarity identification. RESULTS Values for instantaneous and mean heart rates for the smartphone-acquired and reference ECGs were within 1 beat of each other when mean heart rates were calculated. Intraobserver agreement for rhythm assessment was very high, with maximum disagreement for any observer for only 2 of 51 dogs and only 4 of 27 cats. There was minimal disagreement in the polarity of depolarization between the smartphone-acquired and reference ECGs in dogs but frequent disagreement in cats. Interobserver agreement for smartphone-acquired ECGs was similar to that for reference ECGs. with all 3 observers agreeing on the rhythm analysis and minimal disagreement on polarity. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested that ECGs acquired with the smartphone-based device accurately identified heart rate and rhythm in dogs and cats. Thus, the device may allow veterinarians to evaluate and manage cardiac arrhythmias relatively inexpensively at the cage side and could also allow clinicians to rapidly share information via email for further consultation, potentially enhancing patient care.

A robust approach for ECG-based analysis of cardiopulmonary coupling.

PubMed

Zheng, Jiewen; Wang, Weidong; Zhang, Zhengbo; Wu, Dalei; Wu, Hao; Peng, Chung-Kang

2016-07-01

Deriving respiratory signal from a surface electrocardiogram (ECG) measurement has advantage of simultaneously monitoring of cardiac and respiratory activities. ECG-based cardiopulmonary coupling (CPC) analysis estimated by heart period variability and ECG-derived respiration (EDR) shows promising applications in medical field. The aim of this paper is to provide a quantitative analysis of the ECG-based CPC, and further improve its performance. Two conventional strategies were tested to obtain EDR signal: R-S wave amplitude and area of the QRS complex. An adaptive filter was utilized to extract the common component of inter-beat interval (RRI) and EDR, generating enhanced versions of EDR signal. CPC is assessed through probing the nonlinear phase interactions between RRI series and respiratory signal. Respiratory oscillations presented in both RRI series and respiratory signals were extracted by ensemble empirical mode decomposition for coupling analysis via phase synchronization index. The results demonstrated that CPC estimated from conventional EDR series exhibits constant and proportional biases, while that estimated from enhanced EDR series is more reliable. Adaptive filtering can improve the accuracy of the ECG-based CPC estimation significantly and achieve robust CPC analysis. The improved ECG-based CPC estimation may provide additional prognostic information for both sleep medicine and autonomic function analysis. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Energy Analysis of Decoders for Rakeness-Based Compressed Sensing of ECG Signals.

PubMed

Pareschi, Fabio; Mangia, Mauro; Bortolotti, Daniele; Bartolini, Andrea; Benini, Luca; Rovatti, Riccardo; Setti, Gianluca

2017-12-01

In recent years, compressed sensing (CS) has proved to be effective in lowering the power consumption of sensing nodes in biomedical signal processing devices. This is due to the fact the CS is capable of reducing the amount of data to be transmitted to ensure correct reconstruction of the acquired waveforms. Rakeness-based CS has been introduced to further reduce the amount of transmitted data by exploiting the uneven distribution to the sensed signal energy. Yet, so far no thorough analysis exists on the impact of its adoption on CS decoder performance. The latter point is of great importance, since body-area sensor network architectures may include intermediate gateway nodes that receive and reconstruct signals to provide local services before relaying data to a remote server. In this paper, we fill this gap by showing that rakeness-based design also improves reconstruction performance. We quantify these findings in the case of ECG signals and when a variety of reconstruction algorithms are used either in a low-power microcontroller or a heterogeneous mobile computing platform.

Standardised pre-competitive screening of athletes in some European and African countries: the SMILE study.

PubMed

Assanelli, Deodato; Deodato, Assanelli; Ermolao, Andrea; Andrea, Ermolao; Carre, François; François, Carré; Deligiannis, Asterios; Asterios, Deligiannis; Mellwig, Klaus; Mellwig, Klaus; Klaus, Mellwig; Tahmi, Mohamed; Mohamed, Tahmi; Cesana, Bruno Mario; Mario, Cesana Bruno; Levaggi, Rosella; Rosella, Levaggi; Aliverti, Paola; Paola, Aliverti; Sharma, Sanjay; Sanjay, Sharma

2014-06-01

Most of the available data on the cardiovascular screening of athletes come from Italy, with fewer records being available outside of Italy and for non-Caucasian populations. The goals of the SMILE project (Sport Medicine Intervention to save Lives through ECG) are to evaluate the usefulness of 12-lead ECGs for the detection of cardiac diseases in athletes from three European countries and one African country and to estimate how many second-level examinations are needed subsequent to the initial screening in order to classify athletes with abnormal characteristics. A digital network consisting of Sport Centres and second and third opinion centres was set up in Greece, Germany, France and Algeria. Standard digital data input was carried out through the application of 12-lead ECGs, Bethesda questionnaires and physical examinations. Two hundred ninety-three of the 6,634 consecutive athletes required further evaluation, mostly (88.4 %) as a consequence of abnormal ECGs. After careful evaluation, 237 were determined to be healthy or apparently healthy, while 56 athletes were found to have cardiac disorders and were thus disqualified from active participation in sports. There was a large difference in the prevalence of diseases detected in Europe as compared with Algeria (0.23 and 4.01 %, respectively). Our data confirmed the noteworthy value of 12-lead resting ECGs as compared with other first-level evaluations, especially in athletes with asymptomatic cardiac diseases. Its value seems to have been even higher in Algeria than in the European countries. The establishment of a digital network of Sport Centres for second/third opinions in conjunction with the use of standard digital data input seems to be a valuable means for increasing the effectiveness of screening.

Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs.

PubMed

Daluwatte, C; Johannesen, L; Galeotti, L; Vicente, J; Strauss, D G; Scully, C G

2016-08-01

False and non-actionable alarms in critical care can be reduced by developing algorithms which assess the trueness of an arrhythmia alarm from a bedside monitor. Computational approaches that automatically identify artefacts in ECG signals are an important branch of physiological signal processing which tries to address this issue. Signal quality indices (SQIs) derived considering differences between artefacts which occur in ECG signals and normal QRS morphology have the potential to discriminate pathologically different arrhythmic ECG segments as artefacts. Using ECG signals from the PhysioNet/Computing in Cardiology Challenge 2015 training set, we studied previously reported ECG SQIs in the scientific literature to differentiate ECG segments with artefacts from arrhythmic ECG segments. We found that the ability of SQIs to discriminate between ECG artefacts and arrhythmic ECG varies based on arrhythmia type since the pathology of each arrhythmic ECG waveform is different. Therefore, to reduce the risk of SQIs classifying arrhythmic events as noise it is important to validate and test SQIs with databases that include arrhythmias. Arrhythmia specific SQIs may also minimize the risk of misclassifying arrhythmic events as noise.

Making Sense of the ECG - Cases for Self-Assessment Houghton Andrew R Gray David Making Sense of the ECG - Cases for Self-Assessment 290pp Hodder Education 9780340946893 034094689X [Formula: see text].

PubMed

2010-10-27

This practical, pocket-book approach to ECG interpretation accompanies the well-known text Making Sense of the ECG, by the same authors. It is also designed to be used alone to test knowledge of ECG interpretation and to make clinical decisions based on presented scenarios.

Making sense of the ECG: cases for self-assessment Making Sense of the ECG: Cases for Self-Assessment Houghton Andrew and Gray David Hodder Education £18.99 290pp 9780340946893 034094689X [Formula: see text].

PubMed

2011-02-10

This practical pocket-book approach to electrocardiogram (ECG) interpretation accompanies Making sense of the eCg by the same authors. it is also designed to be used alone to test knowledge of ECG interpretation and to make clinical decisions based on presented scenarios.

[Development of a portable ambulatory ECG monitor based on embedded microprocessor unit].

PubMed

Wang, Da-xiong; Wang, Guo-jun

2005-06-01

To develop a new kind of portable ambulatory ECG monitor. The hardware and software were designed based on RCA-CDP1802. New methods of ECG data compression and feature extraction of QRS complexes were applied to software design. A model for automatic arrhythmia analysis was established for real-time ambulatory ECG Data analysis. Compact, low power consumption and low cost were emphasized in the hardware design. This compact and light-weight monitor with low power consumption and high intelligence was capable of real-time monitoring arrhythmia for more than 48 h. More than ten types of arrhythmia could be detected, only the compressed abnormal ECG data was recorded and could be transmitted to the host if required. The monitor meets the design requirements and can be used for ambulatory ECG monitoring.

Internet based ECG medical information system.

PubMed

James, D A; Rowlands, D; Mahnovetski, R; Channells, J; Cutmore, T

2003-03-01

Physiological monitoring of humans for medical applications is well established and ready to be adapted to the Internet. This paper describes the implementation of a Medical Information System (MIS-ECG system) incorporating an Internet based ECG acquisition device. Traditionally clinical monitoring of ECG is largely a labour intensive process with data being typically stored on paper. Until recently, ECG monitoring applications have also been constrained somewhat by the size of the equipment required. Today's technology enables large and fixed hospital monitoring systems to be replaced by small portable devices. With an increasing emphasis on health management a truly integrated information system for the acquisition, analysis, patient particulars and archiving is now a realistic possibility. This paper describes recent Internet and technological advances and presents the design and testing of the MIS-ECG system that utilises those advances.

A 300-mV 220-nW event-driven ADC with real-time QRS detection for wearable ECG sensors.

PubMed

Zhang, Xiaoyang; Lian, Yong

2014-12-01

This paper presents an ultra-low-power event-driven analog-to-digital converter (ADC) with real-time QRS detection for wearable electrocardiogram (ECG) sensors in wireless body sensor network (WBSN) applications. Two QRS detection algorithms, pulse-triggered (PUT) and time-assisted PUT (t-PUT), are proposed based on the level-crossing events generated from the ADC. The PUT detector achieves 97.63% sensitivity and 97.33% positive prediction in simulation on the MIT-BIH Arrhythmia Database. The t-PUT improves the sensitivity and positive prediction to 97.76% and 98.59% respectively. Fabricated in 0.13 μm CMOS technology, the ADC with QRS detector consumes only 220 nW measured under 300 mV power supply, making it the first nanoWatt compact analog-to-information (A2I) converter with embedded QRS detector.

Ischemic ECG abnormalities are associated with an increased risk for death among subjects with COPD, also among those without known heart disease.

PubMed

Nilsson, Ulf; Blomberg, Anders; Johansson, Bengt; Backman, Helena; Eriksson, Berne; Lindberg, Anne

2017-01-01

An abstract, including parts of the results, has been presented at an oral session at the European Respiratory Society International Conference, London, UK, September 2016. Cardiovascular comorbidity contributes to increased mortality among subjects with COPD. However, the prognostic value of ECG abnormalities in COPD has rarely been studied in population-based surveys. To assess the impact of ischemic ECG abnormalities (I-ECG) on mortality among individuals with COPD, compared to subjects with normal lung function (NLF), in a population-based study. During 2002-2004, all subjects with FEV 1 /VC <0.70 (COPD, n=993) were identified from population-based cohorts, together with age- and sex-matched referents without COPD. Re-examination in 2005 included interview, spirometry, and 12-lead ECG in COPD (n=635) and referents [n=991, whereof 786 had NLF]. All ECGs were Minnesota-coded. Mortality data were collected until December 31, 2010. I-ECG was equally common in COPD and NLF. The 5-year cumulative mortality was higher among subjects with I-ECG in both groups (29.6% vs 10.6%, P <0.001 and 17.1% vs 6.6%, P <0.001). COPD, but not NLF, with I-ECG had increased risk for death assessed as the mortality risk ratio [95% confidence interval (CI)] when compared with NLF without I-ECG, 2.36 (1.45-3.85) and 1.65 (0.94-2.90) when adjusted for common confounders. When analyzed separately among the COPD cohort, the increased risk for death associated with I-ECG persisted after adjustment for FEV 1 % predicted, 1.89 (1.20-2.99). A majority of those with I-ECG had no previously reported heart disease (74.2% in NLF and 67.3% in COPD) and the pattern was similar among them. I-ECG was associated with an increased risk for death in COPD, independent of common confounders and disease severity. I-ECG was of prognostic value also among those without previously known heart disease.

Biosignals learning and synthesis using deep neural networks.

PubMed

Belo, David; Rodrigues, João; Vaz, João R; Pezarat-Correia, Pedro; Gamboa, Hugo

2017-09-25

Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field. The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

Tele-electrocardiography in the epidemiological 'Study of Health in Pomerania' (SHIP).

PubMed

Alte, Dietrich; Völzke, Henry; Robinson, Daniel M; Kleine, Volker; Grabe, Hans Jörgen; John, Ulrich; Felix, Stephan B

2006-01-01

We have evaluated a portable electrocardiogram (ECG) card in the large population-based epidemiological 'Study of Health in Pomerania' (SHIP). In all, 7008 men and women (20-79 years) were randomly selected from population registries and 4310 subjects participated. Participants used an ECG card for four weeks and recorded two ECGs daily. The participants were also encouraged to record additional ECGs in the case of symptomatic arrhythmias, chest pain or dizziness. The ECGs were sent via telephone. Acrobat (.pdf) files arrived at the study centre via email. Arrhythmias were analysed by visual ECG inspection. Seventy-one per cent of the participants sent at least 80% of the requested ECGs for four weeks. There were few problems (about 70) in the total of 38,162 ECGs transmitted. Overall, 94% of all ECGs were rated as 'good'. Physicians required about 1.5 h to read approximately 100 ECGs daily. The functionality and ergonomics of ECG cards appear to be sufficiently developed for large-scale use in epidemiological studies.

[A Smart Low-Power-Consumption ECG Monitor Based on MSP430F5529 and CC2540].

PubMed

Gong, Yuan; Cao, Jin; Luo, Zehui; Zhou, Guohui

2015-07-01

A design of ECG monitor was presented in this paper. It is based on the latest MCU and BLE4.0 technologies and can interact with multi-platform smart devices with extra low power consumption. Besides, a clinical expansion part can realize functions including displaying the real-time ECG and heart rate curve, reading abnormal ECG signals stored in the monitor, and setting alarm threshold. These functions are suitable for follow-up use.

[Experience in the use of equipment for ECG system analysis in municipal polyclinics].

PubMed

Bondarenko, A A

2006-01-01

Two electrocardiographs, an analog-digital electrocardiograph with preliminary analog filtering of signal and a smart cardiograph implemented as a PC-compatible device without preliminary analog filtering, are considered. Advantages and disadvantages of ECG systems based on artificial intelligence are discussed. ECG interpretation modes provided by the two electrocardiographs are considered. The reliability of automatic ECG interpretation is assessed. Problems of rational use of automated ECG processing systems are discussed.

New methodologies for measuring Brugada ECG patterns cannot differentiate the ECG pattern of Brugada syndrome from Brugada phenocopy.

PubMed

Gottschalk, Byron H; Garcia-Niebla, Javier; Anselm, Daniel D; Jaidka, Atul; De Luna, Antoni Bayés; Baranchuk, Adrian

2016-01-01

Brugada phenocopies (BrP) are clinical entities characterized by ECG patterns that are identical to true Brugada syndrome (BrS), but are elicited by various clinical circumstances. A recent study demonstrated that the patterns of BrP and BrS are indistinguishable under the naked eye, thereby validating the concept that the patterns are identical. The aim of our study was to determine whether recently developed ECG criteria would allow for discrimination between type-2 BrS ECG pattern and type-2 BrP ECG pattern. Ten ECGs from confirmed BrS (aborted sudden death, transformation into type 1 upon sodium channel blocking test and/or ventricular arrhythmias, positive genetics) cases and 9 ECGs from confirmed BrP were included in the study. Surface 12-lead ECGs were scanned, saved in JPEG format for blind measurement of two values: (i) β-angle; and (ii) the base of the triangle. Cut-off values of ≥58° for the β-angle and ≥4mm for the base of the triangle were used to determine the BrS ECG pattern. Mean values for the β-angle in leads V1 and V2 were 66.7±25.5 and 55.4±28.1 for BrS and 54.1±26.5 and 43.1±16.1 for BrP respectively (p=NS). Mean values for the base of the triangle in V1 and V2 were 7.5±3.9 and 5.7±3.9 for BrS and 5.6±3.2 and 4.7±2.7 for BrP respectively (p=NS). The β-angle had a sensitivity of 60%, specificity of 78% (LR+ 2.7, LR- 0.5). The base of the triangle had a sensitivity of 80%, specificity of 40% (LR+ 1.4, LR- 0.5). New ECG criteria presented relatively low sensitivity and specificity, positive and negative predictive values to discriminate between BrS and BrP ECG patterns, providing further evidence that the two patterns are identical. Copyright © 2016 Elsevier Inc. All rights reserved.

Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices.

PubMed

Gradl, Stefan; Kugler, Patrick; Lohmuller, Clemens; Eskofier, Bjoern

2012-01-01

We developed an application for Android™-based mobile devices that allows real-time electrocardiogram (ECG) monitoring and automated arrhythmia detection by analyzing ECG parameters. ECG data provided by pre-recorded files or acquired live by accessing a Shimmer™ sensor node via Bluetooth™ can be processed and evaluated. The application is based on the Pan-Tompkins algorithm for QRS-detection and contains further algorithm blocks to detect abnormal heartbeats. The algorithm was validated using the MIT-BIH Arrhythmia and MIT-BIH Supraventricular Arrhythmia databases. More than 99% of all QRS complexes were detected correctly by the algorithm. Overall sensitivity for abnormal beat detection was 89.5% with a specificity of 80.6%. The application is available for download and may be used for real-time ECG-monitoring on mobile devices.

Temporal abstraction and inductive logic programming for arrhythmia recognition from electrocardiograms.

PubMed

Carrault, G; Cordier, M-O; Quiniou, R; Wang, F

2003-07-01

This paper proposes a novel approach to cardiac arrhythmia recognition from electrocardiograms (ECGs). ECGs record the electrical activity of the heart and are used to diagnose many heart disorders. The numerical ECG is first temporally abstracted into series of time-stamped events. Temporal abstraction makes use of artificial neural networks to extract interesting waves and their features from the input signals. A temporal reasoner called a chronicle recogniser processes such series in order to discover temporal patterns called chronicles which can be related to cardiac arrhythmias. Generally, it is difficult to elicit an accurate set of chronicles from a doctor. Thus, we propose to learn automatically from symbolic ECG examples the chronicles discriminating the arrhythmias belonging to some specific subset. Since temporal relationships are of major importance, inductive logic programming (ILP) is the tool of choice as it enables first-order relational learning. The approach has been evaluated on real ECGs taken from the MIT-BIH database. The performance of the different modules as well as the efficiency of the whole system is presented. The results are rather good and demonstrate that integrating numerical techniques for low level perception and symbolic techniques for high level classification is very valuable.

A Combined Independent Source Separation and Quality Index Optimization Method for Fetal ECG Extraction from Abdominal Maternal Leads

PubMed Central

Billeci, Lucia; Varanini, Maurizio

2017-01-01

The non-invasive fetal electrocardiogram (fECG) technique has recently received considerable interest in monitoring fetal health. The aim of our paper is to propose a novel fECG algorithm based on the combination of the criteria of independent source separation and of a quality index optimization (ICAQIO-based). The algorithm was compared with two methods applying the two different criteria independently—the ICA-based and the QIO-based methods—which were previously developed by our group. All three methods were tested on the recently implemented Fetal ECG Synthetic Database (FECGSYNDB). Moreover, the performance of the algorithm was tested on real data from the PhysioNet fetal ECG Challenge 2013 Database. The proposed combined method outperformed the other two algorithms on the FECGSYNDB (ICAQIO-based: 98.78%, QIO-based: 97.77%, ICA-based: 97.61%). Significant differences were obtained in particular in the conditions when uterine contractions and maternal and fetal ectopic beats occurred. On the real data, all three methods obtained very high performances, with the QIO-based method proving slightly better than the other two (ICAQIO-based: 99.38%, QIO-based: 99.76%, ICA-based: 99.37%). The findings from this study suggest that the proposed method could potentially be applied as a novel algorithm for accurate extraction of fECG, especially in critical recording conditions. PMID:28509860

Extraction of fetal ECG signal by an improved method using extended Kalman smoother framework from single channel abdominal ECG signal.

PubMed

Panigrahy, D; Sahu, P K

2017-03-01

This paper proposes a five-stage based methodology to extract the fetal electrocardiogram (FECG) from the single channel abdominal ECG using differential evolution (DE) algorithm, extended Kalman smoother (EKS) and adaptive neuro fuzzy inference system (ANFIS) framework. The heart rate of the fetus can easily be detected after estimation of the fetal ECG signal. The abdominal ECG signal contains fetal ECG signal, maternal ECG component, and noise. To estimate the fetal ECG signal from the abdominal ECG signal, removal of the noise and the maternal ECG component presented in it is necessary. The pre-processing stage is used to remove the noise from the abdominal ECG signal. The EKS framework is used to estimate the maternal ECG signal from the abdominal ECG signal. The optimized parameters of the maternal ECG components are required to develop the state and measurement equation of the EKS framework. These optimized maternal ECG parameters are selected by the differential evolution algorithm. The relationship between the maternal ECG signal and the available maternal ECG component in the abdominal ECG signal is nonlinear. To estimate the actual maternal ECG component present in the abdominal ECG signal and also to recognize this nonlinear relationship the ANFIS is used. Inputs to the ANFIS framework are the output of EKS and the pre-processed abdominal ECG signal. The fetal ECG signal is computed by subtracting the output of ANFIS from the pre-processed abdominal ECG signal. Non-invasive fetal ECG database and set A of 2013 physionet/computing in cardiology challenge database (PCDB) are used for validation of the proposed methodology. The proposed methodology shows a sensitivity of 94.21%, accuracy of 90.66%, and positive predictive value of 96.05% from the non-invasive fetal ECG database. The proposed methodology also shows a sensitivity of 91.47%, accuracy of 84.89%, and positive predictive value of 92.18% from the set A of PCDB.

Left Ventricular Hypertrophy: An allometric comparative analysis of different ECG markers

NASA Astrophysics Data System (ADS)

Bonomini, M. P.; Ingallina, F.; Barone, V.; Valentinuzzi, M. E.; Arini, P. D.

2011-12-01

Allometry, in general biology, measures the relative growth of a part in relation to the whole living organism. Left ventricular hypertrophy (LVH) is the heart adaptation to excessive load (systolic or diastolic). The increase in left ventricular mass leads to an increase in the electrocardiographic voltages. Based on clinical data, we compared the allometric behavior of three different ECG markers of LVH. To do this, the allometric fit AECG = δ + β (VM) relating left ventricular mass (estimated from ecocardiographic data) and ECG amplitudes (expressed as the Cornell-Voltage, Sokolow and the ECG overall voltage indexes) were compared. Besides, sensitivity and specifity for each index were analyzed. The more sensitive the ECG criteria, the better the allometric fit. In conclusion: The allometric paradigm should be regarded as the way to design new and more sensitive ECG-based LVH markers.

Performance of human body communication-based wearable ECG with capacitive coupling electrodes

PubMed Central

Sakuma, Jun; Anzai, Daisuke

2016-01-01

Wearable electrocardiogram (ECG) is attracting much attention in daily healthcare applications, and human body communication (HBC) technology provides an evident advantage in making the sensing electrodes of ECG also working for transmission through the human body. In view of actual usage in daily life, however, non-contact electrodes to the human body are desirable. In this Letter, the authors discussed the ECG circuit structure in the HBC-based wearable ECG for removing the common mode noise when employing non-contact capacitive coupling electrodes. Through the comparison of experimental results, they have shown that the authors’ proposed circuit structure with the third electrode directly connected to signal ground can provide an effect on common mode noise reduction similar to the usual drive-right-leg circuit, and a sufficiently good acquisition performance of ECG signals. PMID:27733931

Performance of human body communication-based wearable ECG with capacitive coupling electrodes.

PubMed

Sakuma, Jun; Anzai, Daisuke; Wang, Jianqing

2016-09-01

Wearable electrocardiogram (ECG) is attracting much attention in daily healthcare applications, and human body communication (HBC) technology provides an evident advantage in making the sensing electrodes of ECG also working for transmission through the human body. In view of actual usage in daily life, however, non-contact electrodes to the human body are desirable. In this Letter, the authors discussed the ECG circuit structure in the HBC-based wearable ECG for removing the common mode noise when employing non-contact capacitive coupling electrodes. Through the comparison of experimental results, they have shown that the authors' proposed circuit structure with the third electrode directly connected to signal ground can provide an effect on common mode noise reduction similar to the usual drive-right-leg circuit, and a sufficiently good acquisition performance of ECG signals.

Unveiling the Biometric Potential of Finger-Based ECG Signals

PubMed Central

Lourenço, André; Silva, Hugo; Fred, Ana

2011-01-01

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications. PMID:21837235

Unveiling the biometric potential of finger-based ECG signals.

PubMed

Lourenço, André; Silva, Hugo; Fred, Ana

2011-01-01

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.

Convolutional Neural Network-Based Classification of Driver's Emotion during Aggressive and Smooth Driving Using Multi-Modal Camera Sensors.

PubMed

Lee, Kwan Woo; Yoon, Hyo Sik; Song, Jong Min; Park, Kang Ryoung

2018-03-23

Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver's body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver's emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver's face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods.

A novel LabVIEW-based multi-channel non-invasive abdominal maternal-fetal electrocardiogram signal generator.

PubMed

Martinek, Radek; Kelnar, Michal; Koudelka, Petr; Vanus, Jan; Bilik, Petr; Janku, Petr; Nazeran, Homer; Zidek, Jan

2016-02-01

This paper describes the design, construction, and testing of a multi-channel fetal electrocardiogram (fECG) signal generator based on LabVIEW. Special attention is paid to the fetal heart development in relation to the fetus' anatomy, physiology, and pathology. The non-invasive signal generator enables many parameters to be set, including fetal heart rate (FHR), maternal heart rate (MHR), gestational age (GA), fECG interferences (biological and technical artifacts), as well as other fECG signal characteristics. Furthermore, based on the change in the FHR and in the T wave-to-QRS complex ratio (T/QRS), the generator enables manifestations of hypoxic states (hypoxemia, hypoxia, and asphyxia) to be monitored while complying with clinical recommendations for classifications in cardiotocography (CTG) and fECG ST segment analysis (STAN). The generator can also produce synthetic signals with defined properties for 6 input leads (4 abdominal and 2 thoracic). Such signals are well suited to the testing of new and existing methods of fECG processing and are effective in suppressing maternal ECG while non-invasively monitoring abdominal fECG. They may also contribute to the development of a new diagnostic method, which may be referred to as non-invasive trans-abdominal CTG +  STAN. The functional prototype is based on virtual instrumentation using the LabVIEW developmental environment and its associated data acquisition measurement cards (DAQmx). The generator also makes it possible to create synthetic signals and measure actual fetal and maternal ECGs by means of bioelectrodes.

ECG Wave-Maven: An Internet-based Electrocardiography Self-Assessment Program for Students and Clinicians.

PubMed

McClennen, Seth; Nathanson, Larry A; Safran, Charles; Goldberger, Ary L

2003-12-01

To create a multimedia internet-based ECG teaching tool, with the ability to rapidly incorporate new clinical cases. We created ECG Wave-Maven ( http://ecg.bidmc.harvard.edu ), a novel teaching tool with a direct link to an institution-wide clinical repository. We analyzed usage data from the web between December, 2000 and May 2002. In 17 months, there have been 4105 distinct uses of the program. A majority of users are physicians or medical students (2605, 63%), and almost half report use as an educational tool. The internet offers an opportunity to provide easily-expandable, open access resources for ECG pedagogy which may be used to complement traditional methods of instruction.

Experimental evaluations of wearable ECG monitor.

PubMed

Ha, Kiryong; Kim, Youngsung; Jung, Junyoung; Lee, Jeunwoo

2008-01-01

Healthcare industry is changing with ubiquitous computing environment and wearable ECG measurement is one of the most popular approaches in this healthcare industry. Reliability and performance of healthcare device is fundamental issue for widespread adoptions, and interdisciplinary perspectives of wearable ECG monitor make this more difficult. In this paper, we propose evaluation criteria considering characteristic of both ECG measurement and ubiquitous computing. With our wearable ECG monitors, various levels of experimental analysis are performed based on evaluation strategy.

ECG-based gating in ultra high field cardiovascular magnetic resonance using an independent component analysis approach.

PubMed

Krug, Johannes W; Rose, Georg; Clifford, Gari D; Oster, Julien

2013-11-19

In Cardiovascular Magnetic Resonance (CMR), the synchronization of image acquisition with heart motion is performed in clinical practice by processing the electrocardiogram (ECG). The ECG-based synchronization is well established for MR scanners with magnetic fields up to 3 T. However, this technique is prone to errors in ultra high field environments, e.g. in 7 T MR scanners as used in research applications. The high magnetic fields cause severe magnetohydrodynamic (MHD) effects which disturb the ECG signal. Image synchronization is thus less reliable and yields artefacts in CMR images. A strategy based on Independent Component Analysis (ICA) was pursued in this work to enhance the ECG contribution and attenuate the MHD effect. ICA was applied to 12-lead ECG signals recorded inside a 7 T MR scanner. An automatic source identification procedure was proposed to identify an independent component (IC) dominated by the ECG signal. The identified IC was then used for detecting the R-peaks. The presented ICA-based method was compared to other R-peak detection methods using 1) the raw ECG signal, 2) the raw vectorcardiogram (VCG), 3) the state-of-the-art gating technique based on the VCG, 4) an updated version of the VCG-based approach and 5) the ICA of the VCG. ECG signals from eight volunteers were recorded inside the MR scanner. Recordings with an overall length of 87 min accounting for 5457 QRS complexes were available for the analysis. The records were divided into a training and a test dataset. In terms of R-peak detection within the test dataset, the proposed ICA-based algorithm achieved a detection performance with an average sensitivity (Se) of 99.2%, a positive predictive value (+P) of 99.1%, with an average trigger delay and jitter of 5.8 ms and 5.0 ms, respectively. Long term stability of the demixing matrix was shown based on two measurements of the same subject, each being separated by one year, whereas an averaged detection performance of Se = 99.4% and +P = 99.7% was achieved.Compared to the state-of-the-art VCG-based gating technique at 7 T, the proposed method increased the sensitivity and positive predictive value within the test dataset by 27.1% and 42.7%, respectively. The presented ICA-based method allows the estimation and identification of an IC dominated by the ECG signal. R-peak detection based on this IC outperforms the state-of-the-art VCG-based technique in a 7 T MR scanner environment.

A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

PubMed Central

Zhu, Bohui; Ding, Yongsheng; Hao, Kuangrong

2013-01-01

This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias. PMID:23690875

A fabric wrist patch sensor for continuous and comprehensive monitoring of the cardiovascular system.

PubMed

Kwonjoon Lee; Kiseok Song; Taehwan Roh; Hoi-Jun Yoo

2016-08-01

The wrist patch-type ECG/APW sensor system is proposed for continuous and comprehensive monitoring of the patient's cardiovascular system. The wrist patch-type ECG/APW sensor system is consists of ECG/APW sensor, ECG/APW electrodes, and base station for real-time monitoring of the patient's status. The ECG/APW sensor and electrodes are composed of wrist patch, bandage-type ECG electrode and fabric APW electrode, respectively so that the patient's cardiovascular system can be continuously monitored in daily life with free hand-movement. Since the proposed wrist patchtype ECG/APW sensor simultaneously measures ECG/APW, the cardiac indicators, such as HR and PAT, can be extracted for comprehensive and accurate monitoring of the patient's cardiovascular system. The proposed wrist patch-type ECG/APW sensor system is successfully verified using the commercial PPG sensor (RP520) and demonstrated with the customized Android application on the smart phone.

Biometric sample extraction using Mahalanobis distance in Cardioid based graph using electrocardiogram signals.

PubMed

Sidek, Khairul; Khali, Ibrahim

2012-01-01

In this paper, a person identification mechanism implemented with Cardioid based graph using electrocardiogram (ECG) is presented. Cardioid based graph has given a reasonably good classification accuracy in terms of differentiating between individuals. However, the current feature extraction method using Euclidean distance could be further improved by using Mahalanobis distance measurement producing extracted coefficients which takes into account the correlations of the data set. Identification is then done by applying these extracted features to Radial Basis Function Network. A total of 30 ECG data from MITBIH Normal Sinus Rhythm database (NSRDB) and MITBIH Arrhythmia database (MITDB) were used for development and evaluation purposes. Our experimentation results suggest that the proposed feature extraction method has significantly increased the classification performance of subjects in both databases with accuracy from 97.50% to 99.80% in NSRDB and 96.50% to 99.40% in MITDB. High sensitivity, specificity and positive predictive value of 99.17%, 99.91% and 99.23% for NSRDB and 99.30%, 99.90% and 99.40% for MITDB also validates the proposed method. This result also indicates that the right feature extraction technique plays a vital role in determining the persistency of the classification accuracy for Cardioid based person identification mechanism.

CardioGuard: A Brassiere-Based Reliable ECG Monitoring Sensor System for Supporting Daily Smartphone Healthcare Applications

PubMed Central

Kwon, Sungjun; Kim, Jeehoon; Kang, Seungwoo; Lee, Youngki; Baek, Hyunjae

2014-01-01

Abstract We propose CardioGuard, a brassiere-based reliable electrocardiogram (ECG) monitoring sensor system, for supporting daily smartphone healthcare applications. It is designed to satisfy two key requirements for user-unobtrusive daily ECG monitoring: reliability of ECG sensing and usability of the sensor. The system is validated through extensive evaluations. The evaluation results showed that the CardioGuard sensor reliably measure the ECG during 12 representative daily activities including diverse movement levels; 89.53% of QRS peaks were detected on average. The questionnaire-based user study with 15 participants showed that the CardioGuard sensor was comfortable and unobtrusive. Additionally, the signal-to-noise ratio test and the washing durability test were conducted to show the high-quality sensing of the proposed sensor and its physical durability in practical use, respectively. PMID:25405527

Relationship between echocardiographic LV mass and ECG based left ventricular voltages in an adolescent population: related or random?

PubMed

Czosek, Richard J; Cnota, James F; Knilans, Timothy K; Pratt, Jesse; Guerrier, Karine; Anderson, Jeffrey B

2014-09-01

In attempts to detect diseases that may place adolescents at risk for sudden death, some have advocated for population-based screening. Controversy exists over electrocardiography (ECG) screening due to the lack of specificity, cost, and detrimental effects of false positive or extraneous outcomes. Analyze the relationship between precordial lead voltage on ECG and left ventricle (LV) mass by echocardiogram in adolescent athletes. Retrospective cohort analysis of a prospectively obtained population of self-identified adolescent athletes during sports screening with ECG and echocardiogram. Correlation between ECG LV voltages (R wave in V6 [RV6] and S wave in lead V1 [SV1]) was compared to echocardiogram-based measurements of left ventricular mass. Potential effects on ECG voltages by body anthropometrics, including weight, body mass index (BMI), and body surface area were analyzed, and ECG voltages indexed to BMI were compared to LV mass indices to analyze for improved correlation. A total of 659 adolescents enrolled in this study (64% male). The mean age was 15.4 years (14-18). The correlations between LV mass and RV6, SV1, and RV6 + SV1 were all less than 0.20. The false positive rate for abnormal voltages was relatively high (5.5%) but improved if abnormal voltages in both RV6 and SV1 were mandated simultaneously (0%). Indexing ECG voltages to BMI significantly improved correlation to LV mass, though false positive findings were increased (12.9%). There is poor correlation between ECG precordial voltages and echocardiographic LV mass. This relationship is modified by BMI. This finding may contribute to the poor ECG screening characteristics. ©2014 Wiley Periodicals, Inc.

Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals.

PubMed

Elhaj, Fatin A; Salim, Naomie; Harris, Arief R; Swee, Tan Tian; Ahmed, Taqwa

2016-04-01

Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart abnormalities. Due to the presence of noise, the non-stationary nature of the ECG signal (i.e. the changing morphology of the ECG signal with respect to time) and the irregularity of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. The computer-aided analysis of ECG results assists physicians to detect cardiovascular diseases. The development of many existing arrhythmia systems has depended on the findings from linear experiments on ECG data which achieve high performance on noise-free data. However, nonlinear experiments characterize the ECG signal more effectively sense, extract hidden information in the ECG signal, and achieve good performance under noisy conditions. This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data. In this study, five types of beat classes of arrhythmia as recommended by the Association for Advancement of Medical Instrumentation are analyzed: non-ectopic beats (N), supra-ventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F) and unclassifiable and paced beats (U). The characterization ability of nonlinear features such as high order statistics and cumulants and nonlinear feature reduction methods such as independent component analysis are combined with linear features, namely, the principal component analysis of discrete wavelet transform coefficients. The features are tested for their ability to differentiate different classes of data using different classifiers, namely, the support vector machine and neural network methods with tenfold cross-validation. Our proposed method is able to classify the N, S, V, F and U arrhythmia classes with high accuracy (98.91%) using a combined support vector machine and radial basis function method. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Evaluation of a web-based ECG-interpretation programme for undergraduate medical students.

PubMed

Nilsson, Mikael; Bolinder, Gunilla; Held, Claes; Johansson, Bo-Lennart; Fors, Uno; Ostergren, Jan

2008-04-23

Most clinicians and teachers agree that knowledge about ECG is of importance in the medical curriculum. Students at Karolinska Institute have asked for more training in ECG-interpretation during their undergraduate studies. Clinical tutors, however, have difficulties in meeting these demands due to shortage of time. Thus, alternative ways to learn and practice ECG-interpretation are needed. Education offered via the Internet is readily available, geographically independent and flexible. Furthermore, the quality of education may increase and become more effective through a superior educational approach, improved visualization and interactivity. A Web-based comprehensive ECG-interpretation programme has been evaluated. Medical students from the sixth semester were given an optional opportunity to access the programme from the start of their course. Usage logs and an initial evaluation survey were obtained from each student. A diagnostic test was performed in order to assess the effect on skills in ECG interpretation. Students from the corresponding course, at another teaching hospital and without access to the ECG-programme but with conventional teaching of ECG served as a control group. 20 of the 32 students in the intervention group had tested the programme after 2 months. On a five-graded scale (1- bad to 5 - very good) they ranked the utility of a web-based programme for this purpose as 4.1 and the quality of the programme software as 3.9. At the diagnostic test (maximal points 16) by the end of the 5-month course at the 6th semester the mean result for the students in the intervention group was 9.7 compared with 8.1 for the control group (p = 0.03). Students ranked the Web-based ECG-interpretation programme as a useful instrument to learn ECG. Furthermore, Internet-delivered education may be more effective than traditional teaching methods due to greater immediacy, improved visualisation and interactivity.

Some regularity on how to locate electrodes for higher fECG SNRs

NASA Astrophysics Data System (ADS)

Zhang, Jie-Min; Huang, Xiao-Lin; Guan, Qun; Liu, Tie-Bing; Li, Ping; Zhao, Ying; Liu, Hong-Xing

2015-03-01

The electrocardiogram (ECG) recorded from the abdominal surface of a pregnant woman is a composite of maternal ECG, fetal ECG (fECG) and other noises, while only the fECG component is always needed by us. With different locations of electrode pairs on the maternal abdominal surface to measure fECGs, the signal-to-noise ratios (SNRs) of the recorded abdominal ECGs are also correspondingly different. Some regularity on how to locate electrodes to obtain higher fECG SNRs is needed practically. In this paper, 343 groups of abdominal ECG records were acquired from 78 pregnant women with different electrode pairs locating, and an appropriate extended research database is formed. Then the regularity on fECG SNRs corresponding to different electrode pairs locating was studied. Based on statistical analysis, it is shown that the fECG SNRs are significantly higher in certain locations than others. Reasonable explanation is also provided to the statistical result using the theories of the fetal cardiac electrical axis and the signal phase delay. Project supported by the National Natural Science Foundation of China (Grant No. 61271079) and the Supporting Plan Project of Jiangsu Province, China (Grant No. BE2010720).

Flexible Graphene Electrodes for Prolonged Dynamic ECG Monitoring

PubMed Central

Lou, Cunguang; Li, Ruikai; Li, Zhaopeng; Liang, Tie; Wei, Zihui; Run, Mingtao; Yan, Xiaobing; Liu, Xiuling

2016-01-01

This paper describes the development of a graphene-based dry flexible electrocardiography (ECG) electrode and a portable wireless ECG measurement system. First, graphene films on polyethylene terephthalate (PET) substrates and graphene paper were used to construct the ECG electrode. Then, a graphene textile was synthesized for the fabrication of a wearable ECG monitoring system. The structure and the electrical properties of the graphene electrodes were evaluated using Raman spectroscopy, scanning electron microscopy (SEM), and alternating current impedance spectroscopy. ECG signals were then collected from healthy subjects using the developed graphene electrode and portable measurement system. The results show that the graphene electrode was able to acquire the typical characteristics and features of human ECG signals with a high signal-to-noise (SNR) ratio in different states of motion. A week-long continuous wearability test showed no degradation in the ECG signal quality over time. The graphene-based flexible electrode demonstrates comfortability, good biocompatibility, and high electrophysiological detection sensitivity. The graphene electrode also combines the potential for use in long-term wearable dynamic cardiac activity monitoring systems with convenience and comfort for use in home health care of elderly and high-risk adults. PMID:27809270

Electrocardiogram signal denoising based on a new improved wavelet thresholding

NASA Astrophysics Data System (ADS)

Han, Guoqiang; Xu, Zhijun

2016-08-01

Good quality electrocardiogram (ECG) is utilized by physicians for the interpretation and identification of physiological and pathological phenomena. In general, ECG signals may mix various noises such as baseline wander, power line interference, and electromagnetic interference in gathering and recording process. As ECG signals are non-stationary physiological signals, wavelet transform is investigated to be an effective tool to discard noises from corrupted signals. A new compromising threshold function called sigmoid function-based thresholding scheme is adopted in processing ECG signals. Compared with other methods such as hard/soft thresholding or other existing thresholding functions, the new algorithm has many advantages in the noise reduction of ECG signals. It perfectly overcomes the discontinuity at ±T of hard thresholding and reduces the fixed deviation of soft thresholding. The improved wavelet thresholding denoising can be proved to be more efficient than existing algorithms in ECG signal denoising. The signal to noise ratio, mean square error, and percent root mean square difference are calculated to verify the denoising performance as quantitative tools. The experimental results reveal that the waves including P, Q, R, and S waves of ECG signals after denoising coincide with the original ECG signals by employing the new proposed method.

Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.

PubMed

Urtnasan, Erdenebayar; Park, Jong-Uk; Joo, Eun-Yeon; Lee, Kyoung-Joung

2018-04-23

In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F 1 -score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.

International recommendations for electrocardiographic interpretation in athletes.

PubMed

Sharma, Sanjay; Drezner, Jonathan A; Baggish, Aaron; Papadakis, Michael; Wilson, Mathew G; Prutkin, Jordan M; La Gerche, Andre; Ackerman, Michael J; Borjesson, Mats; Salerno, Jack C; Asif, Irfan M; Owens, David S; Chung, Eugene H; Emery, Michael S; Froelicher, Victor F; Heidbuchel, Hein; Adamuz, Carmen; Asplund, Chad A; Cohen, Gordon; Harmon, Kimberly G; Marek, Joseph C; Molossi, Silvana; Niebauer, Josef; Pelto, Hank F; Perez, Marco V; Riding, Nathan R; Saarel, Tess; Schmied, Christian M; Shipon, David M; Stein, Ricardo; Vetter, Victoria L; Pelliccia, Antonio; Corrado, Domenico

2018-04-21

Sudden cardiac death (SCD) is the leading cause of mortality in athletes during sport. A variety of mostly hereditary, structural, or electrical cardiac disorders are associated with SCD in young athletes, the majority of which can be identified or suggested by abnormalities on a resting 12-lead electrocardiogram (ECG). Whether used for diagnostic or screening purposes, physicians responsible for the cardiovascular care of athletes should be knowledgeable and competent in ECG interpretation in athletes. However, in most countries a shortage of physician expertise limits wider application of the ECG in the care of the athlete. A critical need exists for physician education in modern ECG interpretation that distinguishes normal physiological adaptations in athletes from distinctly abnormal findings suggestive of underlying pathology. Since the original 2010 European Society of Cardiology recommendations for ECG interpretation in athletes, ECG standards have evolved quickly over the last decade; pushed by a growing body of scientific data that both tests proposed criteria sets and establishes new evidence to guide refinements. On 26-27 February 2015, an international group of experts in sports cardiology, inherited cardiac disease, and sports medicine convened in Seattle, Washington, to update contemporary standards for ECG interpretation in athletes. The objective of the meeting was to define and revise ECG interpretation standards based on new and emerging research and to develop a clear guide to the proper evaluation of ECG abnormalities in athletes. This statement represents an international consensus for ECG interpretation in athletes and provides expert opinion-based recommendations linking specific ECG abnormalities and the secondary evaluation for conditions associated with SCD.

Near Field Communication-based telemonitoring with integrated ECG recordings.

PubMed

Morak, J; Kumpusch, H; Hayn, D; Leitner, M; Scherr, D; Fruhwald, F M; Schreier, G

2011-01-01

Telemonitoring of vital signs is an established option in treatment of patients with chronic heart failure (CHF). In order to allow for early detection of atrial fibrillation (AF) which is highly prevalent in the CHF population telemonitoring programs should include electrocardiogram (ECG) signals. It was therefore the aim to extend our current home monitoring system based on mobile phones and Near Field Communication technology (NFC) to enable patients acquiring their ECG signals autonomously in an easy-to-use way. We prototypically developed a sensing device for the concurrent acquisition of blood pressure and ECG signals. The design of the device equipped with NFC technology and Bluetooth allowed for intuitive interaction with a mobile phone based patient terminal. This ECG monitoring system was evaluated in the course of a clinical pilot trial to assess the system's technical feasibility, usability and patient's adherence to twice daily usage. 21 patients (4f, 54 ± 14 years) suffering from CHF were included in the study and were asked to transmit two ECG recordings per day via the telemonitoring system autonomously over a monitoring period of seven days. One patient dropped out from the study. 211 data sets were transmitted over a cumulative monitoring period of 140 days (overall adherence rate 82.2%). 55% and 8% of the transmitted ECG signals were sufficient for ventricular and atrial rhythm assessment, respectively. Although ECG signal quality has to be improved for better AF detection the developed communication design of joining Bluetooth and NFC technology in our telemonitoring system allows for ambulatory ECG acquisition with high adherence rates and system usability in heart failure patients.

Use of electromyographic and electrocardiographic signals to detect sleep bruxism episodes in a natural environment.

PubMed

Castroflorio, Tommaso; Mesin, Luca; Tartaglia, Gianluca Martino; Sforza, Chiarella; Farina, Dario

2013-11-01

Diagnosis of bruxism is difficult since not all contractions of masticatory muscles during sleeping are bruxism episodes. In this paper, we propose the use of both EMG and ECG signals for the detection of sleep bruxism. Data have been acquired from 21 healthy volunteers and 21 sleep bruxers. The masseter surface EMGs were detected with bipolar concentric electrodes and the ECG with monopolar electrodes located on the clavicular regions. Recordings were made at the subjects' homes during sleeping. Bruxism episodes were automatically detected as characterized by masseter EMG amplitude greater than 10% of the maximum and heart rate increasing by more than 25% with respect to baseline within 1 s before the increase in EMG amplitude above the 10% threshold. Furthermore, the subjects were classified as bruxers and nonbruxers by a neural network. The number of bruxism episodes per night was 24.6 ± 8.4 for bruxers and 4.3 ± 4.5 for controls ( P < 0.0001). The classification error between bruxers and nonbruxers was 1% which was substantially lower than when using EMG only for the classification. These results show that the proposed system, based on the joint analysis of EMG and ECG, can provide support for the clinical diagnosis of bruxism.

Detection of segments with fetal QRS complex from abdominal maternal ECG recordings using support vector machine

NASA Astrophysics Data System (ADS)

Delgado, Juan A.; Altuve, Miguel; Nabhan Homsi, Masun

2015-12-01

This paper introduces a robust method based on the Support Vector Machine (SVM) algorithm to detect the presence of Fetal QRS (fQRS) complexes in electrocardiogram (ECG) recordings provided by the PhysioNet/CinC challenge 2013. ECG signals are first segmented into contiguous frames of 250 ms duration and then labeled in six classes. Fetal segments are tagged according to the position of fQRS complex within each one. Next, segment features extraction and dimensionality reduction are obtained by applying principal component analysis on Haar-wavelet transform. After that, two sub-datasets are generated to separate representative segments from atypical ones. Imbalanced class problem is dealt by applying sampling without replacement on each sub-dataset. Finally, two SVMs are trained and cross-validated using the two balanced sub-datasets separately. Experimental results show that the proposed approach achieves high performance rates in fetal heartbeats detection that reach up to 90.95% of accuracy, 92.16% of sensitivity, 88.51% of specificity, 94.13% of positive predictive value and 84.96% of negative predictive value. A comparative study is also carried out to show the performance of other two machine learning algorithms for fQRS complex estimation, which are K-nearest neighborhood and Bayesian network.

[Current status of the development of wireless sensors for medical applications].

PubMed

Moor, C; Braecklein, M; Jörns, N

2005-01-01

Wireless near-field transmission has been a challenge for scientists developing medical sensors for a long time. Here, instruments which measure a patient's ECG, oxygen saturation, blood pressure, peak flow, weight, blood glucose etc. are to be equipped with suitable transmission technology. Application scenarios for these sensors can be found in all medical areas where cable connections are irritating for the doctor, patient and other care personnel. This problem is especially common in sport medicine, sleep medicine, emergency medicine and intensive care. Based on its beneficial properties with regard to power consumption, range, data security and network capability, the worldwide standard radio technology Bluetooth was selected to transmit measurements. Since digital data is sent to a receiving station via Bluetooth, the measurement pre-processing now takes place in the patient sensor itself, instead of being processed by the monitor. In this article, a Bluetooth ECG, Bluetooth pulse oximeter, Bluetooth peak flow meter and Bluetooth event recorder will be introduced. On the one hand, systems can be realized with these devices, which allow patients to be monitored online (ECG, pulse oximeter). These devices can also be integrated in disease management programs (peak flow meter) and can be used to monitor high-risk patients in their home environment (event recorder).

[Cardiological emergency network in Lombardy].

PubMed

Marzegalli, Maurizio; Fontana, Giancarlo; Sesana, Giovanni; Grieco, Niccolò; Lombardi, Federico; Elena, Corrada; Ieva, Francesca; Paganoni, Anna Maria

2008-10-01

To achieve a reduction of time to reperfusion through the organization of an interhospital network and the involvement of the Regional Health Authority. Four major endpoints were identified: institutional governance action, clinical management of acute ST-elevation myocardial infarction (STEMI), priority actions for cardiac arrest and early defibrillation, actions to avoid the delay related to decision-making, and logistic factors. Since 2001 in the urban area of Milan a network has been operating among 23 coronary care units, the 118 Dispatch Center (national free number for medical emergencies) and the Health Country Government Agency named Group for Prehospital Cardiac Emergency. In order to monitor the network activity and time to treatment and clinical outcomes a periodic monthly survey, called MOMI (One Month Monitoring Myocardial Infarction), was undertaken and repeated twice yearly. Data were evaluated according to hospital admission modality. Global times are: symptom onset to first medical contact 116 min (interquartile range [IQR] 189), time to first ECG 7 min (IQR 12), door-to-balloon time 77 min (IQR 81.7). Non-parametric test showed that the modality of hospital admittance was the most critical determinant of door-to-balloon time. The shortest one (49.5 min) was that of patients transported by means of advanced rescue units with 12-lead ECG teletransmission and activation of a fast track directly to the cath lab. Our data show how in a complex urban area the organization of an interhospital network and the availability of ECG teletransmission are effective in reducing time to reperfusion, in the treatment of major arrhythmias and in pre-alert of coronary care units and cath labs in case of confirmed STEMI. This experience also stimulated an improvement in technological equipment of rescue units with extension of 12-lead teletransmission to basic life support units. Through the Health Country Government Agency and the Scientific Societies we carry on with our job to create a regional network for cardiac emergency involving all the hospitals.

Microprocessor Based Real-Time Monitoring of Multiple ECG Signals

PubMed Central

Nasipuri, M.; Basu, D.K.; Dattagupta, R.; Kundu, M.; Banerjee, S.

1987-01-01

A microprocessor based system capable of realtime monitoring of multiple ECG signals has been described. The system consists of a number of microprocessors connected in a hierarchical fashion and capable of working concurrently on ECG data collected from different channels. The system can monitor different arrhythmic abnormalities for at least 36 patients even for a heart rate of 500 beats/min.

e-Learning versus lecture-based courses in ECG interpretation for undergraduate medical students: a randomized noninferiority study.

PubMed

Montassier, Emmanuel; Hardouin, Jean-Benoît; Segard, Julien; Batard, Eric; Potel, Gilles; Planchon, Bernard; Trochu, Jean-Noël; Pottier, Pierre

2016-04-01

An ECG is pivotal for the diagnosis of coronary heart disease. Previous studies have reported deficiencies in ECG interpretation skills that have been responsible for misdiagnosis. However, the optimal way to acquire ECG interpretation skills is still under discussion. Thus, our objective was to compare the effectiveness of e-learning and lecture-based courses for learning ECG interpretation skills in a large randomized study. We conducted a prospective, randomized, controlled, noninferiority study. Participants were recruited from among fifth-year medical students and were assigned to the e-learning group or the lecture-based group using a computer-generated random allocation sequence. The e-learning and lecture-based groups were compared on a score of effectiveness, comparing the 95% unilateral confidence interval (95% UCI) of the score of effectiveness with the mean effectiveness in the lecture-based group, adjusted for a noninferiority margin. Ninety-eight students were enrolled. As compared with the lecture-based course, e-learning was noninferior with regard to the postcourse test score (15.1; 95% UCI 14.2; +∞), which can be compared with 12.5 [the mean effectiveness in the lecture-based group (15.0) minus the noninferiority margin (2.5)]. Furthermore, there was a significant increase in the test score points in both the e-learning and lecture-based groups during the study period (both P<0.0001). Our randomized study showed that the e-learning course is an effective tool for the acquisition of ECG interpretation skills by medical students. These preliminary results should be confirmed with further multicenter studies before the implementation of e-learning courses for learning ECG interpretation skills during medical school.

Design of electrocardiography measurement system with an algorithm to remove noise

NASA Astrophysics Data System (ADS)

Kwon, Hyeokjun; Oh, Sechang; Kumar, Prashanth; Varadan, Vijay K.

2011-04-01

Electrocardiography (ECG) is an important diagnostic tool that can provide vital information about diseases that may not be detectable with other biological signals like, SpO2(Oxygen Saturation), pulse rate, respiration, and blood pressure. For this reason, EKG measurement is mandatory for accurate diagnosis. Recent development in information technology has facilitated remote monitoring systems which can check patient's current status. Moreover, remote monitoring systems can obviate the need for patients to go to hospitals periodically. Such representative wireless communication system is Zigbee sensor network because Zigbee sensor network provides low power consumption and multi-device connection. When we measure EKG signal, another important factor that we should consider is about unexpected signals mixed to EKG signal. The unexpected signals give a severe impact in distorting original EKG signal. There are three kinds of types in noise elements such as muscle noise, movement noise, and respiration noise. This paper describes the design method for EKG measurement system with Zigbee sensor network and proposes an algorithm to remove noises from measured ECG signal.

Predicting termination of atrial fibrillation based on the structure and quantification of the recurrence plot.

PubMed

Sun, Rongrong; Wang, Yuanyuan

2008-11-01

Predicting the spontaneous termination of the atrial fibrillation (AF) leads to not only better understanding of mechanisms of the arrhythmia but also the improved treatment of the sustained AF. A novel method is proposed to characterize the AF based on structure and the quantification of the recurrence plot (RP) to predict the termination of the AF. The RP of the electrocardiogram (ECG) signal is firstly obtained and eleven features are extracted to characterize its three basic patterns. Then the sequential forward search (SFS) algorithm and Davies-Bouldin criterion are utilized to select the feature subset which can predict the AF termination effectively. Finally, the multilayer perceptron (MLP) neural network is applied to predict the AF termination. An AF database which includes one training set and two testing sets (A and B) of Holter ECG recordings is studied. Experiment results show that 97% of testing set A and 95% of testing set B are correctly classified. It demonstrates that this algorithm has the ability to predict the spontaneous termination of the AF effectively.

SenseMyHeart: A cloud service and API for wearable heart monitors.

PubMed

Pinto Silva, P M; Silva Cunha, J P

2015-01-01

In the era of ubiquitous computing, the growing adoption of wearable systems and body sensor networks is trailing the path for new research and software for cardiovascular intensity, energy expenditure and stress and fatigue detection through cardiovascular monitoring. Several systems have received clinical-certification and provide huge amounts of reliable heart-related data in a continuous basis. PhysioNet provides equally reliable open-source software tools for ECG processing and analysis that can be combined with these devices. However, this software remains difficult to use in a mobile environment and for researchers unfamiliar with Linux-based systems. In the present paper we present an approach that aims at tackling these limitations by developing a cloud service that provides an API for a PhysioNet-based pipeline for ECG processing and Heart Rate Variability measurement. We describe the proposed solution, along with its advantages and tradeoffs. We also present some client tools (windows and Android) and several projects where the developed cloud service has been used successfully as a standard for Heart Rate and Heart Rate Variability studies in different scenarios.

ANMCO/SIT Consensus Document: telemedicine for cardiovascular emergency networks

PubMed Central

Gulizia, Michele Massimo; Gabrielli, Domenico; Sicuro, Marco; De Gennaro, Luisa; Giammaria, Massimo; Grieco, Niccolò Brenno; Grosseto, Daniele; Mantovan, Roberto; Mazzanti, Marco; Menotti, Alberto; Brunetti, Natale Daniele; Severi, Silva; Russo, Giancarmine; Gensini, Gian Franco

2017-01-01

Abstract Telemedicine has deeply innovated the field of emergency cardiology, particularly the treatment of acute myocardial infarction. The ability to record an ECG in the early prehospital phase, thus avoiding any delay in diagnosing myocardial infarction with direct transfer to the cath-lab for primary angioplasty, has proven to significantly reduce treatment times and mortality. This consensus document aims to analyse the available evidence and organizational models based on a support by telemedicine, focusing on technical requirements, education, and legal aspects. PMID:28751844

Development of a portable Linux-based ECG measurement and monitoring system.

PubMed

Tan, Tan-Hsu; Chang, Ching-Su; Huang, Yung-Fa; Chen, Yung-Fu; Lee, Cheng

2011-08-01

This work presents a portable Linux-based electrocardiogram (ECG) signals measurement and monitoring system. The proposed system consists of an ECG front end and an embedded Linux platform (ELP). The ECG front end digitizes 12-lead ECG signals acquired from electrodes and then delivers them to the ELP via a universal serial bus (USB) interface for storage, signal processing, and graphic display. The proposed system can be installed anywhere (e.g., offices, homes, healthcare centers and ambulances) to allow people to self-monitor their health conditions at any time. The proposed system also enables remote diagnosis via Internet. Additionally, the system has a 7-in. interactive TFT-LCD touch screen that enables users to execute various functions, such as scaling a single-lead or multiple-lead ECG waveforms. The effectiveness of the proposed system was verified by using a commercial 12-lead ECG signal simulator and in vivo experiments. In addition to its portability, the proposed system is license-free as Linux, an open-source code, is utilized during software development. The cost-effectiveness of the system significantly enhances its practical application for personal healthcare.

A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing.

PubMed

Oresko, Joseph J; Duschl, Heather; Cheng, Allen C

2010-05-01

Cardiovascular disease (CVD) is the single leading cause of global mortality and is projected to remain so. Cardiac arrhythmia is a very common type of CVD and may indicate an increased risk of stroke or sudden cardiac death. The ECG is the most widely adopted clinical tool to diagnose and assess the risk of arrhythmia. ECGs measure and display the electrical activity of the heart from the body surface. During patients' hospital visits, however, arrhythmias may not be detected on standard resting ECG machines, since the condition may not be present at that moment in time. While Holter-based portable monitoring solutions offer 24-48 h ECG recording, they lack the capability of providing any real-time feedback for the thousands of heart beats they record, which must be tediously analyzed offline. In this paper, we seek to unite the portability of Holter monitors and the real-time processing capability of state-of-the-art resting ECG machines to provide an assistive diagnosis solution using smartphones. Specifically, we developed two smartphone-based wearable CVD-detection platforms capable of performing real-time ECG acquisition and display, feature extraction, and beat classification. Furthermore, the same statistical summaries available on resting ECG machines are provided.

Empirical mode decomposition of the ECG signal for noise removal

NASA Astrophysics Data System (ADS)

Khan, Jesmin; Bhuiyan, Sharif; Murphy, Gregory; Alam, Mohammad

2011-04-01

Electrocardiography is a diagnostic procedure for the detection and diagnosis of heart abnormalities. The electrocardiogram (ECG) signal contains important information that is utilized by physicians for the diagnosis and analysis of heart diseases. So good quality ECG signal plays a vital role for the interpretation and identification of pathological, anatomical and physiological aspects of the whole cardiac muscle. However, the ECG signals are corrupted by noise which severely limit the utility of the recorded ECG signal for medical evaluation. The most common noise presents in the ECG signal is the high frequency noise caused by the forces acting on the electrodes. In this paper, we propose a new ECG denoising method based on the empirical mode decomposition (EMD). The proposed method is able to enhance the ECG signal upon removing the noise with minimum signal distortion. Simulation is done on the MIT-BIH database to verify the efficacy of the proposed algorithm. Experiments show that the presented method offers very good results to remove noise from the ECG signal.

Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator.

PubMed

Hannula, Manne; Huttunen, Kerttu; Koskelo, Jukka; Laitinen, Tomi; Leino, Tuomo

2008-01-01

In this study, the performances of artificial neural network (ANN) analysis and multilinear regression (MLR) model-based estimation of heart rate were compared in an evaluation of individual cognitive workload. The data comprised electrocardiography (ECG) measurements and an evaluation of cognitive load that induces psychophysiological stress (PPS), collected from 14 interceptor fighter pilots during complex simulated F/A-18 Hornet air battles. In our data, the mean absolute error of the ANN estimate was 11.4 as a visual analog scale score, being 13-23% better than the mean absolute error of the MLR model in the estimation of cognitive workload.

Design and implementation of a multiband digital filter using FPGA to extract the ECG signal in the presence of different interference signals.

PubMed

Aboutabikh, Kamal; Aboukerdah, Nader

2015-07-01

In this paper, we propose a practical way to synthesize and filter an ECG signal in the presence of four types of interference signals: (1) those arising from power networks with a fundamental frequency of 50Hz, (2) those arising from respiration, having a frequency range from 0.05 to 0.5Hz, (3) muscle signals with a frequency of 25Hz, and (4) white noise present within the ECG signal band. This was done by implementing a multiband digital filter (seven bands) of type FIR Multiband Least Squares using a digital programmable device (Cyclone II EP2C70F896C6 FPGA, Altera), which was placed on an education and development board (DE2-70, Terasic). This filter was designed using the VHDL language in the Quartus II 9.1 design environment. The proposed method depends on Direct Digital Frequency Synthesizers (DDFS) designed to synthesize the ECG signal and various interference signals. So that the synthetic ECG specifications would be closer to actual ECG signals after filtering, we designed in a single multiband digital filter instead of using three separate digital filters LPF, HPF, BSF. Thus all interference signals were removed with a single digital filter. The multiband digital filter results were studied using a digital oscilloscope to characterize input and output signals in the presence of differing sinusoidal interference signals and white noise. Copyright © 2015 Elsevier Ltd. All rights reserved.

Enhancement of low sampling frequency recordings for ECG biometric matching using interpolation.

PubMed

Sidek, Khairul Azami; Khalil, Ibrahim

2013-01-01

Electrocardiogram (ECG) based biometric matching suffers from high misclassification error with lower sampling frequency data. This situation may lead to an unreliable and vulnerable identity authentication process in high security applications. In this paper, quality enhancement techniques for ECG data with low sampling frequency has been proposed for person identification based on piecewise cubic Hermite interpolation (PCHIP) and piecewise cubic spline interpolation (SPLINE). A total of 70 ECG recordings from 4 different public ECG databases with 2 different sampling frequencies were applied for development and performance comparison purposes. An analytical method was used for feature extraction. The ECG recordings were segmented into two parts: the enrolment and recognition datasets. Three biometric matching methods, namely, Cross Correlation (CC), Percent Root-Mean-Square Deviation (PRD) and Wavelet Distance Measurement (WDM) were used for performance evaluation before and after applying interpolation techniques. Results of the experiments suggest that biometric matching with interpolated ECG data on average achieved higher matching percentage value of up to 4% for CC, 3% for PRD and 94% for WDM. These results are compared with the existing method when using ECG recordings with lower sampling frequency. Moreover, increasing the sample size from 56 to 70 subjects improves the results of the experiment by 4% for CC, 14.6% for PRD and 0.3% for WDM. Furthermore, higher classification accuracy of up to 99.1% for PCHIP and 99.2% for SPLINE with interpolated ECG data as compared of up to 97.2% without interpolation ECG data verifies the study claim that applying interpolation techniques enhances the quality of the ECG data. Crown Copyright © 2012. Published by Elsevier Ireland Ltd. All rights reserved.

Derivation of respiration rate from ambulatory ECG and PPG using Ensemble Empirical Mode Decomposition: Comparison and fusion.

PubMed

Orphanidou, Christina

2017-02-01

A new method for extracting the respiratory rate from ECG and PPG obtained via wearable sensors is presented. The proposed technique employs Ensemble Empirical Mode Decomposition in order to identify the respiration "mode" from the noise-corrupted Heart Rate Variability/Pulse Rate Variability and Amplitude Modulation signals extracted from ECG and PPG signals. The technique was validated with respect to a Respiratory Impedance Pneumography (RIP) signal using the mean absolute and the average relative errors for a group ambulatory hospital patients. We compared approaches using single respiration-induced modulations on the ECG and PPG signals with approaches fusing the different modulations. Additionally, we investigated whether the presence of both the simultaneously recorded ECG and PPG signals provided a benefit in the overall system performance. Our method outperformed state-of-the-art ECG- and PPG-based algorithms and gave the best results over the whole database with a mean error of 1.8bpm for 1min estimates when using the fused ECG modulations, which was a relative error of 10.3%. No statistically significant differences were found when comparing the ECG-, PPG- and ECG/PPG-based approaches, indicating that the PPG can be used as a valid alternative to the ECG for applications using wearable sensors. While the presence of both the ECG and PPG signals did not provide an improvement in the estimation error, it increased the proportion of windows for which an estimate was obtained by at least 9%, indicating that the use of two simultaneously recorded signals might be desirable in high-acuity cases where an RR estimate is required more frequently. Copyright © 2016 Elsevier Ltd. All rights reserved.

A novel ECG data compression method based on adaptive Fourier decomposition

NASA Astrophysics Data System (ADS)

Tan, Chunyu; Zhang, Liming

2017-12-01

This paper presents a novel electrocardiogram (ECG) compression method based on adaptive Fourier decomposition (AFD). AFD is a newly developed signal decomposition approach, which can decompose a signal with fast convergence, and hence reconstruct ECG signals with high fidelity. Unlike most of the high performance algorithms, our method does not make use of any preprocessing operation before compression. Huffman coding is employed for further compression. Validated with 48 ECG recordings of MIT-BIH arrhythmia database, the proposed method achieves the compression ratio (CR) of 35.53 and the percentage root mean square difference (PRD) of 1.47% on average with N = 8 decomposition times and a robust PRD-CR relationship. The results demonstrate that the proposed method has a good performance compared with the state-of-the-art ECG compressors.

Electrocardiogram findings in emergency department patients with syncope.

PubMed

Quinn, James; McDermott, Daniel

2011-07-01

To determine the sensitivity and specificity of the San Francisco Syncope Rule (SFSR) electrocardiogram (ECG) criteria for determining cardiac outcomes and to define the specific ECG findings that are the most important in patients with syncope. A consecutive cohort of emergency department (ED) patients with syncope or near syncope was considered. The treating emergency physicians assessed 50 predictor variables, including an ECG and rhythm assessment. For the ECG assessment, the physicians were asked to categorize the ECG as normal or abnormal based on any changes that were old or new. They also did a separate rhythm assessment and could use any of the ECGs or available monitoring strips, including prehospital strips, when making this assessment. All patients were followed up to determine a broad composite study outcome. The final ECG criterion for the SFSR was any nonsinus rhythm or new ECG changes. In this specific study, the initial assessments in the database were used to determine only cardiac-related outcomes (arrhythmia, myocardial infarction, structural, sudden death) based on set criteria, and the authors determined the sensitivity and specificity of the ECG criteria for cardiac outcomes only. All ECGs classified as "abnormal" by the study criteria were compared to the official cardiology reading to determine specific findings on the ECG. Univariate and multivariate analysis were used to determine important specific ECG and rhythm findings. A total of 684 consecutive patients were considered, with 218 having positive ECG criteria and 42 (6%) having important cardiac outcomes. ECG criteria predicted 36 of 42 patients with cardiac outcomes, with a sensitivity of 86% (95% confidence interval [CI] = 71% to 94%), a specificity of 70% (95% CI = 66% to 74%), and a negative predictive value of 99% (95% CI = 97% to 99%). Regarding specific ECG findings, any nonsinus rhythm from any source and any left bundle conduction problem (i.e., any left bundle branch block, left anterior fascicular block, left posterior fascicular block, or QRS widening) were 2.5 and 3.5 times more likely associated with significant cardiac outcomes. The ECG criteria from the SFSR are relatively simple, and if used correctly can help predict which patients are at risk of cardiac outcomes. Furthermore, any left bundle branch block conduction problems or any nonsinus rhythms found during the ED stay should be especially concerning for physicians caring for patients presenting with syncope. © 2011 by the Society for Academic Emergency Medicine.

An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare.

PubMed

Yang, Zhe; Zhou, Qihao; Lei, Lei; Zheng, Kan; Xiang, Wei

2016-12-01

Public healthcare has been paid an increasing attention given the exponential growth human population and medical expenses. It is well known that an effective health monitoring system can detect abnormalities of health conditions in time and make diagnoses according to the gleaned data. As a vital approach to diagnose heart diseases, ECG monitoring is widely studied and applied. However, nearly all existing portable ECG monitoring systems cannot work without a mobile application, which is responsible for data collection and display. In this paper, we propose a new method for ECG monitoring based on Internet-of-Things (IoT) techniques. ECG data are gathered using a wearable monitoring node and are transmitted directly to the IoT cloud using Wi-Fi. Both the HTTP and MQTT protocols are employed in the IoT cloud in order to provide visual and timely ECG data to users. Nearly all smart terminals with a web browser can acquire ECG data conveniently, which has greatly alleviated the cross-platform issue. Experiments are carried out on healthy volunteers in order to verify the reliability of the entire system. Experimental results reveal that the proposed system is reliable in collecting and displaying real-time ECG data, which can aid in the primary diagnosis of certain heart diseases.

Near Field Communication-based telemonitoring with integrated ECG recordings

PubMed Central

Morak, J.; Kumpusch, H.; Hayn, D.; Leitner, M.; Scherr, D.; Fruhwald, F.M.; Schreier, G.

2011-01-01

Objectives Telemonitoring of vital signs is an established option in treatment of patients with chronic heart failure (CHF). In order to allow for early detection of atrial fibrillation (AF) which is highly prevalent in the CHF population telemonitoring programs should include electrocardiogram (ECG) signals. It was therefore the aim to extend our current home monitoring system based on mobile phones and Near Field Communication technology (NFC) to enable patients acquiring their ECG signals autonomously in an easy-to-use way. Methods We prototypically developed a sensing device for the concurrent acquisition of blood pressure and ECG signals. The design of the device equipped with NFC technology and Bluetooth allowed for intuitive interaction with a mobile phone based patient terminal. This ECG monitoring system was evaluated in the course of a clinical pilot trial to assess the system’s technical feasibility, usability and patient’s adherence to twice daily usage. Results 21 patients (4f, 54 ± 14 years) suffering from CHF were included in the study and were asked to transmit two ECG recordings per day via the telemonitoring system autonomously over a monitoring period of seven days. One patient dropped out from the study. 211 data sets were transmitted over a cumulative monitoring period of 140 days (overall adherence rate 82.2%). 55% and 8% of the transmitted ECG signals were sufficient for ventricular and atrial rhythm assessment, respectively. Conclusions Although ECG signal quality has to be improved for better AF detection the developed communication design of joining Bluetooth and NFC technology in our telemonitoring system allows for ambulatory ECG acquisition with high adherence rates and system usability in heart failure patients. PMID:23616890

A system for intelligent home care ECG upload and priorisation.

PubMed

D'Angelo, Lorenzo T; Tarita, Eugeniu; Zywietz, Tosja K; Lueth, Tim C

2010-01-01

In this contribution, a system for internet based, automated home care ECG upload and priorisation is presented for the first time. It unifies the advantages of existing telemonitoring ECG systems adding functionalities such as automated priorisation and usability for home care. Chronic cardiac diseases are a big group in the geriatric field. Most of them can be easily diagnosed with help of an electrocardiogram. A frequent or long-term ECG analysis allows early diagnosis of e.g. a cardiac infarction. Nevertheless, patients often aren't willing to visit a doctor for prophylactic purposes. Possible solutions of this problem are home care devices, which are used to investigate patients at home without the presence of a doctor on site. As the diffusion of such systems leads to a huge amount of data which has to be managed and evaluated, the presented approach focuses on an easy to use software for ECG upload from home, a web based management application and an algorithm for ECG preanalysis and priorisation.

Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment

PubMed Central

De Cooman, Thomas; Gu, Ying; Cleeren, Evy; Claes, Kasper; Van Paesschen, Wim; Van Huffel, Sabine; Hunyadi, Borbála

2017-01-01

Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG. PMID:29027928

Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment.

PubMed

Vandecasteele, Kaat; De Cooman, Thomas; Gu, Ying; Cleeren, Evy; Claes, Kasper; Paesschen, Wim Van; Huffel, Sabine Van; Hunyadi, Borbála

2017-10-13

Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks

PubMed Central

Pirbhulal, Sandeep; Zhang, Heye; Mukhopadhyay, Subhas Chandra; Li, Chunyue; Wang, Yumei; Li, Guanglin; Wu, Wanqing; Zhang, Yuan-Ting

2015-01-01

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption. PMID:26131666

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks.

PubMed

Pirbhulal, Sandeep; Zhang, Heye; Mukhopadhyay, Subhas Chandra; Li, Chunyue; Wang, Yumei; Li, Guanglin; Wu, Wanqing; Zhang, Yuan-Ting

2015-06-26

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node

PubMed Central

Cai, Zhipeng; Zou, Fumin; Zhang, Xiangyu

2018-01-01

Energy efficiency is still the obstacle for long-term real-time wireless ECG monitoring. In this paper, a digital compressed sensing- (CS-) based single-spot Bluetooth ECG node is proposed to deal with the challenge in wireless ECG application. A periodic sleep/wake-up scheme and a CS-based compression algorithm are implemented in a node, which consists of ultra-low-power analog front-end, microcontroller, Bluetooth 4.0 communication module, and so forth. The efficiency improvement and the node's specifics are evidenced by the experiments using the ECG signals sampled by the proposed node under daily activities of lay, sit, stand, walk, and run. Under using sparse binary matrix (SBM), block sparse Bayesian learning (BSBL) method, and discrete cosine transform (DCT) basis, all ECG signals were essentially undistorted recovered with root-mean-square differences (PRDs) which are less than 6%. The proposed sleep/wake-up scheme and data compression can reduce the airtime over energy-hungry wireless links, the energy consumption of proposed node is 6.53 mJ, and the energy consumption of radio decreases 77.37%. Moreover, the energy consumption increase caused by CS code execution is negligible, which is 1.3% of the total energy consumption. PMID:29599945

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach

PubMed Central

Al-Ali, Abdulla; Mohamed, Amr; Ward, Rabab

2018-01-01

Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B/K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance (CR=6 and PRD=1.88) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring. PMID:29337892

A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node.

PubMed

Luo, Kan; Cai, Zhipeng; Du, Keqin; Zou, Fumin; Zhang, Xiangyu; Li, Jianqing

2018-01-01

Energy efficiency is still the obstacle for long-term real-time wireless ECG monitoring. In this paper, a digital compressed sensing- (CS-) based single-spot Bluetooth ECG node is proposed to deal with the challenge in wireless ECG application. A periodic sleep/wake-up scheme and a CS-based compression algorithm are implemented in a node, which consists of ultra-low-power analog front-end, microcontroller, Bluetooth 4.0 communication module, and so forth. The efficiency improvement and the node's specifics are evidenced by the experiments using the ECG signals sampled by the proposed node under daily activities of lay, sit, stand, walk, and run. Under using sparse binary matrix (SBM), block sparse Bayesian learning (BSBL) method, and discrete cosine transform (DCT) basis, all ECG signals were essentially undistorted recovered with root-mean-square differences (PRDs) which are less than 6%. The proposed sleep/wake-up scheme and data compression can reduce the airtime over energy-hungry wireless links, the energy consumption of proposed node is 6.53 mJ, and the energy consumption of radio decreases 77.37%. Moreover, the energy consumption increase caused by CS code execution is negligible, which is 1.3% of the total energy consumption.

Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach.

PubMed

Elgendi, Mohamed; Al-Ali, Abdulla; Mohamed, Amr; Ward, Rabab

2018-01-16

Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.

An integrated healthcare information system for end-to-end standardized exchange and homogeneous management of digital ECG formats.

PubMed

Trigo, Jesús Daniel; Martínez, Ignacio; Alesanco, Alvaro; Kollmann, Alexander; Escayola, Javier; Hayn, Dieter; Schreier, Günter; García, José

2012-07-01

This paper investigates the application of the enterprise information system (EIS) paradigm to standardized cardiovascular condition monitoring. There are many specifications in cardiology, particularly in the ECG standardization arena. The existence of ECG formats, however, does not guarantee the implementation of homogeneous, standardized solutions for ECG management. In fact, hospital management services need to cope with various ECG formats and, moreover, several different visualization applications. This heterogeneity hampers the normalization of integrated, standardized healthcare information systems, hence the need for finding an appropriate combination of ECG formats and a suitable EIS-based software architecture that enables standardized exchange and homogeneous management of ECG formats. Determining such a combination is one objective of this paper. The second aim is to design and develop the integrated healthcare information system that satisfies the requirements posed by the previous determination. The ECG formats selected include ISO/IEEE11073, Standard Communications Protocol for Computer-Assisted Electrocardiography, and an ECG ontology. The EIS-enabling techniques and technologies selected include web services, simple object access protocol, extensible markup language, or business process execution language. Such a selection ensures the standardized exchange of ECGs within, or across, healthcare information systems while providing modularity and accessibility.

Recommendations for the standardization and interpretation of the electrocardiogram. Part I: The electrocardiogram and its technology. A scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society.

PubMed

Kligfield, Paul; Gettes, Leonard S; Bailey, James J; Childers, Rory; Deal, Barbara J; Hancock, E William; van Herpen, Gerard; Kors, Jan A; Macfarlane, Peter; Mirvis, David M; Pahlm, Olle; Rautaharju, Pentti; Wagner, Galen S

2007-03-01

This statement examines the relation of the resting ECG to its technology. Its purpose is to foster understanding of how the modern ECG is derived and displayed and to establish standards that will improve the accuracy and usefulness of the ECG in practice. Derivation of representative waveforms and measurements based on global intervals are described. Special emphasis is placed on digital signal acquisition and computer-based signal processing, which provide automated measurements that lead to computer-generated diagnostic statements. Lead placement, recording methods, and waveform presentation are reviewed. Throughout the statement, recommendations for ECG standards are placed in context of the clinical implications of evolving ECG technology.

Electrocardiogram interpretation and arrhythmia management: a primary and secondary care survey.

PubMed

Begg, Gordon; Willan, Kathryn; Tyndall, Keith; Pepper, Chris; Tayebjee, Muzahir

2016-05-01

There is increasing desire among service commissioners to treat arrhythmia in primary care. Accurate interpretation of the electrocardiogram (ECG) is fundamental to this. ECG interpretation has previously been shown to vary widely but there is little recent data. To examine the interpretation of ECGs in primary and secondary care. A cross-sectional survey of participants' interpretation of six ECGs and hypothetical management of patients based on those ECGs, at primary care educational events, and a cardiology department in Leeds. A total of 262 primary care clinicians and 20 cardiology clinicians were surveyed via questionnaire. Answers were compared with expert electrophysiologist opinion. In primary care, abnormal ECGs were interpreted as normal by 23% of responders. ST elevation and prolonged QT were incorrectly interpreted as normal by 1% and 22%, respectively. In cardiology, abnormal ECGs were interpreted as normal by 3%. ECG provision and interpretation remains inconsistent in both primary and secondary care. Primary care practitioners are less experienced and less confident with ECG interpretation than cardiologists, and require support in this area. © British Journal of General Practice 2016.

FastICA peel-off for ECG interference removal from surface EMG.

PubMed

Chen, Maoqi; Zhang, Xu; Chen, Xiang; Zhu, Mingxing; Li, Guanglin; Zhou, Ping

2016-06-13

Multi-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart. A novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surface EMG signals. Although demonstrating spatial variability in waveform shape, the ECG interference in different channels shares the same firing instants. Utilizing the firing information estimated from FastICA, ECG interference can be separated from surface EMG by a "peel off" processing. The performance of the method was quantified with synthetic signals by combining a series of experimentally recorded "clean" surface EMG and "pure" ECG interference. It was demonstrated that the new method can remove ECG interference efficiently with little distortion to surface EMG amplitude and frequency. The proposed method was also validated using experimental surface EMG signals contaminated by ECG interference. The proposed FastICA peel-off method can be used as a new and practical solution to eliminating ECG interference from multichannel EMG recordings.

A mixed signal ECG processing platform with an adaptive sampling ADC for portable monitoring applications.

PubMed

Kim, Hyejung; Van Hoof, Chris; Yazicioglu, Refet Firat

2011-01-01

This paper describes a mixed-signal ECG processing platform with an 12-bit ADC architecture that can adapt its sampling rate according to the input signals rate of change. This enables the sampling of ECG signals with significantly reduced data rate without loss of information. The presented adaptive sampling scheme reduces the ADC power consumption, enables the processing of ECG signals with lower power consumption, and reduces the power consumption of the radio while streaming the ECG signals. The test results show that running a CWT-based R peak detection algorithm using the adaptively sampled ECG signals consumes only 45.6 μW and it leads to 36% less overall system power consumption.

Competency in ECG Interpretation Among Medical Students

PubMed Central

Kopeć, Grzegorz; Magoń, Wojciech; Hołda, Mateusz; Podolec, Piotr

2015-01-01

Background Electrocardiogram (ECG) is commonly used in diagnosis of heart diseases, including many life-threatening disorders. We aimed to assess skills in ECG interpretation among Polish medical students and to analyze the determinants of these skills. Material/Methods Undergraduates from all Polish medical schools were asked to complete a web-based survey containing 18 ECG strips. Questions concerned primary ECG parameters (rate, rhythm, and axis), emergencies, and common ECG abnormalities. Analysis was restricted to students in their clinical years (4th–6th), and students in their preclinical years (1st–3rd) were used as controls. Results We enrolled 536 medical students (females: n=299; 55.8%), aged 19 to 31 (23±1.6) years from all Polish medical schools. Most (72%) were in their clinical years. The overall rate of good response was better in students in years 4th–5th than those in years 1st–3rd (66% vs. 56%; p<0.0001). Competency in ECG interpretation was higher in students who reported ECG self-learning (69% vs. 62%; p<0.0001) but no difference was found between students who attended or did not attend regular ECG classes (66% vs. 66%; p=0.99). On multivariable analysis (p<0.0001), being in clinical years (OR: 2.45 [1.35–4.46] and self-learning (OR: 2.44 [1.46–4.08]) determined competency in ECG interpretation. Conclusions Polish medical students in their clinical years have a good level of competency in interpreting the primary ECG parameters, but their ability to recognize ECG signs of emergencies and common heart abnormalities is low. ECG interpretation skills are determined by self-education but not by attendance at regular ECG classes. Our results indicate qualitative and quantitative deficiencies in teaching ECG interpretation at medical schools. PMID:26541993

Designing ECG-based physical unclonable function for security of wearable devices.

PubMed

Shihui Yin; Chisung Bae; Sang Joon Kim; Jae-Sun Seo

2017-07-01

As a plethora of wearable devices are being introduced, significant concerns exist on the privacy and security of personal data stored on these devices. Expanding on recent works of using electrocardiogram (ECG) as a modality for biometric authentication, in this work, we investigate the possibility of using personal ECG signals as the individually unique source for physical unclonable function (PUF), which eventually can be used as the key for encryption and decryption engines. We present new signal processing and machine learning algorithms that learn and extract maximally different ECG features for different individuals and minimally different ECG features for the same individual over time. Experimental results with a large 741-subject in-house ECG database show that the distributions of the intra-subject (same person) Hamming distance of extracted ECG features and the inter-subject Hamming distance have minimal overlap. 256-b random numbers generated from the ECG features of 648 (out of 741) subjects pass the NIST randomness tests.

T-wave end detection using neural networks and Support Vector Machines.

PubMed

Suárez-León, Alexander Alexeis; Varon, Carolina; Willems, Rik; Van Huffel, Sabine; Vázquez-Seisdedos, Carlos Román

2018-05-01

In this paper we propose a new approach for detecting the end of the T-wave in the electrocardiogram (ECG) using Neural Networks and Support Vector Machines. Both, Multilayer Perceptron (MLP) neural networks and Fixed-Size Least-Squares Support Vector Machines (FS-LSSVM) were used as regression algorithms to determine the end of the T-wave. Different strategies for selecting the training set such as random selection, k-means, robust clustering and maximum quadratic (Rényi) entropy were evaluated. Individual parameters were tuned for each method during training and the results are given for the evaluation set. A comparison between MLP and FS-LSSVM approaches was performed. Finally, a fair comparison of the FS-LSSVM method with other state-of-the-art algorithms for detecting the end of the T-wave was included. The experimental results show that FS-LSSVM approaches are more suitable as regression algorithms than MLP neural networks. Despite the small training sets used, the FS-LSSVM methods outperformed the state-of-the-art techniques. FS-LSSVM can be successfully used as a T-wave end detection algorithm in ECG even with small training set sizes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Optimisation algorithms for ECG data compression.

PubMed

Haugland, D; Heber, J G; Husøy, J H

1997-07-01

The use of exact optimisation algorithms for compressing digital electrocardiograms (ECGs) is demonstrated. As opposed to traditional time-domain methods, which use heuristics to select a small subset of representative signal samples, the problem of selecting the subset is formulated in rigorous mathematical terms. This approach makes it possible to derive algorithms guaranteeing the smallest possible reconstruction error when a bounded selection of signal samples is interpolated. The proposed model resembles well-known network models and is solved by a cubic dynamic programming algorithm. When applied to standard test problems, the algorithm produces a compressed representation for which the distortion is about one-half of that obtained by traditional time-domain compression techniques at reasonable compression ratios. This illustrates that, in terms of the accuracy of decoded signals, existing time-domain heuristics for ECG compression may be far from what is theoretically achievable. The paper is an attempt to bridge this gap.

Software design of a remote real-time ECG monitoring system

NASA Astrophysics Data System (ADS)

Yu, Chengbo; Tao, Hongyan

2005-12-01

Heart disease is one of the main diseases that threaten the health and lives of human beings. At present, the normal remote ECG monitoring system has the disadvantages of a short testing distance and limitation of monitoring lines. Because of accident and paroxysmal disease, ECG monitoring has extended from the hospital to the family. Therefore, remote ECG monitoring through the Internet has the actual value and significance. The principle and design method of software of the remote dynamic ECG monitor was presented and discussed. The monitoring software is programmed with Delphi software based on client-sever interactive mode. The application program of the system, which makes use of multithreading technology, is shown to perform in an excellent manner. The program includes remote link users and ECG processing, i.e. ECG data's receiving, real-time displaying, recording and replaying. The system can connect many clients simultaneously and perform real-time monitoring to patients.

Real-time prediction of acute cardiovascular events using hardware-implemented Bayesian networks.

PubMed

Tylman, Wojciech; Waszyrowski, Tomasz; Napieralski, Andrzej; Kamiński, Marek; Trafidło, Tamara; Kulesza, Zbigniew; Kotas, Rafał; Marciniak, Paweł; Tomala, Radosław; Wenerski, Maciej

2016-02-01

This paper presents a decision support system that aims to estimate a patient׳s general condition and detect situations which pose an immediate danger to the patient׳s health or life. The use of this system might be especially important in places such as accident and emergency departments or admission wards, where a small medical team has to take care of many patients in various general conditions. Particular stress is laid on cardiovascular and pulmonary conditions, including those leading to sudden cardiac arrest. The proposed system is a stand-alone microprocessor-based device that works in conjunction with a standard vital signs monitor, which provides input signals such as temperature, blood pressure, pulseoxymetry, ECG, and ICG. The signals are preprocessed and analysed by a set of artificial intelligence algorithms, the core of which is based on Bayesian networks. The paper focuses on the construction and evaluation of the Bayesian network, both its structure and numerical specification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Implementation of national practice guidelines to reduce waste and optimize patient value.

PubMed

Langell, John T; Bledsoe, Amber; Vijaykumar, Sathya; Anderson, Terry; Zawalski, Ivy; Zimmerman, Joshua

2016-06-15

The financial health care crisis has provided the platform to drive operational improvements at US health care facilities. This has led to adoption of lean operation principles by many health care organizations as a means of eliminating waste and improving operational efficiencies and overall value to patients. We believe that standardized implementation of national practice guidelines can provide the framework to help to reduce financial waste. We analyzed our institutional preoperative electrocardiogram (ECG) ordering practices for patients undergoing elective surgery at our institution from February-March, 2012 to identify utilization and review compliance with American Heart Association guidelines. We then implemented an ECG ordering algorithm based on these guidelines and studied changes in ordering patterns, associated cost savings and hospital billing for the same period in 2013. From February-March 2012, 677 noncardiac surgical procedures were performed at our institution, and 312 (46.1%) had a preoperative ECG. After implementation of our evidence-based ECG ordering algorithm for the same period in 2013, 707 noncardiac surgical cases were performed, and 120 (16.9%) had a preoperative ECG. Preoperative ECG utilization dropped 63% with an annual institutional cost savings of $72,906 and $291,618 in total annual health care savings. Based on our data, US-wide implementation of our evidence-based ECG ordering algorithm could save the US health care system >$1,868,800,000 per year. Here, we demonstrate that standardized application of a national practice guideline can be used to eliminate nearly $2 billion per year in waste from the US health care system. Copyright © 2016 Elsevier Inc. All rights reserved.

Piezoelectric extraction of ECG signal

NASA Astrophysics Data System (ADS)

Ahmad, Mahmoud Al

2016-11-01

The monitoring and early detection of abnormalities or variations in the cardiac cycle functionality are very critical practices and have significant impact on the prevention of heart diseases and their associated complications. Currently, in the field of biomedical engineering, there is a growing need for devices capable of measuring and monitoring a wide range of cardiac cycle parameters continuously, effectively and on a real-time basis using easily accessible and reusable probes. In this paper, the revolutionary generation and extraction of the corresponding ECG signal using a piezoelectric transducer as alternative for the ECG will be discussed. The piezoelectric transducer pick up the vibrations from the heart beats and convert them into electrical output signals. To this end, piezoelectric and signal processing techniques were employed to extract the ECG corresponding signal from the piezoelectric output voltage signal. The measured electrode based and the extracted piezoelectric based ECG traces are well corroborated. Their peaks amplitudes and locations are well aligned with each other.

Sinabro: A Smartphone-Integrated Opportunistic Electrocardiogram Monitoring System

PubMed Central

Kwon, Sungjun; Lee, Dongseok; Kim, Jeehoon; Lee, Youngki; Kang, Seungwoo; Seo, Sangwon; Park, Kwangsuk

2016-01-01

In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG) monitoring system, Sinabro, which monitors a user’s ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR) and heart rate variability (HRV), to support the pervasive healthcare apps for smartphones based on the user’s high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1) upgrading the sensor device; (2) improving the feature extraction process; and (3) evaluating extensions of the system. We evaluated these extensions with a good set of algorithm parameters that were suggested based on empirical analyses. The results showed that the system could capture ECG reliably and extract highly accurate ECG-derived features with a reasonable rate of data drop during the user’s daily smartphone use. PMID:26978364

Sinabro: A Smartphone-Integrated Opportunistic Electrocardiogram Monitoring System.

PubMed

Kwon, Sungjun; Lee, Dongseok; Kim, Jeehoon; Lee, Youngki; Kang, Seungwoo; Seo, Sangwon; Park, Kwangsuk

2016-03-11

In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG) monitoring system, Sinabro, which monitors a user's ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR) and heart rate variability (HRV), to support the pervasive healthcare apps for smartphones based on the user's high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1) upgrading the sensor device; (2) improving the feature extraction process; and (3) evaluating extensions of the system. We evaluated these extensions with a good set of algorithm parameters that were suggested based on empirical analyses. The results showed that the system could capture ECG reliably and extract highly accurate ECG-derived features with a reasonable rate of data drop during the user's daily smartphone use.

Recommendations for the standardization and interpretation of the electrocardiogram: part I: the electrocardiogram and its technology a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society endorsed by the International Society for Computerized Electrocardiology.

PubMed

Kligfield, Paul; Gettes, Leonard S; Bailey, James J; Childers, Rory; Deal, Barbara J; Hancock, E William; van Herpen, Gerard; Kors, Jan A; Macfarlane, Peter; Mirvis, David M; Pahlm, Olle; Rautaharju, Pentti; Wagner, Galen S; Josephson, Mark; Mason, Jay W; Okin, Peter; Surawicz, Borys; Wellens, Hein

2007-03-13

This statement examines the relation of the resting ECG to its technology. Its purpose is to foster understanding of how the modern ECG is derived and displayed and to establish standards that will improve the accuracy and usefulness of the ECG in practice. Derivation of representative waveforms and measurements based on global intervals are described. Special emphasis is placed on digital signal acquisition and computer-based signal processing, which provide automated measurements that lead to computer-generated diagnostic statements. Lead placement, recording methods, and waveform presentation are reviewed. Throughout the statement, recommendations for ECG standards are placed in context of the clinical implications of evolving ECG technology.

Recommendations for the standardization and interpretation of the electrocardiogram: part I: The electrocardiogram and its technology: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology.

PubMed

Kligfield, Paul; Gettes, Leonard S; Bailey, James J; Childers, Rory; Deal, Barbara J; Hancock, E William; van Herpen, Gerard; Kors, Jan A; Macfarlane, Peter; Mirvis, David M; Pahlm, Olle; Rautaharju, Pentti; Wagner, Galen S; Josephson, Mark; Mason, Jay W; Okin, Peter; Surawicz, Borys; Wellens, Hein

2007-03-13

This statement examines the relation of the resting ECG to its technology. Its purpose is to foster understanding of how the modern ECG is derived and displayed and to establish standards that will improve the accuracy and usefulness of the ECG in practice. Derivation of representative waveforms and measurements based on global intervals are described. Special emphasis is placed on digital signal acquisition and computer-based signal processing, which provide automated measurements that lead to computer-generated diagnostic statements. Lead placement, recording methods, and waveform presentation are reviewed. Throughout the statement, recommendations for ECG standards are placed in context of the clinical implications of evolving ECG technology.

Variable Association between Components of the Metabolic Syndrome and Electrocardiographic Abnormalities in Korean Adults

PubMed Central

Kim, Chul-Hee; Ko, Kwan-Ho; Park, Seong-Wook; Park, Joong-Yeol; Lee, Ki-Up

2010-01-01

Background/Aims Resting electrocardiogram (ECG) abnormalities have been strongly associated with cardiovascular disease mortality. Little is known, however, about the association between individual components of metabolic syndrome and ECG abnormalities, especially in Asian populations. Methods We examined clinical and laboratory data from 31,399 subjects (age 20 to 89 years) who underwent medical check-ups. ECG abnormalities were divided into minor and major abnormalities based on Novacode criteria. Ischemic ECG findings were separately identified and analyzed. Results The overall prevalence rates of ECG abnormalities were significantly higher in subjects with than in those without metabolic syndrome (p < 0.01). Ischemic ECG was strongly associated with metabolic syndrome in all age groups of both sexes, except for younger women. In multiple logistic regression analysis, metabolic syndrome was independently associated with ischemic ECG (odds ratio, 2.30 [2.04 to 2.62]; p < 0.01), after adjusting for sex, age, smoking, and family history of cardiovascular disease. Of the metabolic syndrome components, hyperglycemia in younger subjects and hypertension in elderly subjects were major factors for ischemic ECG changes, whereas hypertriglyceridemia was not an independent risk factor in any age group. The association between ischemic ECG findings and central obesity was weaker in women than in men. Conclusions Metabolic syndrome was strongly associated with ECG abnormalities, especially ischemic ECG findings, in Koreans. The association between each component of metabolic syndrome and ECG abnormalities varied according to age and sex. PMID:20526391

Study design and rationale for biomedical shirt-based electrocardiography monitoring in relevant clinical situations: ECG-shirt study.

PubMed

Balsam, Paweł; Lodziński, Piotr; Tymińska, Agata; Ozierański, Krzysztof; Januszkiewicz, Łukasz; Główczyńska, Renata; Wesołowska, Katarzyna; Peller, Michał; Pietrzak, Radosław; Książczyk, Tomasz; Borodzicz, Sonia; Kołtowski, Łukasz; Borkowski, Mariusz; Werner, Bożena; Opolski, Grzegorz; Grabowski, Marcin

2018-01-01

Today, the main challenge for researchers is to develop new technologies which may help to improve the diagnoses of cardiovascular disease (CVD), thereby reducing healthcare costs and improving the quality of life for patients. This study aims to show the utility of biomedical shirt-based electrocardiography (ECG) monitoring of patients with CVD in different clinical situations using the Nuubo® ECG (nECG) system. An investigator-initiated, multicenter, prospective observational study was carried out in a cardiology (adult and pediatric) and cardiac rehabilitation wards. ECG monitoring was used with the biomedical shirt in the following four independent groups of patients: 1) 30 patients after pulmonary vein isolation (PVI), 2) 30 cardiac resynchronization therapy (CRT) recipients, 3) 120 patients during cardiac rehabilitation after myocardial infarction, and 4) 40 pediatric patients with supraventricular tachycardia (SVT) before electrophysiology study. Approval for all study groups was obtained from the institutional review board. The biomedical shirt captures the electrocardiographic signal via textile electrodes integrated into a garment. The software allows the visualization and analysis of data such as ECG, heart rate, arrhythmia detecting algorithm and relative position of the body is captured by an electronic device. The major advantages of the nECG system are continuous ECG monitoring during daily activities, high quality of ECG recordings, as well as assurance of a proper adherence due to adequate comfort while wearing the shirt. There are only a few studies that have examined wearable systems, especially in pediatric populations. This study is registered in ClinicalTrials.gov: Identifier NCT03068169. (Cardiol J 2018; 25, 1: 52-59).

Implementation of a portable device for real-time ECG signal analysis.

PubMed

Jeon, Taegyun; Kim, Byoungho; Jeon, Moongu; Lee, Byung-Geun

2014-12-10

Cardiac disease is one of the main causes of catastrophic mortality. Therefore, detecting the symptoms of cardiac disease as early as possible is important for increasing the patient's survival. In this study, a compact and effective architecture for detecting atrial fibrillation (AFib) and myocardial ischemia is proposed. We developed a portable device using this architecture, which allows real-time electrocardiogram (ECG) signal acquisition and analysis for cardiac diseases. A noisy ECG signal was preprocessed by an analog front-end consisting of analog filters and amplifiers before it was converted into digital data. The analog front-end was minimized to reduce the size of the device and power consumption by implementing some of its functions with digital filters realized in software. With the ECG data, we detected QRS complexes based on wavelet analysis and feature extraction for morphological shape and regularity using an ARM processor. A classifier for cardiac disease was constructed based on features extracted from a training dataset using support vector machines. The classifier then categorized the ECG data into normal beats, AFib, and myocardial ischemia. A portable ECG device was implemented, and successfully acquired and processed ECG signals. The performance of this device was also verified by comparing the processed ECG data with high-quality ECG data from a public cardiac database. Because of reduced computational complexity, the ARM processor was able to process up to a thousand samples per second, and this allowed real-time acquisition and diagnosis of heart disease. Experimental results for detection of heart disease showed that the device classified AFib and ischemia with a sensitivity of 95.1% and a specificity of 95.9%. Current home care and telemedicine systems have a separate device and diagnostic service system, which results in additional time and cost. Our proposed portable ECG device provides captured ECG data and suspected waveform to identify sporadic and chronic events of heart diseases. This device has been built and evaluated for high quality of signals, low computational complexity, and accurate detection.

Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury

PubMed Central

Nitzken, Matthew; Bajaj, Nihit; Aslan, Sevda; Gimel’farb, Georgy; Ovechkin, Alexander

2013-01-01

Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals. PMID:24307920

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals.

PubMed

Sahoo, Prasan Kumar; Thakkar, Hiren Kumar; Lin, Wen-Yen; Chang, Po-Cheng; Lee, Ming-Yih

2018-01-28

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG.

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

PubMed Central

Lin, Wen-Yen; Chang, Po-Cheng

2018-01-01

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG. PMID:29382098

Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury.

PubMed

Nitzken, Matthew; Bajaj, Nihit; Aslan, Sevda; Gimel'farb, Georgy; El-Baz, Ayman; Ovechkin, Alexander

2013-07-18

Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals.

A review on digital ECG formats and the relationships between them.

PubMed

Trigo, Jesús Daniel; Alesanco, Alvaro; Martínez, Ignacio; García, José

2012-05-01

A plethora of digital ECG formats have been proposed and implemented. This heterogeneity hinders the design and development of interoperable systems and entails critical integration issues for the healthcare information systems. This paper aims at performing a comprehensive overview on the current state of affairs of the interoperable exchange of digital ECG signals. This includes 1) a review on existing digital ECG formats, 2) a collection of applications and cardiology settings using such formats, 3) a compilation of the relationships between such formats, and 4) a reflection on the current situation and foreseeable future of the interoperable exchange of digital ECG signals. The objectives have been approached by completing and updating previous reviews on the topic through appropriate database mining. 39 digital ECG formats, 56 applications, tools or implantation experiences, 47 mappings/converters, and 6 relationships between such formats have been found in the literature. The creation and generalization of a single standardized ECG format is a desirable goal. However, this unification requires political commitment and international cooperation among different standardization bodies. Ongoing ontology-based approaches covering ECG domain have recently emerged as a promising alternative for reaching fully fledged ECG interoperability in the near future.

Spatial enhancement of ECG using diagnostic similarity score based lead selective multi-scale linear model.

PubMed

Nallikuzhy, Jiss J; Dandapat, S

2017-06-01

In this work, a new patient-specific approach to enhance the spatial resolution of ECG is proposed and evaluated. The proposed model transforms a three-lead ECG into a standard twelve-lead ECG thereby enhancing its spatial resolution. The three leads used for prediction are obtained from the standard twelve-lead ECG. The proposed model takes advantage of the improved inter-lead correlation in wavelet domain. Since the model is patient-specific, it also selects the optimal predictor leads for a given patient using a lead selection algorithm. The lead selection algorithm is based on a new diagnostic similarity score which computes the diagnostic closeness between the original and the spatially enhanced leads. Standard closeness measures are used to assess the performance of the model. The similarity in diagnostic information between the original and the spatially enhanced leads are evaluated using various diagnostic measures. Repeatability and diagnosability are performed to quantify the applicability of the model. A comparison of the proposed model is performed with existing models that transform a subset of standard twelve-lead ECG into the standard twelve-lead ECG. From the analysis of the results, it is evident that the proposed model preserves diagnostic information better compared to other models. Copyright © 2017 Elsevier Ltd. All rights reserved.

A decision support system and rule-based algorithm to augment the human interpretation of the 12-lead electrocardiogram.

PubMed

Cairns, Andrew W; Bond, Raymond R; Finlay, Dewar D; Guldenring, Daniel; Badilini, Fabio; Libretti, Guido; Peace, Aaron J; Leslie, Stephen J

The 12-lead Electrocardiogram (ECG) has been used to detect cardiac abnormalities in the same format for more than 70years. However, due to the complex nature of 12-lead ECG interpretation, there is a significant cognitive workload required from the interpreter. This complexity in ECG interpretation often leads to errors in diagnosis and subsequent treatment. We have previously reported on the development of an ECG interpretation support system designed to augment the human interpretation process. This computerised decision support system has been named 'Interactive Progressive based Interpretation' (IPI). In this study, a decision support algorithm was built into the IPI system to suggest potential diagnoses based on the interpreter's annotations of the 12-lead ECG. We hypothesise semi-automatic interpretation using a digital assistant can be an optimal man-machine model for ECG interpretation. To improve interpretation accuracy and reduce missed co-abnormalities. The Differential Diagnoses Algorithm (DDA) was developed using web technologies where diagnostic ECG criteria are defined in an open storage format, Javascript Object Notation (JSON), which is queried using a rule-based reasoning algorithm to suggest diagnoses. To test our hypothesis, a counterbalanced trial was designed where subjects interpreted ECGs using the conventional approach and using the IPI+DDA approach. A total of 375 interpretations were collected. The IPI+DDA approach was shown to improve diagnostic accuracy by 8.7% (although not statistically significant, p-value=0.1852), the IPI+DDA suggested the correct interpretation more often than the human interpreter in 7/10 cases (varying statistical significance). Human interpretation accuracy increased to 70% when seven suggestions were generated. Although results were not found to be statistically significant, we found; 1) our decision support tool increased the number of correct interpretations, 2) the DDA algorithm suggested the correct interpretation more often than humans, and 3) as many as 7 computerised diagnostic suggestions augmented human decision making in ECG interpretation. Statistical significance may be achieved by expanding sample size. Copyright © 2017 Elsevier Inc. All rights reserved.

A 0.7-V 17.4- μ W 3-lead wireless ECG SoC.

PubMed

Khayatzadeh, Mahmood; Zhang, Xiaoyang; Tan, Jun; Liew, Wen-Sin; Lian, Yong

2013-10-01

This paper presents a fully integrated sub-1 V 3-lead wireless ECG System-on-Chip (SoC) for wireless body sensor network applications. The SoC includes a two-channel ECG front-end with a driven-right-leg circuit, an 8-bit SAR ADC, a custom-designed 16-bit microcontroller, two banks of 16 kb SRAM, and a MICS band transceiver. The microcontroller and SRAM blocks are able to operate at sub-/near-threshold regime for the best energy consumption. The proposed SoC has been implemented in a standard 0.13- μ m CMOS process. Measurement results show the microcontroller consumes only 2.62 pJ per instruction at 0.35 V . Both microcontroller and memory blocks are functional down to 0.25 V. The entire SoC is capable of working at single 0.7-V supply. At the best case, it consumes 17.4 μ W in heart rate detection mode and 74.8 μW in raw data acquisition mode under sampling rate of 500 Hz. This makes it one of the best ECG SoCs among state-of-the-art biomedical chips.

Standard-compliant real-time transmission of ECGs: harmonization of ISO/IEEE 11073-PHD and SCP-ECG.

PubMed

Trigo, Jesús D; Chiarugi, Franco; Alesanco, Alvaro; Martínez-Espronceda, Miguel; Chronaki, Catherine E; Escayola, Javier; Martínez, Ignacio; García, José

2009-01-01

Ambient assisted living and integrated care in an aging society is based on the vision of the lifelong Electronic Health Record calling for HealthCare Information Systems and medical device interoperability. For medical devices this aim can be achieved by the consistent implementation of harmonized international interoperability standards. The ISO/IEEE 11073 (x73) family of standards is a reference standard for medical device interoperability. In its Personal Health Device (PHD) version several devices have been included, but an ECG device specialization is not yet available. On the other hand, the SCP-ECG standard for short-term diagnostic ECGs (EN1064) has been recently approved as an international standard ISO/IEEE 11073-91064:2009. In this paper, the relationships between a proposed x73-PHD model for an ECG device and the fields of the SCP-ECG standard are investigated. A proof-of-concept implementation of the proposed x73-PHD ECG model is also presented, identifying open issues to be addressed by standards development for the wider interoperability adoption of x73-PHD standards.

[The Key Technology Study on Cloud Computing Platform for ECG Monitoring Based on Regional Internet of Things].

PubMed

Yang, Shu; Qiu, Yuyan; Shi, Bo

2016-09-01

This paper explores the methods of building the internet of things of a regional ECG monitoring, focused on the implementation of ECG monitoring center based on cloud computing platform. It analyzes implementation principles of automatic identifi cation in the types of arrhythmia. It also studies the system architecture and key techniques of cloud computing platform, including server load balancing technology, reliable storage of massive smalfi les and the implications of quick search function.

Field programmable gate array based fuzzy neural signal processing system for differential diagnosis of QRS complex tachycardia and tachyarrhythmia in noisy ECG signals.

PubMed

Chowdhury, Shubhajit Roy

2012-04-01

The paper reports of a Field Programmable Gate Array (FPGA) based embedded system for detection of QRS complex in a noisy electrocardiogram (ECG) signal and thereafter differential diagnosis of tachycardia and tachyarrhythmia. The QRS complex has been detected after application of entropy measure of fuzziness to build a detection function of ECG signal, which has been previously filtered to remove power line interference and base line wander. Using the detected QRS complexes, differential diagnosis of tachycardia and tachyarrhythmia has been performed. The entire algorithm has been realized in hardware on an FPGA. Using the standard CSE ECG database, the algorithm performed highly effectively. The performance of the algorithm in respect of QRS detection with sensitivity (Se) of 99.74% and accuracy of 99.5% is achieved when tested using single channel ECG with entropy criteria. The performance of the QRS detection system has been compared and found to be better than most of the QRS detection systems available in literature. Using the system, 200 patients have been diagnosed with an accuracy of 98.5%.

Development of a portable wireless system for bipolar concentric ECG recording

NASA Astrophysics Data System (ADS)

Prats-Boluda, G.; Ye-Lin, Y.; Bueno Barrachina, J. M.; Senent, E.; Rodriguez de Sanabria, R.; Garcia-Casado, J.

2015-07-01

Cardiovascular diseases (CVDs) remain the biggest cause of deaths worldwide. ECG monitoring is a key tool for early diagnosis of CVDs. Conventional monitors use monopolar electrodes resulting in poor spatial resolution surface recordings and requiring extensive wiring. High-spatial resolution surface electrocardiographic recordings provide valuable information for the diagnosis of a wide range of cardiac abnormalities, including infarction and arrhythmia. The aim of this work was to develop and test a wireless recording system for acquiring high spatial resolution ECG signals, based on a flexible tripolar concentric electrode (TCE) without cable wiring or external reference electrode which would make more comnfortable its use in clinical practice. For this, a portable, wireless sensor node for analogue conditioning, digitalization and transmission of a bipolar concentric ECG signal (BC-ECG) using a TCE and a Mason-likar Lead-I ECG (ML-Lead-I ECG) signal was developed. Experimental results from a total of 32 healthy volunteers showed that the ECG fiducial points in the BC-ECG signals, recorded with external and internal reference electrode, are consistent with those of simultaneous ML-Lead-I ECG. No statistically significant difference was found in either signal amplitude or morphology, regardless of the reference electrode used, being the signal-to-noise similar to that of ML-Lead-I ECG. Furthermore, it has been observed that BC-ECG signals contain information that could not available in conventional records, specially related to atria activity. The proposed wireless sensor node provides non-invasive high-local resolution ECG signals using only a TCE without additional wiring, which would have great potential in medical diagnosis of diseases such as atrial or ventricular fibrillations or arrhythmias that currently require invasive diagnostic procedures (catheterization).

Pilot study analyzing automated ECG screening of hypertrophic cardiomyopathy.

PubMed

Campbell, Matthew J; Zhou, Xuefu; Han, Chia; Abrishami, Hedayat; Webster, Gregory; Miyake, Christina Y; Sower, Christopher T; Anderson, Jeffrey B; Knilans, Timothy K; Czosek, Richard J

2017-06-01

Hypertrophic cardiomyopathy (HCM) is one of the leading causes of sudden cardiac death in athletes. However, preparticipation ECG screening has often been criticized for failing to meet cost-effectiveness thresholds, in part because of high false-positive rates and the cost of ECG screening itself. The purpose of this study was to assess the testing characteristics of an automated ECG algorithm designed to screen for HCM in a multi-institutional pediatric cohort. ECGs from patients with HCM aged 12 to 20 years from 3 pediatric institutions were screened for ECG criteria for HCM using a previously described automated computer algorithm developed specifically for HCM ECG screening. The results were compared to a known healthy pediatric cohort. The studies then were read by trained electrophysiologists using standard ECG criteria and compared to the results of automated screening. One hundred twenty-eight ECGs from unique patients with phenotypic HCM were obtained and compared with 256 studies from healthy control patients matched in 2:1 fashion. When presented with the ECGs, the non-voltage-based algorithm resulted in 81.2% sensitivity and 90.7% specificity. A trained electrophysiologist read the same data according to the Seattle Criteria, with 71% sensitivity with 95.7% specificity. The sensitivity of screening as well as the components of the ECG screening itself varied by institution. This pilot study demonstrates a potential for automated ECG screening algorithms to detect HCM with testing characteristics similar to that of a trained electrophysiologist. In addition, there appear to be differences in ECG characteristics between patient populations, which may account for the difficulties in universal screening. Copyright © 2017 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Prediction of heart abnormality using MLP network

NASA Astrophysics Data System (ADS)

Hashim, Fakroul Ridzuan; Januar, Yulni; Mat, Muhammad Hadzren; Rizman, Zairi Ismael; Awang, Mat Kamil

2018-02-01

Heart abnormality does not choose gender, age and races when it strikes. With no warning signs or symptoms, it can result to a sudden death of the patient. Generally, heart's irregular electrical activity is defined as heart abnormality. Via implementation of Multilayer Perceptron (MLP) network, this paper tries to develop a program that allows the detection of heart abnormality activity. Utilizing several training algorithms with Purelin activation function, an amount of heartbeat signals received through the electrocardiogram (ECG) will be employed to condition the MLP network.

E-Bra system for women ECG measurement with GPRS communication, Nanosensor, and motion artifact remove algorithm

NASA Astrophysics Data System (ADS)

Kwon, Hyeokjun; Oh, Sechang; Kumar, Prashanth S.; Varadan, Vijay K.

2012-10-01

CardioVascular Disease(CVD)s lead the sudden cardiac death due to irregular phenomenon of the cardiac signal by the abnormal case of blood vessel and cardiac structure. For last two decades, cardiac disease research for man is under active discussion. As a result, the death rate by cardiac disease in men has been falling gradually compared with relatively increasing the women death rate due to CVD[2]. The main reason of this phenomenon causes the lack a sense of the seriousness to female CVD and different symptom of female CVD compared with the symptoms of male CVD. Usually, because the women CVD accompanies with ordinary symptoms unrecognizing the heart abnormality signal such as unusual fatigue, sleep disturbances, shortness of breath, anxiety, chest discomfort, and indigestion dyspepsia, most women CVD patients do not realize that these symptoms are related to the CVD symptoms. Therefore, periodic ECG signal observation is required for women cardiac disease patients. ElectroCardioGram(ECG) detection, treadmill test/exercise ECG, nuclear scan, coronary angiography, and intracoronary ultrasound are used to diagnose abnormality of heart. Among the medical checkup methods for CVDs checkup, it is very effective method for the diagnosis of cardiac disease and the early detection of heart abnormality to monitor ECG periodically. This paper suggests the effective ECG monitoring system for woman by attaching the system on woman's brassiere by using augmented chest lead attachment method. The suggested system in this paper consists of ECG signal transmission system and a server program to display and analyze the transmitted ECG. The ECG signal transmission system consists of three parts such as ECG physical signal detection part with two electrodes made by gold nanowire structure, data acquisition with AD converter, and data transmission part with GPRS(General Packet Radio Service) communication. Usually, to detect human bio signal, Ag/AgCl or gold cup electrodes are used with conductive gel. However, the gel can be dried when taking long time monitoring. The gold nanowire structure electrodes without consideration of uncomfortable usage of gel are attached on beneath the chest position of a brassiere, and the electrodes convert the physical ECG signal to voltage potential signal. The voltage potential ECG signal is converted to digital signal by AD converter included in microprocessor. The converted ECG signal by AD converter is saved on every 1 sec period in the internal RAM in microprocessor. For transmission of the saved data in the internal RAM to a server computer locating at remote area, the system uses the GPRS communication technology, which can develop the wide area network(WAP) without any gateway and repeater. In addition, the transmission system is operated on client mode of GPRS communication. The remote server is installed a program including the functions of displaying and analyzing the transmitted ECG. To display the ECG data, the program is operated with TCP/IP server mode and static IP address, and to analyze the ECG data, the paper suggests motion artifact remove algorithm including adaptive filter with LMS(least mean square), baseline detection algorithm using predictability estimation theory, a filter with moving weighted factor, low pass filter, peak to peak detection, and interpolation.

Effect of electrocardiogram interference on cortico-cortical connectivity analysis and a possible solution.

PubMed

Govindan, R B; Kota, Srinivas; Al-Shargabi, Tareq; Massaro, An N; Chang, Taeun; du Plessis, Adre

2016-09-01

Electroencephalogram (EEG) signals are often contaminated by the electrocardiogram (ECG) interference, which affects quantitative characterization of EEG. We propose null-coherence, a frequency-based approach, to attenuate the ECG interference in EEG using simultaneously recorded ECG as a reference signal. After validating the proposed approach using numerically simulated data, we apply this approach to EEG recorded from six newborns receiving therapeutic hypothermia for neonatal encephalopathy. We compare our approach with an independent component analysis (ICA), a previously proposed approach to attenuate ECG artifacts in the EEG signal. The power spectrum and the cortico-cortical connectivity of the ECG attenuated EEG was compared against the power spectrum and the cortico-cortical connectivity of the raw EEG. The null-coherence approach attenuated the ECG contamination without leaving any residual of the ECG in the EEG. We show that the null-coherence approach performs better than ICA in attenuating the ECG contamination without enhancing cortico-cortical connectivity. Our analysis suggests that using ICA to remove ECG contamination from the EEG suffers from redistribution problems, whereas the null-coherence approach does not. We show that both the null-coherence and ICA approaches attenuate the ECG contamination. However, the EEG obtained after ICA cleaning displayed higher cortico-cortical connectivity compared with that obtained using the null-coherence approach. This suggests that null-coherence is superior to ICA in attenuating the ECG interference in EEG for cortico-cortical connectivity analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

Research of the Heart Information Monitoring Robert Based on the 3G Wireless Communication Platform

NASA Astrophysics Data System (ADS)

Zhang, Fuli; Yang, Huazhe; Li, Gensong; Hong, Yang; Hu, Qingzhe

Electrocardiogram (ECG) of a person can be recorded and the diagnostic results can be displayed through touching the heart information monitoring Robert. In addition, the heart rate, phonocardiogram (PCG) and the dynamic three-dimensional echocardiography can also be displayed synchronously. Then the difficult ECG can be transmitted to the service center through 3G wireless communication center, followed by diagnosing the ECG by doctors and transmitting the feedback diagnostic results. I-lead ECG of the person can be recorded by the amplification circuit with high gain and low noise. Then, the heart rate and output phonocardiogram are displayed and the model of heart beat are started to trace through the recognition of R wave. Finally, the difficult ECG is transmitted to the service center via 3G communication chips. The displayed ECG is clear, and the stimulated heart beat is synchronous with that of the person. Furthermore, ECG received by the service center is in accordance with the one recorded by the Robert.

Development and validation of a novel algorithm based on the ECG magnet response for rapid identification of any unknown pacemaker.

PubMed

Squara, Fabien; Chik, William W; Benhayon, Daniel; Maeda, Shingo; Latcu, Decebal Gabriel; Lacaze-Gadonneix, Jonathan; Tibi, Thierry; Thomas, Olivier; Cooper, Joshua M; Duthoit, Guillaume

2014-08-01

Pacemaker (PM) interrogation requires correct manufacturer identification. However, an unidentified PM is a frequent occurrence, requiring time-consuming steps to identify the device. The purpose of this study was to develop and validate a novel algorithm for PM manufacturer identification, using the ECG response to magnet application. Data on the magnet responses of all recent PM models (≤15 years) from the 5 major manufacturers were collected. An algorithm based on the ECG response to magnet application to identify the PM manufacturer was subsequently developed. Patients undergoing ECG during magnet application in various clinical situations were prospectively recruited in 7 centers. The algorithm was applied in the analysis of every ECG by a cardiologist blinded to PM information. A second blinded cardiologist analyzed a sample of randomly selected ECGs in order to assess the reproducibility of the results. A total of 250 ECGs were analyzed during magnet application. The algorithm led to the correct single manufacturer choice in 242 ECGs (96.8%), whereas 7 (2.8%) could only be narrowed to either 1 of 2 manufacturer possibilities. Only 2 (0.4%) incorrect manufacturer identifications occurred. The algorithm identified Medtronic and Sorin Group PMs with 100% sensitivity and specificity, Biotronik PMs with 100% sensitivity and 99.5% specificity, and St. Jude and Boston Scientific PMs with 92% sensitivity and 100% specificity. The results were reproducible between the 2 blinded cardiologists with 92% concordant findings. Unknown PM manufacturers can be accurately identified by analyzing the ECG magnet response using this newly developed algorithm. Copyright © 2014 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

Utility of the Electrocardiogram in Drug Overdose and Poisoning: Theoretical Considerations and Clinical Implications

PubMed Central

Yates, Christopher; Manini, Alex F

2012-01-01

The ECG is a rapidly available clinical tool that can help clinicians manage poisoned patients. Specific myocardial effects of cardiotoxic drugs have well-described electrocardiographic manifestations. In the practice of clinical toxicology, classic ECG changes may hint at blockade of ion channels, alterations of adrenergic tone, or dysfunctional metabolic activity of the myocardium. This review will offer a structured approach to ECG interpretation in poisoned patients with a focus on clinical implications and ECG-based management recommendations in the initial evaluation of patients with acute cardiotoxicity. PMID:22708912

Model-based Bayesian filtering of cardiac contaminants from biomedical recordings.

PubMed

Sameni, R; Shamsollahi, M B; Jutten, C

2008-05-01

Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals.

A computer-human interaction model to improve the diagnostic accuracy and clinical decision-making during 12-lead electrocardiogram interpretation.

PubMed

Cairns, Andrew W; Bond, Raymond R; Finlay, Dewar D; Breen, Cathal; Guldenring, Daniel; Gaffney, Robert; Gallagher, Anthony G; Peace, Aaron J; Henn, Pat

2016-12-01

The 12-lead Electrocardiogram (ECG) presents a plethora of information and demands extensive knowledge and a high cognitive workload to interpret. Whilst the ECG is an important clinical tool, it is frequently incorrectly interpreted. Even expert clinicians are known to impulsively provide a diagnosis based on their first impression and often miss co-abnormalities. Given it is widely reported that there is a lack of competency in ECG interpretation, it is imperative to optimise the interpretation process. Predominantly the ECG interpretation process remains a paper based approach and whilst computer algorithms are used to assist interpreters by providing printed computerised diagnoses, there are a lack of interactive human-computer interfaces to guide and assist the interpreter. An interactive computing system was developed to guide the decision making process of a clinician when interpreting the ECG. The system decomposes the interpretation process into a series of interactive sub-tasks and encourages the clinician to systematically interpret the ECG. We have named this model 'Interactive Progressive based Interpretation' (IPI) as the user cannot 'progress' unless they complete each sub-task. Using this model, the ECG is segmented into five parts and presented over five user interfaces (1: Rhythm interpretation, 2: Interpretation of the P-wave morphology, 3: Limb lead interpretation, 4: QRS morphology interpretation with chest lead and rhythm strip presentation and 5: Final review of 12-lead ECG). The IPI model was implemented using emerging web technologies (i.e. HTML5, CSS3, AJAX, PHP and MySQL). It was hypothesised that this system would reduce the number of interpretation errors and increase diagnostic accuracy in ECG interpreters. To test this, we compared the diagnostic accuracy of clinicians when they used the standard approach (control cohort) with clinicians who interpreted the same ECGs using the IPI approach (IPI cohort). For the control cohort, the (mean; standard deviation; confidence interval) of the ECG interpretation accuracy was (45.45%; SD=18.1%; CI=42.07, 48.83). The mean ECG interpretation accuracy rate for the IPI cohort was 58.85% (SD=42.4%; CI=49.12, 68.58), which indicates a positive mean difference of 13.4%. (CI=4.45, 22.35) An N-1 Chi-square test of independence indicated a 92% chance that the IPI cohort will have a higher accuracy rate. Interpreter self-rated confidence also increased between cohorts from a mean of 4.9/10 in the control cohort to 6.8/10 in the IPI cohort (p=0.06). Whilst the IPI cohort had greater diagnostic accuracy, the duration of ECG interpretation was six times longer when compared to the control cohort. We have developed a system that segments and presents the ECG across five graphical user interfaces. Results indicate that this approach improves diagnostic accuracy but with the expense of time, which is a valuable resource in medical practice. Copyright © 2016 Elsevier Inc. All rights reserved.

Home medical monitoring network based on embedded technology

NASA Astrophysics Data System (ADS)

Liu, Guozhong; Deng, Wenyi; Yan, Bixi; Lv, Naiguang

2006-11-01

Remote medical monitoring network for long-term monitoring of physiological variables would be helpful for recovery of patients as people are monitored at more comfortable conditions. Furthermore, long-term monitoring would be beneficial to investigate slowly developing deterioration in wellness status of a subject and provide medical treatment as soon as possible. The home monitor runs on an embedded microcomputer Rabbit3000 and interfaces with different medical monitoring module through serial ports. The network based on asymmetric digital subscriber line (ADSL) or local area network (LAN) is established and a client - server model, each embedded home medical monitor is client and the monitoring center is the server, is applied to the system design. The client is able to provide its information to the server when client's request of connection to the server is permitted. The monitoring center focuses on the management of the communications, the acquisition of medical data, and the visualization and analysis of the data, etc. Diagnosing model of sleep apnea syndrome is built basing on ECG, heart rate, respiration wave, blood pressure, oxygen saturation, air temperature of mouth cavity or nasal cavity, so sleep status can be analyzed by physiological data acquired as people in sleep. Remote medical monitoring network based on embedded micro Internetworking technology have advantages of lower price, convenience and feasibility, which have been tested by the prototype.

A novel algorithm for Bluetooth ECG.

PubMed

Pandya, Utpal T; Desai, Uday B

2012-11-01

In wireless transmission of ECG, data latency will be significant when battery power level and data transmission distance are not maintained. In applications like home monitoring or personalized care, to overcome the joint effect of previous issues of wireless transmission and other ECG measurement noises, a novel filtering strategy is required. Here, a novel algorithm, identified as peak rejection adaptive sampling modified moving average (PRASMMA) algorithm for wireless ECG is introduced. This algorithm first removes error in bit pattern of received data if occurred in wireless transmission and then removes baseline drift. Afterward, a modified moving average is implemented except in the region of each QRS complexes. The algorithm also sets its filtering parameters according to different sampling rate selected for acquisition of signals. To demonstrate the work, a prototyped Bluetooth-based ECG module is used to capture ECG with different sampling rate and in different position of patient. This module transmits ECG wirelessly to Bluetooth-enabled devices where the PRASMMA algorithm is applied on captured ECG. The performance of PRASMMA algorithm is compared with moving average and S-Golay algorithms visually as well as numerically. The results show that the PRASMMA algorithm can significantly improve the ECG reconstruction by efficiently removing the noise and its use can be extended to any parameters where peaks are importance for diagnostic purpose.

Community-Based ECG Monitoring System for Patients with Cardiovascular Diseases.

PubMed

Lin, Bor-Shyh; Wong, Alice M; Tseng, Kevin C

2016-04-01

This study aims to develop a community-based electrocardiogram (ECG) monitoring system for cardiac outpatients to wirelessly detect heart rate, provide personalized healthcare, and enhance interactive social contact because of the prevalence of deaths from cardiovascular disease and the growing problem of aging in the world. The system not only strengthens the performance of the ECG monitoring system but also emphasizes the ergonomic design of wearable devices and user interfaces. In addition, it enables medical professionals to diagnose cardiac symptoms remotely and electronically manage medical reports and suggestions. The experimental result shows high performance of the dry electrode, even in dynamic conditions. The comparison result with different ECG healthcare systems shows the essential factors that the system should possess and the capability of the proposed system. Finally, a user survey was conducted based on the unified theory of acceptance and users of technology (UTAUT) model.

Evaluation of cardiac signals using discrete wavelet transform with MATLAB graphical user interface.

PubMed

John, Agnes Aruna; Subramanian, Aruna Priyadharshni; Jaganathan, Saravana Kumar; Sethuraman, Balasubramanian

2015-01-01

To process the electrocardiogram (ECG) signals using MATLAB-based graphical user interface (GUI) and to classify the signals based on heart rate. The subject condition was identified using R-peak detection based on discrete wavelet transform followed by a Bayes classifier that classifies the ECG signals. The GUI was designed to display the ECG signal plot. Obtained from MIT database 18 patients had normal heart rate and 9 patients had abnormal heart rate; 14.81% of the patients suffered from tachycardia and 18.52% of the patients have bradycardia. The proposed GUI display was found useful to analyze the digitized ECG signal by a non-technical user and may help in diagnostics. Further improvement can be done by employing field programmable gate array for the real time processing of cardiac signals. Copyright © 2015 Cardiological Society of India. Published by Elsevier B.V. All rights reserved.

Machine learning on-a-chip: a high-performance low-power reusable neuron architecture for artificial neural networks in ECG classifications.

PubMed

Sun, Yuwen; Cheng, Allen C

2012-07-01

Artificial neural networks (ANNs) are a promising machine learning technique in classifying non-linear electrocardiogram (ECG) signals and recognizing abnormal patterns suggesting risks of cardiovascular diseases (CVDs). In this paper, we propose a new reusable neuron architecture (RNA) enabling a performance-efficient and cost-effective silicon implementation for ANN. The RNA architecture consists of a single layer of physical RNA neurons, each of which is designed to use minimal hardware resource (e.g., a single 2-input multiplier-accumulator is used to compute the dot product of two vectors). By carefully applying the principal of time sharing, RNA can multiplexs this single layer of physical neurons to efficiently execute both feed-forward and back-propagation computations of an ANN while conserving the area and reducing the power dissipation of the silicon. A three-layer 51-30-12 ANN is implemented in RNA to perform the ECG classification for CVD detection. This RNA hardware also allows on-chip automatic training update. A quantitative design space exploration in area, power dissipation, and execution speed between RNA and three other implementations representative of different reusable hardware strategies is presented and discussed. Compared with an equivalent software implementation in C executed on an embedded microprocessor, the RNA ASIC achieves three orders of magnitude improvements in both the execution speed and the energy efficiency. Copyright © 2012 Elsevier Ltd. All rights reserved.

Cardiovascular emergencies in primary care: an observational retrospective study of a large-scale telecardiology service.

PubMed

Marcolino, Milena Soriano; Santos, Thales Matheus Mendonça; Stefanelli, Fernanda Cotrim; Oliveira, João Antonio de Queiroz; E Silva, Maíra Viana Rego Souza; Andrade, Diomildo Ferreira; Silva, Grace Kelly Matos E; Ribeiro, Antonio Luiz

2017-01-01

Electrocardiograms (ECGs) are an essential examination for identification and management of cardiovascular emergencies.The aim of this study was to report on the frequency and recognition of cardiovascular emergencies in primary care units. Observational retrospective study assessing consecutive patients whose digital ECGs were sent for analysis to the team of the Telehealth Network of Minas Gerais. Data from patients diagnosed with cardiological emergencies in the primary care setting of 750 municipalities in Minas Gerais, Brazil, between March and September 2015, were collected via telephone contact with the healthcare practitioner who performed the ECG. After collection, the data were subjected to statistical analysis. Over the study period, 304 patients with cardiovascular emergencies were diagnosed within primary care. Only 73.4% of these were recognized by the local physicians. Overall, the most frequent ECG abnormalities were acute ischemic patterns (44.7%) and the frequency of such patterns was higher among the ECGs assigned as emergency priority (P = 0.03). It was possible to obtain complete information on 231 patients (75.9%). Among these, the mean age was 65 ± 14.4 years, 57.1% were men and the most prevalent comorbidity was hypertension (68.4%). In total, 77.9% were referred to a unit caring for cases of higher complexity and 11.7% of the patients died. In this study, cardiovascular emergencies were misdiagnosed in primary care settings, acute myocardial ischemia was the most frequent emergency and the mortality rate was high.

Multichannel ECG and Noise Modeling: Application to Maternal and Fetal ECG Signals

NASA Astrophysics Data System (ADS)

Sameni, Reza; Clifford, Gari D.; Jutten, Christian; Shamsollahi, Mohammad B.

2007-12-01

A three-dimensional dynamic model of the electrical activity of the heart is presented. The model is based on the single dipole model of the heart and is later related to the body surface potentials through a linear model which accounts for the temporal movements and rotations of the cardiac dipole, together with a realistic ECG noise model. The proposed model is also generalized to maternal and fetal ECG mixtures recorded from the abdomen of pregnant women in single and multiple pregnancies. The applicability of the model for the evaluation of signal processing algorithms is illustrated using independent component analysis. Considering the difficulties and limitations of recording long-term ECG data, especially from pregnant women, the model described in this paper may serve as an effective means of simulation and analysis of a wide range of ECGs, including adults and fetuses.

A Study on the Optimal Positions of ECG Electrodes in a Garment for the Design of ECG-Monitoring Clothing for Male.

PubMed

Cho, Hakyung; Lee, Joo Hyeon

2015-09-01

Smart clothing is a sort of wearable device used for ubiquitous health monitoring. It provides comfort and efficiency in vital sign measurements and has been studied and developed in various types of monitoring platforms such as T-shirt and sports bra. However, despite these previous approaches, smart clothing for electrocardiography (ECG) monitoring has encountered a serious shortcoming relevant to motion artifacts caused by wearer movement. In effect, motion artifacts are one of the major problems in practical implementation of most wearable health-monitoring devices. In the ECG measurements collected by a garment, motion artifacts are usually caused by improper location of the electrode, leading to lack of contact between the electrode and skin with body motion. The aim of this study was to suggest a design for ECG-monitoring clothing contributing to reduction of motion artifacts. Based on the clothing science theory, it was assumed in this study that the stability of the electrode in a dynamic state differed depending on the electrode location in an ECG-monitoring garment. Founded on this assumption, effects of 56 electrode positions were determined by sectioning the surface of the garment into grids with 6 cm intervals in the front and back of the bodice. In order to determine the optimal locations of the ECG electrodes from the 56 positions, ECG measurements were collected from 10 participants at every electrode position in the garment while the wearer was in motion. The electrode locations indicating both an ECG measurement rate higher than 80.0 % and a large amplitude during motion were selected as the optimal electrode locations. The results of this analysis show four electrode locations with consistently higher ECG measurement rates and larger amplitudes amongst the 56 locations. These four locations were abstracted to be least affected by wearer movement in this research. Based on this result, a design of the garment-formed ECG monitoring platform reflecting the optimal positions of the electrode was suggested.

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

PubMed

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

2016-05-01

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

Advanced ECG in 2016: is there more than just a tracing?

PubMed

Reichlin, Tobias; Abächerli, Roger; Twerenbold, Raphael; Kühne, Michael; Schaer, Beat; Müller, Christian; Sticherling, Christian; Osswald, Stefan

2016-01-01

The 12-lead electrocardiogram (ECG) is the most frequently used technology in clinical cardiology. It is critical for evidence-based management of patients with most cardiovascular conditions, including patients with acute myocardial infarction, suspected chronic cardiac ischaemia, cardiac arrhythmias, heart failure and implantable cardiac devices. In contrast to many other techniques in cardiology, the ECG is simple, small, mobile, universally available and cheap, and therefore particularly attractive. Standard ECG interpretation mainly relies on direct visual assessment. The progress in biomedical computing and signal processing, and the available computational power offer fascinating new options for ECG analysis relevant to all fields of cardiology. Several digital ECG markers and advanced ECG technologies have shown promise in preliminary studies. This article reviews promising novel surface ECG technologies in three different fields. (1) For the detection of myocardial ischaemia and infarction, QRS morphology feature analysis, the analysis of high frequency QRS components (HF-QRS) and methods using vectorcardiography as well as ECG imaging are discussed. (2) For the identification and management of patients with cardiac arrhythmias, methods of advanced P-wave analysis are discussed and the concept of ECG imaging for noninvasive localisation of cardiac arrhythmias is presented. (3) For risk stratification of sudden cardiac death and the selection of patients for medical device therapy, several novel markers including an automated QRS-score for scar quantification, the QRS-T angle or the T-wave peak-to-end-interval are discussed. Despite the existing preliminary data, none of the advanced ECG markers and technologies has yet accomplished the transition into clinical practice. Further refinement of these technologies and broader validation in large unselected patient cohorts are the critical next step needed to facilitate translation of advanced ECG technologies into clinical cardiology.

[Evaluation of a registration card for logging electrocardiographic records into standard personal computers].

PubMed

Pizzuti, A; Baralis, G; Bassignana, A; Antonielli, E; Di Leo, M

1997-01-01

The MS200 Cardioscope, from MRT Micro as., Norway, is a 12 channel ECG card to be directly inserted into a standard personal computer (PC). The standard ISA Bus compatible half length card comes with a set of 10 cables with electrodes and the software for recording, displaying and saving ECG signals. The system is supplied with DOS or Windows software. The goal of the present work was to evaluate the affordability and usability of the MS200 in a clinical setting. We tested the 1.5 DOS version of the software. In 30 patients with various cardiac diseases the ECG signal has been recorded with MS200 and with standard Hellige CardioSmart equipment. The saved ECGs were recalled and printed using an Epson Stylus 800 ink-jet printer. Two cardiologists reviewed the recordings for a looking at output quality, amplitude and speed precision, artifacts, etc. 1) Installation: the card has proven to be totally compatible with the hardware; no changes in default settings had to be made. 2) Usage: the screens are clear; the commands and menus are intuitive and easy to use. Due to the boot-strap and software loading procedures and, most important, off-line printing, the time needed to obtain a complete ECG printout has been longer than that of the reference machine. 3) Archiving and retrieval of ECG: the ECG curves can be saved in original or compressed form: selecting the latter, the noise and non-ECG information is filtered away and the space consumption on disk is reduced: on average, 20 Kb are needed for 10 seconds of signal. The MS200 can be run on a Local Area Network and is prepared for integrating with an existing informative system: we are currently testing the system in this scenery. 4) MS200 includes options for on-line diagnosis, a technology we have not tested in the present work. 5) The only setting allowed for printing full pages is letter size (A4): the quality of printouts is good, with a resolution of 180 DPI. In conclusion, the MS200 system seems reliable and safe. In the configuration we tested, it cannot substitute a dedicated ECG equipment: from this point of view, a smaller PCMCIA-type card with a battery-operated notebook PC will be more suitable for clinical uses. Nevertheless, the possibility to log and track ECG records, integrated into the department informative system, may provide a valuable tool for improving access to medical information.

Smartphone ECG for evaluation of STEMI: results of the ST LEUIS Pilot Study.

PubMed

Muhlestein, Joseph Boone; Le, Viet; Albert, David; Moreno, Fidela Ll; Anderson, Jeffrey L; Yanowitz, Frank; Vranian, Robert B; Barsness, Gregory W; Bethea, Charles F; Severance, Harry W; Ramo, Barry; Pierce, John; Barbagelata, Alejandro; Muhlestein, Joseph Brent

2015-01-01

12-lead ECG is a critical component of initial evaluation of cardiac ischemia, but has traditionally been limited to large, dedicated equipment in medical care environments. Smartphones provide a potential alternative platform for the extension of ECG to new care settings and to improve timeliness of care. To gain experience with smartphone electrocardiography prior to designing a larger multicenter study evaluating standard 12-lead ECG compared to smartphone ECG. 6 patients for whom the hospital STEMI protocol was activated were evaluated with traditional 12-lead ECG followed immediately by a smartphone ECG using right (VnR) and left (VnL) limb leads for precordial grounding. The AliveCor™ Heart Monitor was utilized for this study. All tracings were taken prior to catheterization or immediately after revascularization while still in the catheterization laboratory. The smartphone ECG had excellent correlation with the gold standard 12-lead ECG in all patients. Four out of six tracings were judged to meet STEMI criteria on both modalities as determined by three experienced cardiologists, and in the remaining two, consensus indicated a non-STEMI ECG diagnosis. No significant difference was noted between VnR and VnL. Smartphone based electrocardiography is a promising, developing technology intended to increase availability and speed of electrocardiographic evaluation. This study confirmed the potential of a smartphone ECG for evaluation of acute ischemia and the feasibility of studying this technology further to define the diagnostic accuracy, limitations and appropriate use of this new technology. Copyright © 2015 Elsevier Inc. All rights reserved.

[A novel biologic electricity signal measurement based on neuron chip].

PubMed

Lei, Yinsheng; Wang, Mingshi; Sun, Tongjing; Zhu, Qiang; Qin, Ran

2006-06-01

Neuron chip is a multiprocessor with three pipeline CPU; its communication protocol and control processor are integrated in effect to carry out the function of communication, control, attemper, I/O, etc. A novel biologic electronic signal measurement network system is composed of intelligent measurement nodes with neuron chip at the core. In this study, the electronic signals such as ECG, EEG, EMG and BOS can be synthetically measured by those intelligent nodes, and some valuable diagnostic messages are found. Wavelet transform is employed in this system to analyze various biologic electronic signals due to its strong time-frequency ability of decomposing signal local character. Better effect is gained. This paper introduces the hardware structure of network and intelligent measurement node, the measurement theory and the signal figure of data acquisition and processing.

Cardiac arrhythmia beat classification using DOST and PSO tuned SVM.

PubMed

Raj, Sandeep; Ray, Kailash Chandra; Shankar, Om

2016-11-01

The increase in the number of deaths due to cardiovascular diseases (CVDs) has gained significant attention from the study of electrocardiogram (ECG) signals. These ECG signals are studied by the experienced cardiologist for accurate and proper diagnosis, but it becomes difficult and time-consuming for long-term recordings. Various signal processing techniques are studied to analyze the ECG signal, but they bear limitations due to the non-stationary behavior of ECG signals. Hence, this study aims to improve the classification accuracy rate and provide an automated diagnostic solution for the detection of cardiac arrhythmias. The proposed methodology consists of four stages, i.e. filtering, R-peak detection, feature extraction and classification stages. In this study, Wavelet based approach is used to filter the raw ECG signal, whereas Pan-Tompkins algorithm is used for detecting the R-peak inside the ECG signal. In the feature extraction stage, discrete orthogonal Stockwell transform (DOST) approach is presented for an efficient time-frequency representation (i.e. morphological descriptors) of a time domain signal and retains the absolute phase information to distinguish the various non-stationary behavior ECG signals. Moreover, these morphological descriptors are further reduced in lower dimensional space by using principal component analysis and combined with the dynamic features (i.e based on RR-interval of the ECG signals) of the input signal. This combination of two different kinds of descriptors represents each feature set of an input signal that is utilized for classification into subsequent categories by employing PSO tuned support vector machines (SVM). The proposed methodology is validated on the baseline MIT-BIH arrhythmia database and evaluated under two assessment schemes, yielding an improved overall accuracy of 99.18% for sixteen classes in the category-based and 89.10% for five classes (mapped according to AAMI standard) in the patient-based assessment scheme respectively to the state-of-art diagnosis. The results reported are further compared to the existing methodologies in literature. The proposed feature representation of cardiac signals based on symmetrical features along with PSO based optimization technique for the SVM classifier reported an improved classification accuracy in both the assessment schemes evaluated on the benchmark MIT-BIH arrhythmia database and hence can be utilized for automated computer-aided diagnosis of cardiac arrhythmia beats. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

ECG Denoising Using Marginalized Particle Extended Kalman Filter With an Automatic Particle Weighting Strategy.

PubMed

Hesar, Hamed Danandeh; Mohebbi, Maryam

2017-05-01

In this paper, a model-based Bayesian filtering framework called the "marginalized particle-extended Kalman filter (MP-EKF) algorithm" is proposed for electrocardiogram (ECG) denoising. This algorithm does not have the extended Kalman filter (EKF) shortcoming in handling non-Gaussian nonstationary situations because of its nonlinear framework. In addition, it has less computational complexity compared with particle filter. This filter improves ECG denoising performance by implementing marginalized particle filter framework while reducing its computational complexity using EKF framework. An automatic particle weighting strategy is also proposed here that controls the reliance of our framework to the acquired measurements. We evaluated the proposed filter on several normal ECGs selected from MIT-BIH normal sinus rhythm database. To do so, artificial white Gaussian and colored noises as well as nonstationary real muscle artifact (MA) noise over a range of low SNRs from 10 to -5 dB were added to these normal ECG segments. The benchmark methods were the EKF and extended Kalman smoother (EKS) algorithms which are the first model-based Bayesian algorithms introduced in the field of ECG denoising. From SNR viewpoint, the experiments showed that in the presence of Gaussian white noise, the proposed framework outperforms the EKF and EKS algorithms in lower input SNRs where the measurements and state model are not reliable. Owing to its nonlinear framework and particle weighting strategy, the proposed algorithm attained better results at all input SNRs in non-Gaussian nonstationary situations (such as presence of pink noise, brown noise, and real MA). In addition, the impact of the proposed filtering method on the distortion of diagnostic features of the ECG was investigated and compared with EKF/EKS methods using an ECG diagnostic distortion measure called the "Multi-Scale Entropy Based Weighted Distortion Measure" or MSEWPRD. The results revealed that our proposed algorithm had the lowest MSEPWRD for all noise types at low input SNRs. Therefore, the morphology and diagnostic information of ECG signals were much better conserved compared with EKF/EKS frameworks, especially in non-Gaussian nonstationary situations.

Multi-purpose ECG telemetry system.

PubMed

Marouf, Mohamed; Vukomanovic, Goran; Saranovac, Lazar; Bozic, Miroslav

2017-06-19

The Electrocardiogram ECG is one of the most important non-invasive tools for cardiac diseases diagnosis. Taking advantage of the developed telecommunication infrastructure, several approaches that address the development of telemetry cardiac devices were introduced recently. Telemetry ECG devices allow easy and fast ECG monitoring of patients with suspected cardiac issues. Choosing the right device with the desired working mode, signal quality, and the device cost are still the main obstacles to massive usage of these devices. In this paper, we introduce design, implementation, and validation of a multi-purpose telemetry system for recording, transmission, and interpretation of ECG signals in different recording modes. The system consists of an ECG device, a cloud-based analysis pipeline, and accompanied mobile applications for physicians and patients. The proposed ECG device's mechanical design allows laypersons to easily record post-event short-term ECG signals, using dry electrodes without any preparation. Moreover, patients can use the device to record long-term signals in loop and holter modes, using wet electrodes. In order to overcome the problem of signal quality fluctuation due to using different electrodes types and different placements on subject's chest, customized ECG signal processing and interpretation pipeline is presented for each working mode. We present the evaluation of the novel short-term recorder design. Recording of an ECG signal was performed for 391 patients using a standard 12-leads golden standard ECG and the proposed patient-activated short-term post-event recorder. In the validation phase, a sample of validation signals followed peer review process wherein two experts annotated the signals in terms of signal acceptability for diagnosis.We found that 96% of signals allow detecting arrhythmia and other signal's abnormal changes. Additionally, we compared and presented the correlation coefficient and the automatic QRS delineation results of both short-term post-event recorder and 12-leads golden standard ECG recorder. The proposed multi-purpose ECG device allows physicians to choose the working mode of the same device according to the patient status. The proposed device was designed to allow patients to manage the technical requirements of both working modes. Post-event short-term ECG recording using the proposed design provide physicians reliable three ECG leads with direct symptom-rhythm correlation.

QRS detection based ECG quality assessment.

PubMed

Hayn, Dieter; Jammerbund, Bernhard; Schreier, Günter

2012-09-01

Although immediate feedback concerning ECG signal quality during recording is useful, up to now not much literature describing quality measures is available. We have implemented and evaluated four ECG quality measures. Empty lead criterion (A), spike detection criterion (B) and lead crossing point criterion (C) were calculated from basic signal properties. Measure D quantified the robustness of QRS detection when applied to the signal. An advanced Matlab-based algorithm combining all four measures and a simplified algorithm for Android platforms, excluding measure D, were developed. Both algorithms were evaluated by taking part in the Computing in Cardiology Challenge 2011. Each measure's accuracy and computing time was evaluated separately. During the challenge, the advanced algorithm correctly classified 93.3% of the ECGs in the training-set and 91.6 % in the test-set. Scores for the simplified algorithm were 0.834 in event 2 and 0.873 in event 3. Computing time for measure D was almost five times higher than for other measures. Required accuracy levels depend on the application and are related to computing time. While our simplified algorithm may be accurate for real-time feedback during ECG self-recordings, QRS detection based measures can further increase the performance if sufficient computing power is available.

Long-Term Continuous Ambulatory ECG Monitors and External Cardiac Loop Recorders for Cardiac Arrhythmia: A Health Technology Assessment

PubMed Central

Kabali, Conrad; Xie, Xuanqian; Higgins, Caroline

2017-01-01

Background Ambulatory electrocardiography (ECG) monitors are often used to detect cardiac arrhythmia. For patients with symptoms, an external cardiac loop recorder will often be recommended. The improved recording capacity of newer Holter monitors and similar devices, collectively known as longterm continuous ambulatory ECG monitors, suggests that they will perform just as well as, or better than, external loop recorders. This health technology assessment aimed to evaluate the effectiveness, cost-effectiveness, and budget impact of longterm continuous ECG monitors compared with external loop recorders in detecting symptoms of cardiac arrhythmia. Methods Based on our systematic search for studies published up to January 15, 2016, we did not identify any studies directly comparing the clinical effectiveness of longterm continuous ECG monitors and external loop recorders. Therefore, we conducted an indirect comparison, using a 24-hour Holter monitor as a common comparator. We used a meta-regression model to control for bias due to variation in device-wearing time and baseline syncope rate across studies. We conducted a similar systematic search for cost-utility and cost-effectiveness studies comparing the two types of devices; none were found. Finally, we used historical claims data (2006–2014) to estimate the future 5-year budget impact in Ontario, Canada, of continued public funding for both types of longterm ambulatory ECG monitors. Results Our clinical literature search yielded 7,815 non-duplicate citations, of which 12 cohort studies were eligible for indirect comparison. Seven studies assessed the effectiveness of longterm continuous monitors and five assessed external loop recorders. Both types of devices were more effective than a 24-hour Holter monitor, and we found no substantial difference between them in their ability to detect symptoms (risk difference 0.01; 95% confidence interval −0.18, 0.20). Using GRADE for network meta-analysis, we evaluated the quality of the evidence as low. Our budget impact analysis showed that use of the longterm continuous monitors has grown steadily in Ontario since they became publicly funded in 2006, particularly since 2011 when monitors that can record for 14 days or longer became funded, and the use of external cardiac loop recorders has correspondingly declined. The analysis suggests that, with these trends, continued public funding of both types of longterm ambulatory ECG testing will result in additional costs ranging from $130,000 to $370,000 per year over the next 5 years. Conclusions Although both longterm continuous ambulatory ECG monitors and external cardiac loop recorders were more effective than a 24-hour Holter monitor in detecting symptoms of cardiac arrhythmia, we found no evidence to suggest that these two devices differ in effectiveness. Assuming that the use of longterm continuous monitors will continue to increase in the next 5 years, the public health care system in Ontario can expect to see added costs of $130,000 to $370,000 per year. PMID:28194254

A miniature on-chip multi-functional ECG signal processor with 30 µW ultra-low power consumption.

PubMed

Liu, Xin; Zheng, Yuan Jin; Phyu, Myint Wai; Zhao, Bin; Je, Minkyu; Yuan, Xiao Jun

2010-01-01

In this paper, a miniature low-power Electrocardiogram (ECG) signal processing application specific integrated circuit (ASIC) chip is proposed. This chip provides multiple critical functions for ECG analysis using a systematic wavelet transform algorithm and a novel SRAM-based ASIC architecture, while achieves low cost and high performance. Using 0.18 µm CMOS technology and 1 V power supply, this ASIC chip consumes only 29 µW and occupies an area of 3 mm(2). This on-chip ECG processor is highly suitable for reliable real-time cardiac status monitoring applications.

Fetal ECG extraction using independent component analysis by Jade approach

NASA Astrophysics Data System (ADS)

Giraldo-Guzmán, Jader; Contreras-Ortiz, Sonia H.; Lasprilla, Gloria Isabel Bautista; Kotas, Marian

2017-11-01

Fetal ECG monitoring is a useful method to assess the fetus health and detect abnormal conditions. In this paper we propose an approach to extract fetal ECG from abdomen and chest signals using independent component analysis based on the joint approximate diagonalization of eigenmatrices approach. The JADE approach avoids redundancy, what reduces matrix dimension and computational costs. Signals were filtered with a high pass filter to eliminate low frequency noise. Several levels of decomposition were tested until the fetal ECG was recognized in one of the separated sources output. The proposed method shows fast and good performance.

ANMCO/AIIC/SIT Consensus Information Document: definition, precision, and suitability of electrocardiographic signals of electrocardiographs, ergometry, Holter electrocardiogram, telemetry, and bedside monitoring systems.

PubMed

Gulizia, Michele Massimo; Casolo, Giancarlo; Zuin, Guerrino; Morichelli, Loredana; Calcagnini, Giovanni; Ventimiglia, Vincenzo; Censi, Federica; Caldarola, Pasquale; Russo, Giancarmine; Leogrande, Lorenzo; Franco Gensini, Gian

2017-05-01

The electrocardiogram (ECG) signal can be derived from different sources. These include systems for surface ECG, Holter monitoring, ergometric stress tests, and telemetry systems and bedside monitoring of vital parameters, which are useful for rhythm and ST-segment analysis and ECG screening of electrical sudden cardiac death predictors. A precise ECG diagnosis is based upon correct recording, elaboration, and presentation of the signal. Several sources of artefacts and potential external causes may influence the quality of the original ECG waveforms. Other factors that may affect the quality of the information presented depend upon the technical solutions employed to improve the signal. The choice of the instrumentations and solutions used to offer a high-quality ECG signal are, therefore, of paramount importance. Some requirements are reported in detail in scientific statements and recommendations. The aim of this consensus document is to give scientific reference for the choice of systems able to offer high quality ECG signal acquisition, processing, and presentation suitable for clinical use.

ANMCO/AIIC/SIT Consensus Information Document: definition, precision, and suitability of electrocardiographic signals of electrocardiographs, ergometry, Holter electrocardiogram, telemetry, and bedside monitoring systems

PubMed Central

Casolo, Giancarlo; Zuin, Guerrino; Morichelli, Loredana; Calcagnini, Giovanni; Ventimiglia, Vincenzo; Censi, Federica; Caldarola, Pasquale; Russo, Giancarmine; Leogrande, Lorenzo; Franco Gensini, Gian

2017-01-01

Abstract The electrocardiogram (ECG) signal can be derived from different sources. These include systems for surface ECG, Holter monitoring, ergometric stress tests, and telemetry systems and bedside monitoring of vital parameters, which are useful for rhythm and ST-segment analysis and ECG screening of electrical sudden cardiac death predictors. A precise ECG diagnosis is based upon correct recording, elaboration, and presentation of the signal. Several sources of artefacts and potential external causes may influence the quality of the original ECG waveforms. Other factors that may affect the quality of the information presented depend upon the technical solutions employed to improve the signal. The choice of the instrumentations and solutions used to offer a high-quality ECG signal are, therefore, of paramount importance. Some requirements are reported in detail in scientific statements and recommendations. The aim of this consensus document is to give scientific reference for the choice of systems able to offer high quality ECG signal acquisition, processing, and presentation suitable for clinical use. PMID:28751842

Textile Concentric Ring Electrodes for ECG Recording Based on Screen-Printing Technology

PubMed Central

Ye-Lin, Yiyao; Garcia-Casado, Javier

2018-01-01

Among many of the electrode designs used in electrocardiography (ECG), concentric ring electrodes (CREs) are one of the most promising due to their enhanced spatial resolution. Their development has undergone a great push due to their use in recent years; however, they are not yet widely used in clinical practice. CRE implementation in textiles will lead to a low cost, flexible, comfortable, and robust electrode capable of detecting high spatial resolution ECG signals. A textile CRE set has been designed and developed using screen-printing technology. This is a mature technology in the textile industry and, therefore, does not require heavy investments. Inks employed as conductive elements have been silver and a conducting polymer (poly (3,4-ethylenedioxythiophene) polystyrene sulfonate; PEDOT:PSS). Conducting polymers have biocompatibility advantages, they can be used with flexible substrates, and they are available for several printing technologies. CREs implemented with both inks have been compared by analyzing their electric features and their performance in detecting ECG signals. The results reveal that silver CREs present a higher average thickness and slightly lower skin-electrode impedance than PEDOT:PSS CREs. As for ECG recordings with subjects at rest, both CREs allowed the uptake of bipolar concentric ECG signals (BC-ECG) with signal-to-noise ratios similar to that of conventional ECG recordings. Regarding the saturation and alterations of ECGs captured with textile CREs caused by intentional subject movements, silver CREs presented a more stable response (fewer saturations and alterations) than those of PEDOT:PSS. Moreover, BC-ECG signals provided higher spatial resolution compared to conventional ECG. This improved spatial resolution was manifested in the identification of P1 and P2 waves of atrial activity in most of the BC-ECG signals. It can be concluded that textile silver CREs are more suitable than those of PEDOT:PSS for obtaining BC-ECG records. These developed textile electrodes bring the use of CREs closer to the clinical environment. PMID:29361722

Textile Concentric Ring Electrodes for ECG Recording Based on Screen-Printing Technology.

PubMed

Lidón-Roger, José Vicente; Prats-Boluda, Gema; Ye-Lin, Yiyao; Garcia-Casado, Javier; Garcia-Breijo, Eduardo

2018-01-21

Among many of the electrode designs used in electrocardiography (ECG), concentric ring electrodes (CREs) are one of the most promising due to their enhanced spatial resolution. Their development has undergone a great push due to their use in recent years; however, they are not yet widely used in clinical practice. CRE implementation in textiles will lead to a low cost, flexible, comfortable, and robust electrode capable of detecting high spatial resolution ECG signals. A textile CRE set has been designed and developed using screen-printing technology. This is a mature technology in the textile industry and, therefore, does not require heavy investments. Inks employed as conductive elements have been silver and a conducting polymer (poly (3,4-ethylenedioxythiophene) polystyrene sulfonate; PEDOT:PSS). Conducting polymers have biocompatibility advantages, they can be used with flexible substrates, and they are available for several printing technologies. CREs implemented with both inks have been compared by analyzing their electric features and their performance in detecting ECG signals. The results reveal that silver CREs present a higher average thickness and slightly lower skin-electrode impedance than PEDOT:PSS CREs. As for ECG recordings with subjects at rest, both CREs allowed the uptake of bipolar concentric ECG signals (BC-ECG) with signal-to-noise ratios similar to that of conventional ECG recordings. Regarding the saturation and alterations of ECGs captured with textile CREs caused by intentional subject movements, silver CREs presented a more stable response (fewer saturations and alterations) than those of PEDOT:PSS. Moreover, BC-ECG signals provided higher spatial resolution compared to conventional ECG. This improved spatial resolution was manifested in the identification of P1 and P2 waves of atrial activity in most of the BC-ECG signals. It can be concluded that textile silver CREs are more suitable than those of PEDOT:PSS for obtaining BC-ECG records. These developed textile electrodes bring the use of CREs closer to the clinical environment.

Insulated ECG electrodes

NASA Technical Reports Server (NTRS)

Portnoy, W. M.; David, R. M.

1973-01-01

Insulated, capacitively coupled electrode does not require electrolyte paste for attachment. Other features of electrode include wide range of nontoxic material that may be employed for dielectric because of sputtering technique used. Also, electrode size is reduced because there is no need for external compensating networks with FET operational amplifier.

Biometric and Emotion Identification: An ECG Compression Based Method.

PubMed

Brás, Susana; Ferreira, Jacqueline H T; Soares, Sandra C; Pinho, Armando J

2018-01-01

We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model.

Biometric and Emotion Identification: An ECG Compression Based Method

PubMed Central

Brás, Susana; Ferreira, Jacqueline H. T.; Soares, Sandra C.; Pinho, Armando J.

2018-01-01

We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model. PMID:29670564

A real time ECG signal processing application for arrhythmia detection on portable devices

NASA Astrophysics Data System (ADS)

Georganis, A.; Doulgeraki, N.; Asvestas, P.

2017-11-01

Arrhythmia describes the disorders of normal heart rate, which, depending on the case, can even be fatal for a patient with severe history of heart disease. The purpose of this work is to develop an application for heart signal visualization, processing and analysis in Android portable devices e.g. Mobile phones, tablets, etc. The application is able to retrieve the signal initially from a file and at a later stage this signal is processed and analysed within the device so that it can be classified according to the features of the arrhythmia. In the processing and analysing stage, different algorithms are included among them the Moving Average and Pan Tompkins algorithm as well as the use of wavelets, in order to extract features and characteristics. At the final stage, testing is performed by simulating our application in real-time records, using the TCP network protocol for communicating the mobile with a simulated signal source. The classification of ECG beat to be processed is performed by neural networks.

Premature ventricular contraction detection combining deep neural networks and rules inference.

PubMed

Zhou, Fei-Yan; Jin, Lin-Peng; Dong, Jun

2017-06-01

Premature ventricular contraction (PVC), which is a common form of cardiac arrhythmia caused by ectopic heartbeat, can lead to life-threatening cardiac conditions. Computer-aided PVC detection is of considerable importance in medical centers or outpatient ECG rooms. In this paper, we proposed a new approach that combined deep neural networks and rules inference for PVC detection. The detection performance and generalization were studied using publicly available databases: the MIT-BIH arrhythmia database (MIT-BIH-AR) and the Chinese Cardiovascular Disease Database (CCDD). The PVC detection accuracy on the MIT-BIH-AR database was 99.41%, with a sensitivity and specificity of 97.59% and 99.54%, respectively, which were better than the results from other existing methods. To test the generalization capability, the detection performance was also evaluated on the CCDD. The effectiveness of the proposed method was confirmed by the accuracy (98.03%), sensitivity (96.42%) and specificity (98.06%) with the dataset over 140,000 ECG recordings of the CCDD. Copyright © 2017 Elsevier B.V. All rights reserved.

[Study for portable dynamic ECG monitor and recorder].

PubMed

Yang, Pengcheng; Li, Yongqin; Chen, Bihua

2012-09-01

This Paper presents a portable dynamic ECG monitor system based on MSP430F149 microcontroller. The electrocardiogram detecting system consists of ECG detecting circuit, man-machine interaction module, MSP430F149 and upper computer software. The ECG detecting circuit including a preamplifier, second-order Butterworth low-pass filter, high-pass filter, and 50Hz trap circuit to detects electrocardiogram and depresses various kinds of interference effectively. A microcontroller is used to collect three channel analog signals which can be displayed on TFT LCD. A SD card is used to record real-time data continuously and implement the FTA16 file system. In the end, a host computer system interface is also designed to analyze the ECG signal and the analysis results can provide diagnosis references to clinical doctors.

Fetal ECG extraction from abdominal signals: a review on suppression of fundamental power line interference component and its harmonics.

PubMed

Ţarălungă, Dragoş-Daniel; Ungureanu, Georgeta-Mihaela; Gussi, Ilinca; Strungaru, Rodica; Wolf, Werner

2014-01-01

Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices.

Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

PubMed Central

Ţarălungă, Dragoş-Daniel; Ungureanu, Georgeta-Mihaela; Gussi, Ilinca; Strungaru, Rodica; Wolf, Werner

2014-01-01

Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices. PMID:24660020

Quantitative Assessment of Heart Rate Dynamics during Meditation: An ECG Based Study with Multi-Fractality and Visibility Graph

PubMed Central

Bhaduri, Anirban; Ghosh, Dipak

2016-01-01

The cardiac dynamics during meditation is explored quantitatively with two chaos-based non-linear techniques viz. multi-fractal detrended fluctuation analysis and visibility network analysis techniques. The data used are the instantaneous heart rate (in beats/minute) of subjects performing Kundalini Yoga and Chi meditation from PhysioNet. The results show consistent differences between the quantitative parameters obtained by both the analysis techniques. This indicates an interesting phenomenon of change in the complexity of the cardiac dynamics during meditation supported with quantitative parameters. The results also produce a preliminary evidence that these techniques can be used as a measure of physiological impact on subjects performing meditation. PMID:26909045

Quantitative Assessment of Heart Rate Dynamics during Meditation: An ECG Based Study with Multi-Fractality and Visibility Graph.

PubMed

Bhaduri, Anirban; Ghosh, Dipak

2016-01-01

The cardiac dynamics during meditation is explored quantitatively with two chaos-based non-linear techniques viz. multi-fractal detrended fluctuation analysis and visibility network analysis techniques. The data used are the instantaneous heart rate (in beats/minute) of subjects performing Kundalini Yoga and Chi meditation from PhysioNet. The results show consistent differences between the quantitative parameters obtained by both the analysis techniques. This indicates an interesting phenomenon of change in the complexity of the cardiac dynamics during meditation supported with quantitative parameters. The results also produce a preliminary evidence that these techniques can be used as a measure of physiological impact on subjects performing meditation.

Tansig activation function (of MLP network) for cardiac abnormality detection

NASA Astrophysics Data System (ADS)

Adnan, Ja'afar; Daud, Nik Ghazali Nik; Ishak, Mohd Taufiq; Rizman, Zairi Ismael; Rahman, Muhammad Izzuddin Abd

2018-02-01

Heart abnormality often occurs regardless of gender, age and races. This problem sometimes does not show any symptoms and it can cause a sudden death to the patient. In general, heart abnormality is the irregular electrical activity of the heart. This paper attempts to develop a program that can detect heart abnormality activity through implementation of Multilayer Perceptron (MLP) network. A certain amount of data of the heartbeat signals from the electrocardiogram (ECG) will be used in this project to train the MLP network by using several training algorithms with Tansig activation function.

Microprocessor-based cardiotachometer

NASA Technical Reports Server (NTRS)

Crosier, W. G.; Donaldson, J. A.

1981-01-01

Instrument operates reliably even with stress-test electrocardiogram (ECG) signals subject to noise, baseline wandering, and amplitude change. It records heart rate from preamplified, single-lead ECG input signal and produces digital and analog heart-rate outputs which are fed elsewhere. Analog hardware processes ECG input signal, producing 10-ms pulse for each heartbeat. Microprocessor analyzes resulting pulse train, identifying irregular heartbeats and maintaining stable output during lead switching. Easily modified computer program provides analysis.

A new statistical PCA-ICA algorithm for location of R-peaks in ECG.

PubMed

Chawla, M P S; Verma, H K; Kumar, Vinod

2008-09-16

The success of ICA to separate the independent components from the mixture depends on the properties of the electrocardiogram (ECG) recordings. This paper discusses some of the conditions of independent component analysis (ICA) that could affect the reliability of the separation and evaluation of issues related to the properties of the signals and number of sources. Principal component analysis (PCA) scatter plots are plotted to indicate the diagnostic features in the presence and absence of base-line wander in interpreting the ECG signals. In this analysis, a newly developed statistical algorithm by authors, based on the use of combined PCA-ICA for two correlated channels of 12-channel ECG data is proposed. ICA technique has been successfully implemented in identifying and removal of noise and artifacts from ECG signals. Cleaned ECG signals are obtained using statistical measures like kurtosis and variance of variance after ICA processing. This analysis also paper deals with the detection of QRS complexes in electrocardiograms using combined PCA-ICA algorithm. The efficacy of the combined PCA-ICA algorithm lies in the fact that the location of the R-peaks is bounded from above and below by the location of the cross-over points, hence none of the peaks are ignored or missed.

Usefulness of Maintaining a Normal Electrocardiogram Over Time for Predicting Cardiovascular Health.

PubMed

Soliman, Elsayed Z; Zhang, Zhu-Ming; Chen, Lin Y; Tereshchenko, Larisa G; Arking, Dan; Alonso, Alvaro

2017-01-15

We hypothesized that maintaining a normal electrocardiogram (ECG) status over time is associated with low cardiovascular (CV) disease in a dose-response fashion and subsequently could be used to monitor programs aimed at promoting CV health. This analysis included 4,856 CV disease-free participants from the Atherosclerosis Risk in Communities study who had a normal ECG at baseline (1987 to 1989) and complete electrocardiographic data in subsequent 3 visits (1990 to 1992, 1993 to 1995, and 1996 to 1998). Participants were classified based on maintaining their normal ECG status during these 4 visits into "maintained," "not maintained," or "inconsistent" normal ECG status as defined by the Minnesota ECG classification. CV disease events (coronary heart disease, heart failure, and stroke) were adjudicated from Atherosclerosis Risk in Communities visit-4 through 2010. Over a median follow-up of 13.2 years, 885 CV disease events occurred. The incidence rate of CV disease events was lowest among study participants who maintained a normal ECG status, followed by those with an inconsistent pattern, and then those who did not maintain their normal ECG status (trend p value <0.001). Similarly, the greater the number of visits with a normal ECG status, the lower was the incidence rate of CV disease events (trend p value <0.001). Maintaining (vs not maintaining) a normal ECG status was associated with a lower risk of CV disease, which was lower than that observed in those with inconsistent normal ECG pattern (trend p value <0.01). In conclusion, maintaining a normal ECG status over time is associated with low risk of CV disease in a dose-response fashion, suggesting its potential use as a monitoring tool for programs promoting CV health. Copyright © 2016 Elsevier Inc. All rights reserved.

An ultra low power ECG signal processor design for cardiovascular disease detection.

PubMed

Jain, Sanjeev Kumar; Bhaumik, Basabi

2015-08-01

This paper presents an ultra low power ASIC design based on a new cardiovascular disease diagnostic algorithm. This new algorithm based on forward search is designed for real time ECG signal processing. The algorithm is evaluated for Physionet PTB database from the point of view of cardiovascular disease diagnosis. The failed detection rate of QRS complex peak detection of our algorithm ranges from 0.07% to 0.26% for multi lead ECG signal. The ASIC is designed using 130-nm CMOS low leakage process technology. The area of ASIC is 1.21 mm(2). This ASIC consumes only 96 nW at an operating frequency of 1 kHz with a supply voltage of 0.9 V. Due to ultra low power consumption, our proposed ASIC design is most suitable for energy efficient wearable ECG monitoring devices.

A PC-based generator of surface ECG potentials for computer electrocardiograph testing.

PubMed

Franchi, D; Palagi, G; Bedini, R

1994-02-01

The system is composed of an electronic circuit, connected to a PC, whose outputs, starting from ECGs digitally collected by commercial interpretative electrocardiographs, simulate virtual patients' limb and chest electrode potentials. Appropriate software manages the D/A conversion and lines up the original short-term signal in a ring buffer to generate continuous ECG traces. The device also permits the addition of artifacts and/or baseline wanders/shifts on each lead separately. The system has been accurately tested and statistical indexes have been computed to quantify the reproduction accuracy analyzing, in the generated signal, both the errors induced on the fiducial point measurements and the capability to retain the diagnostic significance. The device integrated with an annotated ECG data base constitutes a reliable and powerful system to be used in the quality assurance testing of computer electrocardiographs.

Evaluation of ECG-gated [(11)C]acetate PET for measuring left ventricular volumes, mass, and myocardial external efficiency.

PubMed

Hansson, Nils Henrik; Tolbod, Lars; Harms, Johannes; Wiggers, Henrik; Kim, Won Yong; Hansen, Esben; Zaremba, Tomas; Frøkiær, Jørgen; Jakobsen, Steen; Sørensen, Jens

2016-08-01

Noninvasive estimation of myocardial external efficiency (MEE) requires measurements of left ventricular (LV) oxygen consumption with [(11)C]acetate PET in addition to LV stroke volume and mass with cardiovascular magnetic resonance (CMR). Measuring LV geometry directly from ECG-gated [(11)C]acetate PET might enable MEE evaluation from a single PET scan. Therefore, we sought to establish the accuracy of measuring LV volumes, mass, and MEE directly from ECG-gated [(11)C]acetate PET. Thirty-five subjects with aortic valve stenosis underwent ECG-gated [(11)C]acetate PET and CMR. List mode PET data were rebinned into 16-bin ECG-gated uptake images before measuring LV volumes and mass using commercial software and compared to CMR. Dynamic datasets were used for calculation of mean LV oxygen consumption and MEE. LV mass, volumes, and ejection fraction measured by CMR and PET correlated strongly (r = 0.86-0.92, P < .001 for all), but were underestimated by PET (P < .001 for all except ESV P = .79). PET-based MEE, corrected for bias, correlated fairly with PET/CMR-based MEE (r = 0.60, P < .001, bias -3 ± 21%, P = .56). PET-based MEE bias was strongly associated with LV wall thickness. Although analysis-related improvements in accuracy are recommended, LV geometry estimated from ECG-gated [(11)C]acetate PET correlate excellently with CMR and can indeed be used to evaluate MEE.

Fast multi-scale feature fusion for ECG heartbeat classification

NASA Astrophysics Data System (ADS)

Ai, Danni; Yang, Jian; Wang, Zeyu; Fan, Jingfan; Ai, Changbin; Wang, Yongtian

2015-12-01

Electrocardiogram (ECG) is conducted to monitor the electrical activity of the heart by presenting small amplitude and duration signals; as a result, hidden information present in ECG data is difficult to determine. However, this concealed information can be used to detect abnormalities. In our study, a fast feature-fusion method of ECG heartbeat classification based on multi-linear subspace learning is proposed. The method consists of four stages. First, baseline and high frequencies are removed to segment heartbeat. Second, as an extension of wavelets, wavelet-packet decomposition is conducted to extract features. With wavelet-packet decomposition, good time and frequency resolutions can be provided simultaneously. Third, decomposed confidences are arranged as a two-way tensor, in which feature fusion is directly implemented with generalized N dimensional ICA (GND-ICA). In this method, co-relationship among different data information is considered, and disadvantages of dimensionality are prevented; this method can also be used to reduce computing compared with linear subspace-learning methods (PCA). Finally, support vector machine (SVM) is considered as a classifier in heartbeat classification. In this study, ECG records are obtained from the MIT-BIT arrhythmia database. Four main heartbeat classes are used to examine the proposed algorithm. Based on the results of five measurements, sensitivity, positive predictivity, accuracy, average accuracy, and t-test, our conclusion is that a GND-ICA-based strategy can be used to provide enhanced ECG heartbeat classification. Furthermore, large redundant features are eliminated, and classification time is reduced.

A wavelet-based ECG delineation algorithm for 32-bit integer online processing

PubMed Central

2011-01-01

Background Since the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li et al. in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as root mean square (RMS) or floating point algebra, which are computationally demanding. Methods This paper presents a 32-bit integer, linear algebra advanced approach to online QRS detection and P-QRS-T waves delineation of a single lead ECG signal, based on WT. Results The QRS detector performance was validated on the MIT-BIH Arrhythmia Database (sensitivity Se = 99.77%, positive predictive value P+ = 99.86%, on 109010 annotated beats) and on the European ST-T Database (Se = 99.81%, P+ = 99.56%, on 788050 annotated beats). The ECG delineator was validated on the QT Database, showing a mean error between manual and automatic annotation below 1.5 samples for all fiducial points: P-onset, P-peak, P-offset, QRS-onset, QRS-offset, T-peak, T-offset, and a mean standard deviation comparable to other established methods. Conclusions The proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, in spite of a simple structure built on integer linear algebra. PMID:21457580

A wavelet-based ECG delineation algorithm for 32-bit integer online processing.

PubMed

Di Marco, Luigi Y; Chiari, Lorenzo

2011-04-03

Since the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li et al. in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as root mean square (RMS) or floating point algebra, which are computationally demanding. This paper presents a 32-bit integer, linear algebra advanced approach to online QRS detection and P-QRS-T waves delineation of a single lead ECG signal, based on WT. The QRS detector performance was validated on the MIT-BIH Arrhythmia Database (sensitivity Se = 99.77%, positive predictive value P+ = 99.86%, on 109010 annotated beats) and on the European ST-T Database (Se = 99.81%, P+ = 99.56%, on 788050 annotated beats). The ECG delineator was validated on the QT Database, showing a mean error between manual and automatic annotation below 1.5 samples for all fiducial points: P-onset, P-peak, P-offset, QRS-onset, QRS-offset, T-peak, T-offset, and a mean standard deviation comparable to other established methods. The proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, in spite of a simple structure built on integer linear algebra.

Wavelet-based Encoding Scheme for Controlling Size of Compressed ECG Segments in Telecardiology Systems.

PubMed

Al-Busaidi, Asiya M; Khriji, Lazhar; Touati, Farid; Rasid, Mohd Fadlee; Mnaouer, Adel Ben

2017-09-12

One of the major issues in time-critical medical applications using wireless technology is the size of the payload packet, which is generally designed to be very small to improve the transmission process. Using small packets to transmit continuous ECG data is still costly. Thus, data compression is commonly used to reduce the huge amount of ECG data transmitted through telecardiology devices. In this paper, a new ECG compression scheme is introduced to ensure that the compressed ECG segments fit into the available limited payload packets, while maintaining a fixed CR to preserve the diagnostic information. The scheme automatically divides the ECG block into segments, while maintaining other compression parameters fixed. This scheme adopts discrete wavelet transform (DWT) method to decompose the ECG data, bit-field preserving (BFP) method to preserve the quality of the DWT coefficients, and a modified running-length encoding (RLE) scheme to encode the coefficients. The proposed dynamic compression scheme showed promising results with a percentage packet reduction (PR) of about 85.39% at low percentage root-mean square difference (PRD) values, less than 1%. ECG records from MIT-BIH Arrhythmia Database were used to test the proposed method. The simulation results showed promising performance that satisfies the needs of portable telecardiology systems, like the limited payload size and low power consumption.

Multiadaptive Bionic Wavelet Transform: Application to ECG Denoising and Baseline Wandering Reduction

NASA Astrophysics Data System (ADS)

Sayadi, Omid; Shamsollahi, Mohammad B.

2007-12-01

We present a new modified wavelet transform, called the multiadaptive bionic wavelet transform (MABWT), that can be applied to ECG signals in order to remove noise from them under a wide range of variations for noise. By using the definition of bionic wavelet transform and adaptively determining both the center frequency of each scale together with the[InlineEquation not available: see fulltext.]-function, the problem of desired signal decomposition is solved. Applying a new proposed thresholding rule works successfully in denoising the ECG. Moreover by using the multiadaptation scheme, lowpass noisy interference effects on the baseline of ECG will be removed as a direct task. The method was extensively clinically tested with real and simulated ECG signals which showed high performance of noise reduction, comparable to those of wavelet transform (WT). Quantitative evaluation of the proposed algorithm shows that the average SNR improvement of MABWT is 1.82 dB more than the WT-based results, for the best case. Also the procedure has largely proved advantageous over wavelet-based methods for baseline wandering cancellation, including both DC components and baseline drifts.

ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition.

PubMed

Sharma, Hemant; Sharma, K K

2018-06-01

Monitoring of the respiration using the electrocardiogram (ECG) is desirable for the simultaneous study of cardiac activities and the respiration in the aspects of comfort, mobility, and cost of the healthcare system. This paper proposes a new approach for deriving the respiration from single-lead ECG based on the iterated Hilbert transform (IHT) and the Hilbert vibration decomposition (HVD). The ECG signal is first decomposed into the multicomponent sinusoidal signals using the IHT technique. Afterward, the lower order amplitude components obtained from the IHT are filtered using the HVD to extract the respiration information. Experiments are performed on the Fantasia and Apnea-ECG datasets. The performance of the proposed ECG-derived respiration (EDR) approach is compared with the existing techniques including the principal component analysis (PCA), R-peak amplitudes (RPA), respiratory sinus arrhythmia (RSA), slopes of the QRS complex, and R-wave angle. The proposed technique showed the higher median values of correlation (first and third quartile) for both the Fantasia and Apnea-ECG datasets as 0.699 (0.55, 0.82) and 0.57 (0.40, 0.73), respectively. Also, the proposed algorithm provided the lowest values of the mean absolute error and the average percentage error computed from the EDR and reference (recorded) respiration signals for both the Fantasia and Apnea-ECG datasets as 1.27 and 9.3%, and 1.35 and 10.2%, respectively. In the experiments performed over different age group subjects of the Fantasia dataset, the proposed algorithm provided effective results in the younger population but outperformed the existing techniques in the case of elderly subjects. The proposed EDR technique has the advantages over existing techniques in terms of the better agreement in the respiratory rates and specifically, it reduces the need for an extra step required for the detection of fiducial points in the ECG for the estimation of respiration which makes the process effective and less-complex. The above performance results obtained from two different datasets validate that the proposed approach can be used for monitoring of the respiration using single-lead ECG.

ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform.

PubMed

El B'charri, Oussama; Latif, Rachid; Elmansouri, Khalifa; Abenaou, Abdenbi; Jenkal, Wissam

2017-02-07

Since the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician's correct decision on patients. The dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet. In this work, we shall provide a comprehensive study on the impact of the choice of threshold algorithm, threshold value, and the appropriate wavelet decomposition level to evaluate the ECG signal de-noising performance. A set of simulations is performed on both synthetic and real ECG signals to achieve the promised results. First, the synthetic ECG signal is used to observe the algorithm response. The evaluation results of synthetic ECG signal corrupted by various types of noise has showed that the modified unified threshold and wavelet hyperbolic threshold de-noising method is better in realistic and colored noises. The tuned threshold is then used on real ECG signals from the MIT-BIH database. The results has shown that the proposed method achieves higher performance than the ordinary dual tree wavelet transform into all kinds of noise removal from ECG signal. The simulation results indicate that the algorithm is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal. Moreover, the algorithm is quite simple and can be used in real time ECG monitoring.

PIC microcontroller-based RF wireless ECG monitoring system.

PubMed

Oweis, R J; Barhoum, A

2007-01-01

This paper presents a radio-telemetry system that provides the possibility of ECG signal transmission from a patient detection circuit via an RF data link. A PC then receives the signal through the National Instrument data acquisition card (NIDAQ). The PC is equipped with software allowing the received ECG signals to be saved, analysed, and sent by email to another part of the world. The proposed telemetry system consists of a patient unit and a PC unit. The amplified and filtered ECG signal is sampled 360 times per second, and the A/D conversion is performed by a PIC16f877 microcontroller. The major contribution of the final proposed system is that it detects, processes and sends patients ECG data over a wireless RF link to a maximum distance of 200 m. Transmitted ECG data with different numbers of samples were received, decoded by means of another PIC microcontroller, and displayed using MATLAB program. The designed software is presented in a graphical user interface utility.

A mobile phone-based ECG monitoring system.

PubMed

Iwamoto, Junichi; Yonezawa, Yoshiharu; Maki, Hiromichi; Ogawa, Hidekuni; Ninomiya, Ishio; Sada, Kouji; Hamada, Shingo; Hahn, Allen W; Caldwell, W Morton

2006-01-01

We have developed a telemedicine system for monitoring a patient's electrocardiogram during daily activities. The recording system consists of three ECG chest electrodes, a variable gain instrumentation amplifier, a low power 8-bit single-chip microcomputer, a 256 KB EEPROM and a 2.4 GHz low transmitting power mobile phone (PHS). The complete system is mounted on a single, lightweight, chest electrode array. When a heart discomfort is felt, the patient pushes the data transmission switch on the recording system. The system sends the recorded ECG waveforms of the two prior minutes and ECG waveforms of the two minutes after the switch is pressed, directly in the hospital server computer via the PHS. The server computer sends the data to the physician on call. The data is displayed on the doctor's Java mobile phone LCD (Liquid Crystal Display), so he or she can monitor the ECG regardless of their location. The developed ECG monitoring system is not only applicable to at-home patients, but should also be useful for monitoring hospital patients.

The evaluation of an open source online training system for teaching 12 lead electrocardiographic interpretation.

PubMed

Breen, Cathal; Zhu, Tingting; Bond, Raymond; Finlay, Dewar; Clifford, Gari

2016-01-01

The aim of this study is to present and evaluate the integration of a low resource JavaScript based ECG training interface (CrowdLabel) and a standardised curriculum for self-guided tuition in ECG interpretation. Participants practiced interpreting ECGs weekly using the CrowdLabel interface to assist with the learning of the traditional didactic taught course material during a 6 week training period. To determine competency students were tested during week 7. A total of 245 unique ECG cases were submitted by each student. Accuracy scores during the training period ranged from 0-59.5% (median = 33.3%). Conversely accuracy scores during the test ranged from 30 - 70% (median = 37.5%) (p < 0.05). There was no correlation between students who interpreted high numbers of ECGs during the training period and their marks obtained. CrowdLabel is shown to be a readily accessible dedicated learning platform to support ECG interpretation competency. Copyright © 2016 Elsevier Inc. All rights reserved.

Implementation of a wireless ECG acquisition SoC for IEEE 802.15.4 (ZigBee) applications.

PubMed

Wang, Liang-Hung; Chen, Tsung-Yen; Lin, Kuang-Hao; Fang, Qiang; Lee, Shuenn-Yuh

2015-01-01

This paper presents a wireless biosignal acquisition system-on-a-chip (WBSA-SoC) specialized for electrocardiogram (ECG) monitoring. The proposed system consists of three subsystems, namely, 1) the ECG acquisition node, 2) the protocol for standard IEEE 802.15.4 ZigBee system, and 3) the RF transmitter circuits. The ZigBee protocol is adopted for wireless communication to achieve high integration, applicability, and portability. A fully integrated CMOS RF front end containing a quadrature voltage-controlled oscillator and a 2.4-GHz low-IF (i.e., zero-IF) transmitter is employed to transmit ECG signals through wireless communication. The low-power WBSA-SoC is implemented by the TSMC 0.18-μm standard CMOS process. An ARM-based displayer with FPGA demodulation and an RF receiver with analog-to-digital mixed-mode circuits are constructed as verification platform to demonstrate the wireless ECG acquisition system. Measurement results on the human body show that the proposed SoC can effectively acquire ECG signals.

Adaptive Fourier decomposition based R-peak detection for noisy ECG Signals.

PubMed

Ze Wang; Chi Man Wong; Feng Wan

2017-07-01

An adaptive Fourier decomposition (AFD) based R-peak detection method is proposed for noisy ECG signals. Although lots of QRS detection methods have been proposed in literature, most detection methods require high signal quality. The proposed method extracts the R waves from the energy domain using the AFD and determines the R-peak locations based on the key decomposition parameters, achieving the denoising and the R-peak detection at the same time. Validated by clinical ECG signals in the MIT-BIH Arrhythmia Database, the proposed method shows better performance than the Pan-Tompkin (PT) algorithm in both situations of a native PT and the PT with a denoising process.

Arduino-based noise robust online heart-rate detection.

PubMed

Das, Sangita; Pal, Saurabh; Mitra, Madhuchhanda

2017-04-01

This paper introduces a noise robust real time heart rate detection system from electrocardiogram (ECG) data. An online data acquisition system is developed to collect ECG signals from human subjects. Heart rate is detected using window-based autocorrelation peak localisation technique. A low-cost Arduino UNO board is used to implement the complete automated process. The performance of the system is compared with PC-based heart rate detection technique. Accuracy of the system is validated through simulated noisy ECG data with various levels of signal to noise ratio (SNR). The mean percentage error of detected heart rate is found to be 0.72% for the noisy database with five different noise levels.

Efficient algorithm for baseline wander and powerline noise removal from ECG signals based on discrete Fourier series.

PubMed

Bahaz, Mohamed; Benzid, Redha

2018-03-01

Electrocardiogram (ECG) signals are often contaminated with artefacts and noises which can lead to incorrect diagnosis when they are visually inspected by cardiologists. In this paper, the well-known discrete Fourier series (DFS) is re-explored and an efficient DFS-based method is proposed to reduce contribution of both baseline wander (BW) and powerline interference (PLI) noises in ECG records. In the first step, the determination of the exact number of low frequency harmonics contributing in BW is achieved. Next, the baseline drift is estimated by the sum of all associated Fourier sinusoids components. Then, the baseline shift is discarded efficiently by a subtraction of its approximated version from the original biased ECG signal. Concerning the PLI, the subtraction of the contributing harmonics calculated in the same manner reduces efficiently such type of noise. In addition of visual quality results, the proposed algorithm shows superior performance in terms of higher signal-to-noise ratio and smaller mean square error when faced to the DCT-based algorithm.

A New Strategy for ECG Baseline Wander Elimination Using Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Shahbakhti, Mohammad; Bagheri, Hamed; Shekarchi, Babak; Mohammadi, Somayeh; Naji, Mohsen

2016-06-01

Electrocardiogram (ECG) signals might be affected by various artifacts and noises that have biological and external sources. Baseline wander (BW) is a low-frequency artifact that may be caused by breathing, body movements and loose sensor contact. In this paper, a novel method based on empirical mode decomposition (EMD) for removal of baseline noise from ECG is presented. When compared to other EMD-based methods, the novelty of this research is to reach the optimized number of decomposed levels for ECG BW de-noising using mean power frequency (MPF), while the reduction of processing time is considered. To evaluate the performance of the proposed method, a fifth-order Butterworth high pass filtering (BHPF) with cut-off frequency at 0.5Hz and wavelet approach are applied. Three performance indices, signal-to-noise ratio (SNR), mean square error (MSE) and correlation coefficient (CC), between pure and filtered signals have been utilized for qualification of presented techniques. Results suggest that the EMD-based method outperforms the other filtering method.

Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques.

PubMed

Sutha, P; Jayanthi, V E

2017-12-08

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.

Energy Efficient Probabilistic Broadcasting for Mobile Ad-Hoc Network

NASA Astrophysics Data System (ADS)

Kumar, Sumit; Mehfuz, Shabana

2017-06-01

In mobile ad-hoc network (MANETs) flooding method is used for broadcasting route request (RREQ) packet from one node to another node for route discovery. This is the simplest method of broadcasting of RREQ packets but it often results in broadcast storm problem, originating collisions and congestion of packets in the network. A probabilistic broadcasting is one of the widely used broadcasting scheme for route discovery in MANETs and provides solution for broadcasting storm problem. But it does not consider limited energy of the battery of the nodes. In this paper, a new energy efficient probabilistic broadcasting (EEPB) is proposed in which probability of broadcasting RREQs is calculated with respect to remaining energy of nodes. The analysis of simulation results clearly indicate that an EEPB route discovery scheme in ad-hoc on demand distance vector (AODV) can increase the network lifetime with a decrease in the average power consumption and RREQ packet overhead. It also decreases the number of dropped packets in the network, in comparison to other EEPB schemes like energy constraint gossip (ECG), energy aware gossip (EAG), energy based gossip (EBG) and network lifetime through energy efficient broadcast gossip (NEBG).

Anatomic distribution of culprit lesions in patients with non-ST-segment elevation myocardial infarction and normal ECG.

PubMed

Moustafa, Abdelmoniem; Abi-Saleh, Bernard; El-Baba, Mohammad; Hamoui, Omar; AlJaroudi, Wael

2016-02-01

In patients presenting with non-ST-elevation myocardial infarction (NSTEMI), left anterior descending (LAD) coronary artery and three-vessel disease are the most commonly encountered culprit lesions in the presence of ST depression, while one third of patients with left circumflex (LCX) artery related infarction have normal ECG. We sought to determine the predictors of presence of culprit lesion in NSTEMI patients based on ECG, echocardiographic, and clinical characteristics. Patients admitted to the coronary care unit with the diagnosis of NSTEMI between June 2012 and December 2013 were retrospectively identified. Admission ECG was interpreted by an electrophysiologist that was blinded to the result of the coronary angiogram. Patients were dichotomized into either normal or abnormal ECG group. The primary endpoint was presence of culprit lesion. Secondary endpoints included length of stay, re-hospitalization within 60 days, and in-hospital mortality. A total of 118 patients that were identified; 47 with normal and 71 with abnormal ECG. At least one culprit lesion was identified in 101 patients (86%), and significantly more among those with abnormal ECG (91.5% vs. 76.6%, P=0.041).The LAD was the most frequently detected culprit lesion in both groups. There was a higher incidence of two and three-vessel disease in the abnormal ECG group (P=0.041).On the other hand, there was a trend of higher LCX involvement (25% vs. 13.8%, P=0.18) and more normal coronary arteries in the normal ECG group (23.4% vs. 8.5%, P=0.041). On multivariate analysis, prior history of coronary artery disease (CAD) [odds ratio (OR) 6.4 (0.8-52)], male gender [OR 5.0 (1.5-17)], and abnormal admission ECG [OR 3.6 (1.12-12)], were independent predictors of a culprit lesion. There was no difference in secondary endpoints between those with normal and abnormal ECG. Among patients presenting with NSTEMI, prior history of CAD, male gender and abnormal admission ECG were independent predictors of a culprit lesion. An abnormal ECG was significantly associated with two and three-vessel disease, while normal ECG was more associated with LCX involvement or normal angiogram. Admission ECG did not impact secondary outcomes.

[The primary research and development of software oversampling mapping system for electrocardiogram].

PubMed

Zhou, Yu; Ren, Jie

2011-04-01

We put forward a new concept of software oversampling mapping system for electrocardiogram (ECG) to assist the research of the ECG inverse problem to improve the generality of mapping system and the quality of mapping signals. We then developed a conceptual system based on the traditional ECG detecting circuit, Labview and DAQ card produced by National Instruments, and at the same time combined the newly-developed oversampling method into the system. The results indicated that the system could map ECG signals accurately and the quality of the signals was good. The improvement of hardware and enhancement of software made the system suitable for mapping in different situations. So the primary development of the software for oversampling mapping system was successful and further research and development can make the system a powerful tool for researching ECG inverse problem.

Electrocardiographic left ventricular hypertrophy predicts recurrence of atrial arrhythmias after catheter ablation of paroxysmal atrial fibrillation.

PubMed

Li, Song-Nan; Wang, Lu; Dong, Jian-Zeng; Yu, Rong-Hui; Long, De-Yong; Tang, Ri-Bo; Sang, Cai-Hua; Jiang, Chen-Xi; Liu, Nian; Bai, Rong; Du, Xin; Ma, Chang-Sheng

2018-06-01

Left ventricular hypertrophy (LVH) is an independent predictor of new-onset atrial fibrillation. Whether LVH can predict the recurrence of arrhythmia after radiofrequency catheter ablation (RFCA) in patients with paroxysmal atrial fibrillation (PAF) remains unclear. PAF patients with baseline-electrocardiographic LVH has a higher recurrence rate after RFCA procedure compared with those without LVH. A total of 436 patients with PAF undergoing first RFCA were consecutively enrolled and clustered into 2 groups based on electrocardiogram (ECG) findings: non-ECG LVH (218 patients) and ECG LVH (218 patients). LVH was characterized by the Romhilt-Estes point score system; the score ≥5points were defined as LVH. At 42 months' (interquartile range, 18.0-60.0 months) follow-up after RFCA, 151 (69.3%) patients in the non-ECG LVH group and 108 (49.5%) patients in the ECG LVH group maintained sinus rhythm without using antiarrhythmic drugs (P < 0.001). Patients with ECG LVH tended to experience a much higher prevalence of stroke and recurrence of atrial arrhythmia episodes compared with those without ECG LVH (log-rank P < 0.001). Multivariate analysis found the presence of ECG LVH and left atrial diameter to be independent risk factors for recurrence after adjusting for confounding factors. The presence of ECG LVH was a strong and independent predictor of recurrence in patients with PAF following RFCA. © 2018 Wiley Periodicals, Inc.

Spatiotemporal Characteristics of QRS Complexes Enable the Diagnosis of Brugada Syndrome Regardless of the Appearance of a Type 1 ECG.

PubMed

Guillem, Maria S; Climent, Andreu M; Millet, José; Berne, Paola; Ramos, Rafael; Brugada, Josep; Brugada, Ramon

2016-05-01

The diagnosis of Brugada syndrome based on the ECG is hampered by the dynamic nature of its ECG manifestations. Brugada syndrome patients are only 25% likely to present a type 1 ECG. The objective of this study is to provide an ECG diagnostic criterion for Brugada syndrome patients that can be applied consistently even in the absence of a type 1 ECG. We recorded 67-lead body surface potential maps from 94 Brugada syndrome patients and 82 controls (including right bundle branch block patients and healthy individuals). The spatial propagation direction during the last r' wave and the slope at the end of the QRS complex were measured and compared between patients groups. Receiver-operating characteristic curves were constructed for half of the database to identify optimal cutoff values; sensitivity and specificity for these cutoff values were measured in the other half of the database. A spontaneous type 1 ECG was present in only 30% of BrS patients. An orientation in the sagittal plane < 101º during the last r' wave and a descending slope < 9.65 mV/s enables the diagnosis of the syndrome with a sensitivity of 69% and a specificity of 97% in non-type 1 Brugada syndrome patients. Spatiotemporal characteristics of surface ECG recordings can enable a robust identification of BrS even without the presence of a type 1 ECG. © 2016 Wiley Periodicals, Inc.

CAVIAR: a tool to improve serial analysis of the 12-lead electrocardiogram.

PubMed

Berg, J; Fayn, J; Edenbrandt, L; Lundh, B; Malmström, P; Rubel, P

1995-09-01

An important part of an electrocardiogram (ECG) interpretation is the comparison between the present ECG and earlier recordings. The purpose of the present study was to evaluate a combination of two computer-based methods, synthesized vectorcardiogram (VCG) and CAVIAR, in this comparison. The methods were applied to a group of 38 normal subjects and to a group of 36 patients treated with anthracyclines. A fraction of these patients are likely to develop cardiac injury during or after the treatment, since anthracyclines are known to cause heart failure and cardiomyopathy. Two ECGs were recorded on each patient, one before and one after the treatment. On each normal subject, two ECGs were recorded with an interval of 8-9 years. A synthesized VCG was calculated from each ECG and the two synthesized VCGs from each subject were analysed with the CAVIAR method. The CAVIAR analysis is a quantitative method and normal limits for four measurements were established using the normal group. Values above these limits were more frequent in the patient group than in the normal group. The conventional ECGs were also analysed visually by an experience ECG interpreter without knowledge of the result of the CAVIAR analysis. No significant serial changes were found in 10 of the patients with high CAVIAR values. Changes in the ECGs were found in two patients with normal CAVIAR values. In summary, synthesized VCG and CAVIAR could be used to highlight small serial changes that are difficult to find in a visual analysis of ECGs.

Detection of Electrocardiogram by Electrodes with Fabrics Using Capacitive Coupling

NASA Astrophysics Data System (ADS)

Ueno, Akinori; Furusawa, Yoichi; Hoshino, Hiroshi; Ishiyama, Yoji

This article reports on a novel technique for detecting electrocardiogram (ECG) at a condition where thin cloth is interpolated between sensing electrodes and the skin to which the electrodes are attached. The technique is based upon capacitive coupling composed of the electrode, the cloth and the skin, so that the electrode can lead alternating electrocardiographic current through capacitance of the coupling. The technique is also founded on impedance transforming circuit that has extremely high input impedance around 1000GΩ and low output impedance, so as to match high output impedance of the electrode to low input impedance required by subsequent circuitry. A pilot ECG measuring device was manufactured using the technique and experiments showed (1) ECG recordings using the device with silk of 240μm thickness or with cotton of 564μm thickness were quite similar to ECGs recorded from the skin using conventional system, (2) stable ECGs were observed with the silk below 600μm thickness or with the cotton below 1128μm thickness, (3) effects of long-term measurement and perspiration on ECG waveform were negligible. These results prove feasibility of the proposed technique for detecting ECG by electrodes with fabrics.

The design of the m-health service application using a Nintendo DS game console.

PubMed

Lee, Sangjoon; Kim, Jungkuk; Lee, Myoungho

2011-03-01

In this article, we developed an m-health monitoring system using a Nintendo DS game console to demonstrate its utility. The proposed system consists of a biosignal acquisition device, wireless sensor network, base-station for signal reception from the sensor network and signal conversion according to Internet protocol, personal computer display program, and the Nintendo DS game console. The system collects three-channel electrocardiogram (ECG) signals for cardiac abnormality detection and three-axis accelerometer signals for fall detection of a person. The collected signals are then transmitted to the base-station through the wireless sensor network, where they are transformed according to the transmission control protocol/Internet protocol (TCP/IP) and sent to the destination IP through Internet network. To test the developed system, the collected signals were displayed on a computer located in different building through wired Internet network and also simultaneously displayed on the Nintendo DS game console connected to Internet network wirelessly. The system was able to collect and transmit signals for more than 24 h without any interruptions or malfunctions, showing the possibility of integrating healthcare monitoring functions into a small handheld-type electronic device developed for different purposes without significant complications. It is expected that the system can be used in an ambulance, nursing home, or general hospital where efficient patient monitoring from long distance is necessary.

Does individual learning styles influence the choice to use a web-based ECG learning programme in a blended learning setting?

PubMed Central

2012-01-01

Background The compressed curriculum in modern knowledge-intensive medicine demands useful tools to achieve approved learning aims in a limited space of time. Web-based learning can be used in different ways to enhance learning. Little is however known regarding its optimal utilisation. Our aim was to investigate if the individual learning styles of medical students influence the choice to use a web-based ECG learning programme in a blended learning setting. Methods The programme, with three types of modules (learning content, self-assessment questions and interactive ECG interpretation training), was offered on a voluntary basis during a face to face ECG learning course for undergraduate medical students. The Index of Learning Styles (ILS) and a general questionnaire including questions about computer and Internet usage, preferred future speciality and prior experience of E-learning were used to explore different factors related to the choice of using the programme or not. Results 93 (76%) out of 123 students answered the ILS instrument and 91 the general questionnaire. 55 students (59%) were defined as users of the web-based ECG-interpretation programme. Cronbach's alpha was analysed with coefficients above 0.7 in all of the four dimensions of ILS. There were no significant differences with regard to learning styles, as assessed by ILS, between the user and non-user groups; Active/Reflective; Visual/Verbal; Sensing/Intuitive; and Sequential/Global (p = 0.56-0.96). Neither did gender, prior experience of E-learning or preference for future speciality differ between groups. Conclusion Among medical students, neither learning styles according to ILS, nor a number of other characteristics seem to influence the choice to use a web-based ECG programme. This finding was consistent also when the usage of the different modules in the programme were considered. Thus, the findings suggest that web-based learning may attract a broad variety of medical students. PMID:22248183

Does individual learning styles influence the choice to use a web-based ECG learning programme in a blended learning setting?

PubMed

Nilsson, Mikael; Östergren, Jan; Fors, Uno; Rickenlund, Anette; Jorfeldt, Lennart; Caidahl, Kenneth; Bolinder, Gunilla

2012-01-16

The compressed curriculum in modern knowledge-intensive medicine demands useful tools to achieve approved learning aims in a limited space of time. Web-based learning can be used in different ways to enhance learning. Little is however known regarding its optimal utilisation. Our aim was to investigate if the individual learning styles of medical students influence the choice to use a web-based ECG learning programme in a blended learning setting. The programme, with three types of modules (learning content, self-assessment questions and interactive ECG interpretation training), was offered on a voluntary basis during a face to face ECG learning course for undergraduate medical students. The Index of Learning Styles (ILS) and a general questionnaire including questions about computer and Internet usage, preferred future speciality and prior experience of E-learning were used to explore different factors related to the choice of using the programme or not. 93 (76%) out of 123 students answered the ILS instrument and 91 the general questionnaire. 55 students (59%) were defined as users of the web-based ECG-interpretation programme. Cronbach's alpha was analysed with coefficients above 0.7 in all of the four dimensions of ILS. There were no significant differences with regard to learning styles, as assessed by ILS, between the user and non-user groups; Active/Reflective; Visual/Verbal; Sensing/Intuitive; and Sequential/Global (p = 0.56-0.96). Neither did gender, prior experience of E-learning or preference for future speciality differ between groups. Among medical students, neither learning styles according to ILS, nor a number of other characteristics seem to influence the choice to use a web-based ECG programme. This finding was consistent also when the usage of the different modules in the programme were considered. Thus, the findings suggest that web-based learning may attract a broad variety of medical students.

Wireless Self-Acquistion of 12-Lead ECG via Android Smart Phone

NASA Technical Reports Server (NTRS)

Schlegel, Todd T.

2012-01-01

Researchers at NASA s Johnson Space Center and at Orbital Research, Inc. (a NASA SBIR grant recipient) have recently developed a dry-electrode harness that allows for self-acquisition of resting 12-lead ECGs by minimally trained laypersons. When used in conjunction with commercial wireless (e.g., Bluetooth(TM) or 802.11-enabled) 12-lead ECG devices and custom smart phone-based software, the collected 12-lead ECG data can also immediately be forwarded from any geographic location within cellular range to the user s physician(s) of choice. The system can also be used to immediately forward to central receiving stations 12-lead ECG data collected during space flight or during activities in any remote terrestrial location supported by an internet or cellular phone infrastructure. The main novel aspects of the system are first, the dry-electrode 12-lead ECG harness itself, and second, an accompanying Android(TM) smart phone-based wireless 12-lead ECG capability. The ECG harness nominally employs dry electrodes manufactured by Orbital Research, Inc, recently cleared through the Food and Drug Administration (FDA). However, other dry electrodes that are not yet FDA cleared, for example those recently developed by Nanosonic, Inc as part of another NASA SBIR grant, can also be used. The various advantageous features of the harness include: 1) laypersons can be quickly instructed on its correct use, remotely if necessary; 2) all tangled "leadwire spaghetti" is eliminated, as is the common clinical problem of "leadwire reversal"; 3) all adhesives and disposables are also eliminated, the harness being fully reusable; if multiple individuals intend to use use the same harness, then standard antimicrobial wipes can be employed to sterilize the dry electrodes (and harness surface if needed) between users; 5) padded cushions at the lateral sides of the torso function to press the left arm (LA) and right arm (RA) dry electrodes mounted on the cushions against sideward or downward-rested arms of the subject; 6) sufficient distal placement of the arm electrodes achieves good electrode abutment to the arms without the need for adhesives, straps, bands, bracelets, or gloves; 7) padding over the sternum avoids "tenting" in the V1 through V3 (and, when present, the V3R) electrode positions; 8) easy-to-don, one-piece design with an adjustable, front-side single point of connection and an adjustable shoulder strap; and 9) Lund or "modified Lund" placement of the dry electrodes, the results of which more effectively reproduce results from "standard" 12-lead ECG placements than do results from Mason-Likar placements. The main limitation of the harness is that "one size does not fit all", meaning that an appropriately sized harness (small, medium, large, etc) must be chosen on the basis of an individual's size. To facilitate the use of the harness with inexpensive, commodity-grade cell phones and tablet devices, 12-lead ECG software is also being developed to accompany the harness for wireless use with Android. For this part of the project, NASA has teamed with TopCoder, Inc and the Harvard-affiliated NASA Tournament Lab in sponsoring java-based software programming contests through TopCoder. While ECG signals from the harness can already be wirelessly received and thoroughly processed (locally or remotely) by commercial-grade conventional (as well as advanced) 12-lead ECG software running on Microsoft Windows(TM), the Android-based software, once completed, will "cast a wider net" by allowing for a greater percentage of cell phone owners to participate in inexpensive, store-and-forward recordings of 12-lead ECGs worldwide, including for example Android cell phone users in many remote, third-world locations. At the time of writing, the Android 12-lead ECG software platform consists of a basic but expanding graphical user interface and accompanying software that: 1) wirelessly receives the 12-lead ECG data stream from a Bluetooth-based, FDA-cleared 12-leaCG device attached to the harness; 2) locally stores the same data in binary format to the SD card on the Android cell phone; and 3) makes the data stream in available in real time, for now to TopCoder's java programming contestants.

Wireless Body Sensor Network for low-power motion-tolerant synchronized vital sign measurement.

PubMed

Volmer, Achim; Orglmeister, Reinhold

2008-01-01

Prophylaxis and rehabilitation of cardiovascular disease require the development of biosignal acquisition and processing devices that are capable of supporting patients in their everyday life. This paper presents a Body Sensor Network (BSN) for use in Personal Healthcare applications. It consists of miniaturized sensor modules for electrocardiogram (ECG), photoplethysmogram (PPG) and phonocardiography (PCG) which are wirelessly connected with a coordinator to collect the data. Each sensor module is combined with a tri-axis accelerometer for patient's posture and activity measurement. As it is possible to extract further information about the health state by fusioning data of different biosensors, the wireless link based on IEEE 802.15.4 was extended by a synchronisation mechanism enabling synchronous sampling of the individual sensors. An adaptive application of algorithms for signal pre-processing and analysis allows the reduction of the transferred data.

History of research in Japan on electrocardiography in the racehorse

PubMed Central

HIRAGA, Atsushi; SUGANO, Shigeru

2015-01-01

ABSTRACT Since the first recording of electrocardiograms (ECGs) of a horse in Japan was carried out in 1944, studies on ECGs have been performed intensively. During the early stages of research from the 1950s to 1960s, leads to use for ECG recording were evaluated using several different approaches including unipolar leads, bipolar limb leads, and bipolar chest leads. Based on these studies, the AB lead, which is oriented along the long axis of the heart, became the standard reference method in Japan. Electrodes of the AB lead are placed on the upper 1/4th point along a straight line between the withers and the left shoulder blade (base: B), and 10 cm posterior to the left olecranon (apex: A). The incidence of equine arrhythmias among racehorses has been surveyed, and details of the electrocardiographic characteristics of several arrhythmias have been investigated. In particular, atrial fibrillation (AF) has been extensively studied, and papers have reported findings such as that paroxysmal AF occurs during racing and described electrocardiographic changes that occur at the onset of AF during exercise. Development of a radiotelemetry system for ECG recording enabled the first recording of equine ECGs during galloping in 1964, the detection of arrhythmias, and calculation of heart rate during exercise. Studies on comparative and developmental changes of ECGs have described characteristics of the equine ECGs. Future research on changes in cardiac function, including autonomic function, that occur with aging may lead to new developments in equine electrocardiography and contribute to improving the health and welfare of the horse. PMID:25829865

Identifying QT prolongation from ECG impressions using a general-purpose Natural Language Processor

PubMed Central

Denny, Joshua C.; Miller, Randolph A.; Waitman, Lemuel Russell; Arrieta, Mark; Peterson, Joshua F.

2009-01-01

Objective Typically detected via electrocardiograms (ECGs), QT interval prolongation is a known risk factor for sudden cardiac death. Since medications can promote or exacerbate the condition, detection of QT interval prolongation is important for clinical decision support. We investigated the accuracy of natural language processing (NLP) for identifying QT prolongation from cardiologist-generated, free-text ECG impressions compared to corrected QT (QTc) thresholds reported by ECG machines. Methods After integrating negation detection to a locally-developed natural language processor, the KnowledgeMap concept identifier, we evaluated NLP-based detection of QT prolongation compared to the calculated QTc on a set of 44,318 ECGs obtained from hospitalized patients. We also created a string query using regular expressions to identify QT prolongation. We calculated sensitivity and specificity of the methods using manual physician review of the cardiologist-generated reports as the gold standard. To investigate causes of “false positive” calculated QTc, we manually reviewed randomly selected ECGs with a long calculated QTc but no mention of QT prolongation. Separately, we validated the performance of the negation detection algorithm on 5,000 manually-categorized ECG phrases for any medical concept (not limited to QT prolongation) prior to developing the NLP query for QT prolongation. Results The NLP query for QT prolongation correctly identified 2,364 of 2,373 ECGs with QT prolongation with a sensitivity of 0.996 and a positive predictive value of 1.000. There were no false positives. The regular expression query had a sensitivity of 0.999 and positive predictive value of 0.982. In contrast, the positive predictive value of common QTc thresholds derived from ECG machines was 0.07–0.25 with corresponding sensitivities of 0.994–0.046. The negation detection algorithm had a recall of 0.973 and precision of 0.982 for 10,490 concepts found within ECG impressions. Conclusions NLP and regular expression queries of cardiologists’ ECG interpretations can more effectively identify QT prolongation than the automated QTc intervals reported by ECG machines. Future clinical decision support could employ NLP queries to detect QTc prolongation and other reported ECG abnormalities. PMID:18938105

Prevalence and prognostic impact of electrocardiographic abnormalities in outpatients with extracardiac artery disease.

PubMed

Hysing, Per; Jonason, Tommy; Leppert, Jerzy; Hedberg, Pär

2017-11-24

Identifying cardiac disease in patients with extracardiac artery disease (ECAD) is essential for clinical decision-making. Electrocardiography (ECG) is an easily accessible tool to unmask subclinical cardiac disease and to risk stratify patient with or without manifest cardiovascular disease (CV). We aimed to examine the prevalence and prognostic impact of ECG changes in outpatients with ECAD. Outpatients with carotid or lower extremity artery disease (n = 435) and community-based controls (n = 397) underwent resting ECG. The patients were followed during a median of 4·8 years for CV events (hospitalization or death caused by ischaemic heart disease, cardiac arrest, heart failure, or stroke). ECG abnormalities were classified according to the Minnesota Code. Major (33% versus 15%, P<0·001) but not minor ECG abnormalities (23% versus 26%, P = 0·42) were significantly more common in patients versus controls. During the follow-up, 141 patients experienced CV events. Both major ECG abnormalities [hazard ratio (HR) 1·58, 95% confidence interval (CI) 1·11-2·25, P = 0·012] and any ECG abnormalities (HR 1·57, 95% CI 1·06-2·33, P = 0·024) were significantly associated with CV events after adjustment for potential risk factors. In conclusion, ECG abnormalities were common in these outpatients with ECAD. Major and any ECG abnormalities were independent predictors of CV events. Addition of easily accessible ECG information might be useful in risk stratification for such patients. © 2017 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

Association between obesity and ECG variables in children and adolescents: A cross-sectional study.

PubMed

Sun, Guo-Zhe; Li, Yang; Zhou, Xing-Hu; Guo, Xiao-Fan; Zhang, Xin-Gang; Zheng, Li-Qiang; Li, Yuan; Jiao, Yun-DI; Sun, Ying-Xian

2013-12-01

Obesity exhibits a wide variety of electrocardiogram (ECG) abnormalities in adults, which often lead to cardiovascular events. However, there is currently no evidence of an association between obesity and ECG variables in children and adolescents. The present study aimed to explore the associations between obesity and ECG intervals and axes in children and adolescents. A cross-sectional observational study of 5,556 students aged 5-18 years was performed. Anthropometric data, blood pressure and standard 12-lead ECGs were collected for each participant. ECG variables were measured manually based on the temporal alignment of simultaneous 12 leads using a CV200 ECG Work Station. Overweight and obese groups demonstrated significantly longer PR intervals, wider QRS durations and leftward shifts of frontal P-wave, QRS and T-wave axes, while the obese group also demonstrated significantly higher heart rates, compared with normal weight groups within normotensive or hypertensive subjects (P<0.05). Abdominal obesity was also associated with longer PR intervals, wider QRS duration and a leftward shift of frontal ECG axes compared with normal waist circumference (WC) within normotensive or hypertensive subjects (P<0.05). Gender was a possible factor affecting the ECG variables. Furthermore, the ECG variables, including PR interval, QRS duration and frontal P-wave, QRS and T-wave axes, were significantly linearly correlated with body mass index, WC and waist-to-height ratio adjusted for age, gender, ethnicity and blood pressure. However, there was no significant association between obesity and the corrected QT interval (P>0.05). The results of the current study indicate that in children and adolescents, general and abdominal obesity is associated with longer PR intervals, wider QRS duration and a leftward shift of frontal P-wave, QRS and T-wave axes, independent of age, gender, ethnicity and blood pressure.

Personal Verification/Identification via Analysis of the Peripheral ECG Leads: Influence of the Personal Health Status on the Accuracy

PubMed Central

Bortolan, Giovanni

2015-01-01

Traditional means for identity validation (PIN codes, passwords), and physiological and behavioral biometric characteristics (fingerprint, iris, and speech) are susceptible to hacker attacks and/or falsification. This paper presents a method for person verification/identification based on correlation of present-to-previous limb ECG leads: I (r I), II (r II), calculated from them first principal ECG component (r PCA), linear and nonlinear combinations between r I, r II, and r PCA. For the verification task, the one-to-one scenario is applied and threshold values for r I, r II, and r PCA and their combinations are derived. The identification task supposes one-to-many scenario and the tested subject is identified according to the maximal correlation with a previously recorded ECG in a database. The population based ECG-ILSA database of 540 patients (147 healthy subjects, 175 patients with cardiac diseases, and 218 with hypertension) has been considered. In addition a common reference PTB dataset (14 healthy individuals) with short time interval between the two acquisitions has been taken into account. The results on ECG-ILSA database were satisfactory with healthy people, and there was not a significant decrease in nonhealthy patients, demonstrating the robustness of the proposed method. With PTB database, the method provides an identification accuracy of 92.9% and a verification sensitivity and specificity of 100% and 89.9%. PMID:26568954

Personal Verification/Identification via Analysis of the Peripheral ECG Leads: Influence of the Personal Health Status on the Accuracy.

PubMed

Jekova, Irena; Bortolan, Giovanni

2015-01-01

Traditional means for identity validation (PIN codes, passwords), and physiological and behavioral biometric characteristics (fingerprint, iris, and speech) are susceptible to hacker attacks and/or falsification. This paper presents a method for person verification/identification based on correlation of present-to-previous limb ECG leads: I (r I), II (r II), calculated from them first principal ECG component (r PCA), linear and nonlinear combinations between r I, r II, and r PCA. For the verification task, the one-to-one scenario is applied and threshold values for r I, r II, and r PCA and their combinations are derived. The identification task supposes one-to-many scenario and the tested subject is identified according to the maximal correlation with a previously recorded ECG in a database. The population based ECG-ILSA database of 540 patients (147 healthy subjects, 175 patients with cardiac diseases, and 218 with hypertension) has been considered. In addition a common reference PTB dataset (14 healthy individuals) with short time interval between the two acquisitions has been taken into account. The results on ECG-ILSA database were satisfactory with healthy people, and there was not a significant decrease in nonhealthy patients, demonstrating the robustness of the proposed method. With PTB database, the method provides an identification accuracy of 92.9% and a verification sensitivity and specificity of 100% and 89.9%.

Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals.

PubMed

Sudarshan, Vidya K; Acharya, U Rajendra; Oh, Shu Lih; Adam, Muhammad; Tan, Jen Hong; Chua, Chua Kuang; Chua, Kok Poo; Tan, Ru San

2017-04-01

Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments. Copyright © 2017 Elsevier Ltd. All rights reserved.

Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection.

PubMed

Nuryani, Nuryani; Ling, Steve S H; Nguyen, H T

2012-04-01

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity.

ECG authentication in post-exercise situation.

PubMed

Dongsuk Sung; Jeehoon Kim; Myungjun Koh; Kwangsuk Park

2017-07-01

Human authentication based on electrocardiogram (ECG) has been a remarkable issue for recent ten years. This paper proposed an authentication technology with the ECG data recorded after the harsh exercise. 55 subjects voluntarily attended to this experiment. A stepper was used as an exercise equipment. The subjects are asked to do stepper for 5 minutes and their ECG signals are acquired before and after the exercise in rest, sitting posture. Linear discriminant analysis (LDA) was used for both feature extraction and classification. Even though, within the first 1 minute recording, the subject recognition accuracy was 59.64%, which is too low to utilize, after one minute the accuracy was higher than 90% and it increased up to 96.22% within 5 minutes, which is plausible to use in authentication circumstances. Therefore, we have concluded that ECG authentication techniques will be able to be used after 1 minute of catching breath.

A Primary Study of Indirect ECG Monitor Embedded in a Bed for Home Health Care

NASA Astrophysics Data System (ADS)

Ueno, Akinori; Shiogai, Yuuki; Ishiyama, Yoji

A system for monitoring electrocardiogram (ECG) through clothes inserted between the measuring electrodes and the body surface of a subject when lying on a mattress has been proposed. The principle of the system is based on capacitive coupling involving the electrode, the clothes, and the skin. Validation of the system revealed the following: (1) In spite of the gain attenuation in the pass band of the system, distortion of the detected signal was subtle even when clothes thicker than 1mm were inserted, (2) The system was able to yield a stable ECG from a subject particularly during sound sleep, (3) The system succeeded in detecting ECG after changing the posture into any of supine, right lateral, or left lateral positions by adopting a newly devised electrode configuration. Therefore, the proposed system appears promising for application to bedding as a non-invasive and awareness-free system for ECG monitoring during sleep.

An ECG ambulatory system with mobile embedded architecture for ST-segment analysis.

PubMed

Miranda-Cid, Alejandro; Alvarado-Serrano, Carlos

2010-01-01

A prototype of a ECG ambulatory system for long term monitoring of ST segment of 3 leads, low power, portability and data storage in solid state memory cards has been developed. The solution presented is based in a mobile embedded architecture of a portable entertainment device used as a tool for storage and processing of bioelectric signals, and a mid-range RISC microcontroller, PIC 16F877, which performs the digitalization and transmission of ECG. The ECG amplifier stage is a low power, unipolar voltage and presents minimal distortion of the phase response of high pass filter in the ST segment. We developed an algorithm that manages access to files through an implementation for FAT32, and the ECG display on the device screen. The records are stored in TXT format for further processing. After the acquisition, the system implemented works as a standard USB mass storage device.

Modelling and prediction for chaotic fir laser attractor using rational function neural network.

PubMed

Cho, S

2001-02-01

Many real-world systems such as irregular ECG signal, volatility of currency exchange rate and heated fluid reaction exhibit highly complex nonlinear characteristic known as chaos. These chaotic systems cannot be retreated satisfactorily using linear system theory due to its high dimensionality and irregularity. This research focuses on prediction and modelling of chaotic FIR (Far InfraRed) laser system for which the underlying equations are not given. This paper proposed a method for prediction and modelling a chaotic FIR laser time series using rational function neural network. Three network architectures, TDNN (Time Delayed Neural Network), RBF (radial basis function) network and the RF (rational function) network, are also presented. Comparisons between these networks performance show the improvements introduced by the RF network in terms of a decrement in network complexity and better ability of predictability.

HSDPA (3.5G)-based ubiquitous integrated biotelemetry system for emergency care.

PubMed

Kang, Jaemin; Shin, Il Hyung; Koo, Yoonseo; Jung, Min Yang; Suh, Gil Joon; Kim, Hee Chan

2007-01-01

We have developed the second prototype system of Ubiquitous Integrated Biotelemetry System for Emergency Care(UIBSEC) using a HSDPA(High Speed Downlink Packet Access) modem to be used by emergency rescuers to get directions from medical doctors in providing emergency medical services for patients in ambulance. Five vital bio-signal instrumentation modules have been implemented, which include noninvasive arterial blood pressure (NIBP), arterial oxygen saturation (SaO2), 6-channel electro-cardiogram(ECG), blood glucose level, and body temperature and real-time motion picture of the patient and GPS information are also taken. Measured patient data, captured motion picture and GPS information are then transferred to a doctor's PC through the HSDPA and TCP/IP networks using stand-alone HSDPA modem. Most prominent feature of the developed system is that it is based on the HSDPA backbone networks available in Korea now, through which we will be able to establish a ubiquitous emergency healthcare service system.

Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study.

PubMed

Lowres, Nicole; Neubeck, Lis; Salkeld, Glenn; Krass, Ines; McLachlan, Andrew J; Redfern, Julie; Bennett, Alexandra A; Briffa, Tom; Bauman, Adrian; Martinez, Carlos; Wallenhorst, Christopher; Lau, Jerrett K; Brieger, David B; Sy, Raymond W; Freedman, S Ben

2014-06-01

Atrial fibrillation (AF) causes a third of all strokes, but often goes undetected before stroke. Identification of unknown AF in the community and subsequent anti-thrombotic treatment could reduce stroke burden. We investigated community screening for unknown AF using an iPhone electrocardiogram (iECG) in pharmacies, and determined the cost-effectiveness of this strategy.Pharmacists performedpulse palpation and iECG recordings, with cardiologist iECG over-reading. General practitioner review/12-lead ECG was facilitated for suspected new AF. An automated AF algorithm was retrospectively applied to collected iECGs. Cost-effectiveness analysis incorporated costs of iECG screening, and treatment/outcome data from a United Kingdom cohort of 5,555 patients with incidentally detected asymptomatic AF. A total of 1,000 pharmacy customers aged ≥65 years (mean 76 ± 7 years; 44% male) were screened. Newly identified AF was found in 1.5% (95% CI, 0.8-2.5%); mean age 79 ± 6 years; all had CHA2DS2-VASc score ≥2. AF prevalence was 6.7% (67/1,000). The automated iECG algorithm showed 98.5% (CI, 92-100%) sensitivity for AF detection and 91.4% (CI, 89-93%) specificity. The incremental cost-effectiveness ratio of extending iECG screening into the community, based on 55% warfarin prescription adherence, would be $AUD5,988 (€3,142; $USD4,066) per Quality Adjusted Life Year gained and $AUD30,481 (€15,993; $USD20,695) for preventing one stroke. Sensitivity analysis indicated cost-effectiveness improved with increased treatment adherence.Screening with iECG in pharmacies with an automated algorithm is both feasible and cost-effective. The high and largely preventable stroke/thromboembolism risk of those with newly identified AF highlights the likely benefits of community AF screening. Guideline recommendation of community iECG AF screening should be considered.

STEM Education in Jordan Applicable to Developing Future Geophysicists: An Example Combining Electrical Engineering and Medical Research

NASA Astrophysics Data System (ADS)

Fraiwan, A.; Khadra, L.; Shahab, W.; Olgaard, D. L.

2010-12-01

Students in developing countries interested in STEM disciplines (science, technology, engineering & math) often choose majors that will improve their job opportunities in their home country when they graduate, e.g. engineering or medicine. Geoscience might be chosen as a sub-discipline of civil engineering, but rarely as a primary major unless there are local economic natural resources. The Institute of International Education administers the ExxonMobil Middle East and North Africa region scholars program designed to develop skilled students with a focus on geoscience and to build relationships with academic leaders by offering select faculty the opportunity to participation in the AGU fall meeting. At the Jordan University of Science and Technology (JUST), research in electrical engineering applied to medicine has potential links to geosciences. In geophysics, neural wavelet analysis (NWA) is commonly used to process complex seismic signals, e.g. for interpreting lithology or identifying hydrocarbons. In this study, NWA was used to characterize cardiac arrhythmias. A classification scheme was developed in which a neural network is used to identify three types of arrhythmia by distinct frequency bands. The performance of this scheme was tested using patient records from two electrocardiography (ECG) databases. These records contain normal ECG signals, as well as abnormal signals from atrial fibrillation (AF), ventricular tachycardia (VT) and ventricular fibrillation (VF) arrhythmias. The continuous wavelet transform is applied over frequencies of 0-50 Hz for times of 0-2s. For a normal ECG, the results show that the strongest signal is in a frequency range of 4-10 Hz. For AF, a low frequency ECG signal in the range of 0-5 Hz extends over the whole time domain. For VT, the low frequency spectrum is in the range of 2-10 Hz, appearing as three distinct bands. For VF, a continuous band in the range of 2-10 Hz extends over the whole time domain. The classification of the three arrhythmias used a Back-propagation neural network whose input is the energy level calculated from the wavelet transform. The network was trained using 13 different patterns (3 for AF, 5 for VT and 5 for VF) and blind tested on 25 records. The classification scheme correctly identified all 9 VF records, 5 of 6 VT records, and 9 of 10 AF records. Manual interpretation of time-frequency seismic data is computationally intensive because large volumes of data are generated during the time-frequency analysis process. The proposed NWA method has the potential to partially automate the interpretation of seismic data. Also, a relatively straight-forward adaptation of the proposed NWA-based classification scheme may help identify hydrocarbon-laden reservoirs, which have been observed to contain enhanced low-frequency content in the time-frequency domain (Castagna, Sun, & Siegfried, 2003).

From Pacemaker to Wearable: Techniques for ECG Detection Systems.

PubMed

Kumar, Ashish; Komaragiri, Rama; Kumar, Manjeet

2018-01-11

With the alarming rise in the deaths due to cardiovascular diseases (CVD), present medical research scenario places notable importance on techniques and methods to detect CVDs. As adduced by world health organization, technological proceeds in the field of cardiac function assessment have become the nucleus and heart of all leading research studies in CVDs in which electrocardiogram (ECG) analysis is the most functional and convenient tool used to test the range of heart-related irregularities. Most of the approaches present in the literature of ECG signal analysis consider noise removal, rhythm-based analysis, and heartbeat detection to improve the performance of a cardiac pacemaker. Advancements achieved in the field of ECG segments detection and beat classification have a limited evaluation and still require clinical approvals. In this paper, approaches on techniques to implement on-chip ECG detector for a cardiac pacemaker system are discussed. Moreover, different challenges regarding the ECG signal morphology analysis deriving from medical literature is extensively reviewed. It is found that robustness to noise, wavelet parameter choice, numerical efficiency, and detection performance are essential performance indicators required by a state-of-the-art ECG detector. Furthermore, many algorithms described in the existing literature are not verified using ECG data from the standard databases. Some ECG detection algorithms show very high detection performance with the total number of detected QRS complexes. However, the high detection performance of the algorithm is verified using only a few datasets. Finally, gaps in current advancements and testing are identified, and the primary challenge remains to be implementing bullseye test for morphology analysis evaluation.

Comparison between pulse oximetry and transthoracic impedance alarm traces during home monitoring.

PubMed

Nassi, N; Piumelli, R; Lombardi, E; Landini, L; Donzelli, G; de Martino, M

2008-02-01

To compare transthoracic impedance (TTI/ECG) and pulse oximetry alarm traces detected during home monitoring in infants at risk of apnoea, bradycardia and hypoxaemia. A retrospective evaluation of the monitor downloads of 67 infants who had undergone either TTI/ECG or pulse oximetry home monitoring using a device which can detect both parameters. The patients were categorised as: apparent life-threatening events (n = 39), preterm infants (n = 21) and miscellaneous (n = 7). TTI/ECG and pulse oximetry alarm traces were scored as either true or false alarms. Classification criteria were based on visual analysis of the impedance and plethysmographic waveforms captured by the memory monitor every time alarm thresholds were violated. 5242 alarms occurred over 3452 days of monitoring: 4562 (87%) were false and 680 (13%) true. The mean duration of monitoring was 51 days (range 5-220 days). There were 2982 TTI/ECG false alarms (65% of the total) and 1580 pulse oximetry false alarms (35%) (p = 0.0042). Of the 680 true alarms, 507 (74%) were desaturations not attributable to central apnoea and 173 (26%) were true TTI/ECG alarms (p = 0.0013). Comparison of pulse oximetry and TTI/ECG alarm traces shows that true events were mostly attributable to desaturations, while false alarms were mainly provoked by TTI/ECG. The total number of false alarms is lower than reported in other studies using TTI/ECG only, thus indicating that monitoring using both pulse oximetry and TTI/ECG is suitable for home use. When the combination of both techniques is not feasible or not required, we recommend the use of motion resistant pulse oximetry alone.

Motion artifact removal algorithm by ICA for e-bra: a women ECG measurement system

NASA Astrophysics Data System (ADS)

Kwon, Hyeokjun; Oh, Sechang; Varadan, Vijay K.

2013-04-01

Wearable ECG(ElectroCardioGram) measurement systems have increasingly been developing for people who suffer from CVD(CardioVascular Disease) and have very active lifestyles. Especially, in the case of female CVD patients, several abnormal CVD symptoms are accompanied with CVDs. Therefore, monitoring women's ECG signal is a significant diagnostic method to prevent from sudden heart attack. The E-bra ECG measurement system from our previous work provides more convenient option for women than Holter monitor system. The e-bra system was developed with a motion artifact removal algorithm by using an adaptive filter with LMS(least mean square) and a wandering noise baseline detection algorithm. In this paper, ICA(independent component analysis) algorithms are suggested to remove motion artifact factor for the e-bra system. Firstly, the ICA algorithms are developed with two kinds of statistical theories: Kurtosis, Endropy and evaluated by performing simulations with a ECG signal created by sgolayfilt function of MATLAB, a noise signal including 0.4Hz, 1.1Hz and 1.9Hz, and a weighed vector W estimated by kurtosis or entropy. A correlation value is shown as the degree of similarity between the created ECG signal and the estimated new ECG signal. In the real time E-Bra system, two pseudo signals are extracted by multiplying with a random weighted vector W, the measured ECG signal from E-bra system, and the noise component signal by noise extraction algorithm from our previous work. The suggested ICA algorithm basing on kurtosis or entropy is used to estimate the new ECG signal Y without noise component.

Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features.

PubMed

Tripathy, Rajesh Kumar; Dandapat, Samarendra

2017-04-01

The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques.

FPGA Implementation of Heart Rate Monitoring System.

PubMed

Panigrahy, D; Rakshit, M; Sahu, P K

2016-03-01

This paper describes a field programmable gate array (FPGA) implementation of a system that calculates the heart rate from Electrocardiogram (ECG) signal. After heart rate calculation, tachycardia, bradycardia or normal heart rate can easily be detected. ECG is a diagnosis tool routinely used to access the electrical activities and muscular function of the heart. Heart rate is calculated by detecting the R peaks from the ECG signal. To provide a portable and the continuous heart rate monitoring system for patients using ECG, needs a dedicated hardware. FPGA provides easy testability, allows faster implementation and verification option for implementing a new design. We have proposed a five-stage based methodology by using basic VHDL blocks like addition, multiplication and data conversion (real to the fixed point and vice-versa). Our proposed heart rate calculation (R-peak detection) method has been validated, using 48 first channel ECG records of the MIT-BIH arrhythmia database. It shows an accuracy of 99.84%, the sensitivity of 99.94% and the positive predictive value of 99.89%. Our proposed method outperforms other well-known methods in case of pathological ECG signals and successfully implemented in FPGA.

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

He, Runnan; Wang, Kuanquan; Li, Qince; Yuan, Yongfeng; Zhao, Na; Liu, Yang; Zhang, Henggui

2017-12-01

Cardiovascular diseases are associated with high morbidity and mortality. However, it is still a challenge to diagnose them accurately and efficiently. Electrocardiogram (ECG), a bioelectrical signal of the heart, provides crucial information about the dynamical functions of the heart, playing an important role in cardiac diagnosis. As the QRS complex in ECG is associated with ventricular depolarization, therefore, accurate QRS detection is vital for interpreting ECG features. In this paper, we proposed a real-time, accurate, and effective algorithm for QRS detection. In the algorithm, a proposed preprocessor with a band-pass filter was first applied to remove baseline wander and power-line interference from the signal. After denoising, a method combining K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) was used for accurate QRS detection in ECGs with different morphologies. The proposed algorithm was tested and validated using 48 ECG records from MIT-BIH arrhythmia database (MITDB), achieved a high averaged detection accuracy, sensitivity and positive predictivity of 99.43, 99.69, and 99.72%, respectively, indicating a notable improvement to extant algorithms as reported in literatures.

Seismocardiography-Based Cardiac Computed Tomography Gating Using Patient-Specific Template Identification and Detection.

PubMed

Yao, Jingting; Tridandapani, Srini; Wick, Carson A; Bhatti, Pamela T

2017-01-01

To more accurately trigger cardiac computed tomography angiography (CTA) than electrocardiography (ECG) alone, a sub-system is proposed as an intermediate step toward fusing ECG with seismocardiography (SCG). Accurate prediction of quiescent phases is crucial to prospectively gating CTA, which is susceptible to cardiac motion and, thus, can affect the diagnostic quality of images. The key innovation of this sub-system is that it identifies the SCG waveform corresponding to heart sounds and determines their phases within the cardiac cycles. Furthermore, this relationship is modeled as a linear function with respect to heart rate. For this paper, B-mode echocardiography is used as the gold standard for identifying the quiescent phases. We analyzed synchronous ECG, SCG, and echocardiography data acquired from seven healthy subjects (mean age: 31; age range: 22-48; males: 4) and 11 cardiac patients (mean age: 56; age range: 31-78; males: 6). On average, the proposed algorithm was able to successfully identify 79% of the SCG waveforms in systole and 68% in diastole. The simulated results show that SCG-based prediction produced less average phase error than that of ECG. It was found that the accuracy of ECG-based gating is more susceptible to increases in heart rate variability, while SCG-based gating is susceptible to high cycle to cycle variability in morphology. This pilot work of prediction using SCG waveforms enriches the framework of a comprehensive system with multiple modalities that could potentially, in real time, improve the image quality of CTA.

WIH-based IEEE 802.11 ECG monitoring implementation.

PubMed

Moein, A; Pouladian, M

2007-01-01

New wireless technologies make possible the implementation of high level integration wireless devices which allow the replacement of traditional large wired monitoring devices. It offers new functionalities to physicians and will reduce the costs. Among these functionalities, biomedical signals can be sent to other devices (PDA, PC . . . ) or processing centers, without restricting the patients' mobility. This article discusses the WIH (Ward-In-Hand) structure and the software required for its implementation before an operational example is presented with its results. The aim of this project is the development and implementation of a reduced size electrocardiograph based on IEEE 802.11 with high speed and more accuracy, which allows wireless monitoring of patients, and the insertion of the information into the Wi-Fi hospital networks.

Cadmium stress assessment based on the electrocardiogram characteristics of zebra fish (Danio rerio): QRS complex could play an important role.

PubMed

Xing, Na; Ji, Lizhen; Song, Jie; Ma, Jingchun; Li, Shangge; Ren, Zongming; Xu, Fei; Zhu, Jianping

2017-10-01

The electrocardiogram (ECG) of zebra fish (Danio rerio) expresses cardiac features that are similar to humans. Here we use sharp microelectrode measurements to obtain ECG characteristics in adult zebra fish and analyze the effects of cadmium chloride (CdCl 2 ) on the heart. We observe the overall changes of ECG parameters in different treatments (0.1 TU, 0.5 TU and 1.0 TU CdCl 2 ), including P wave, Q wave, R wave, S wave, T wave, PR interval (atrial contraction), QRS complex (ventricular depolarization), ST segment, and QT interval (ventricular repolarization). The trends of the ECG parameters showed some responses to the concentration and exposure time of CdCl 2 , but it was difficult to obtain more information about the useful indicators in water quality assessment depending on tendency analysis alone. A self-organizing map (SOM) showed that P values, R values, and T values were similar; R wave and T wave amplitude were similar; and most important, QRS value was similar to the CdCl 2 stress according to the classified data patterns including CdCl 2 stress (E) and ECG components based on the Ward linkage. It suggested that the duration of QRS complex was related to environmental stress E directly. The specification and evaluation of ECG parameters in Cd 2+ pollution suggested that there is a markedly significant correlation between QRS complex and CdCl 2 stress with the highest r (0.729) and the smallest p (0.002) among all ECG characteristics. In this case, it is concluded that QRS complex can be used as an indicator in the CdCl 2 stress assessment due to the lowest AIC data abased on the linear regression model between the CdCl 2 stress and ECG parameters. Copyright © 2017 Elsevier B.V. All rights reserved.

CNT/PDMS composite flexible dry electrodes for long-term ECG monitoring.

PubMed

Jung, Ha-Chul; Moon, Jin-Hee; Baek, Dong-Hyun; Lee, Jae-Hee; Choi, Yoon-Young; Hong, Joung-Sook; Lee, Sang-Hoon

2012-05-01

We fabricated a carbon nanotube (CNT)/ polydimethylsiloxane (PDMS) composite-based dry ECG electrode that can be readily connected to conventional ECG devices, and showed its long-term wearable monitoring capability and robustness to motion and sweat. While the dispersion of CNTs in PDMS is challenging, we optimized the process to disperse untreated CNTs within PDMS by mechanical force only. The electrical and mechanical characteristics of the CNT/PDMS electrode were tested according to the concentration of CNTs and its thickness. The performances of ECG electrodes were evaluated by using 36 types of electrodes which were fabricated with different concentrations of CNTs, and with a differing diameter and thickness. The ECG signals were obtained by using electrodes of diverse sizes to observe the effects of motion and sweat, and the proposed electrode was shown to be robust to both factors. The CNT concentration and diameter of the electrodes were critical parameters in obtaining high-quality ECG signals. The electrode was shown to be biocompatible from the cytotoxicity test. A seven-day continuous wearability test showed that the quality of the ECG signal did not degrade over time, and skin reactions such as itching or erythema were not observed. This electrode could be used for the long-term measurement of other electrical biosignals for ubiquitous health monitoring including EMG, EEG, and ERG.

Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

PubMed Central

Penzel, Thomas; Kantelhardt, Jan W.; Bartsch, Ronny P.; Riedl, Maik; Kraemer, Jan F.; Wessel, Niels; Garcia, Carmen; Glos, Martin; Fietze, Ingo; Schöbel, Christoph

2016-01-01

The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However, their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG, and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability (HRV) analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave). PMID:27826247

Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography.

PubMed

Penzel, Thomas; Kantelhardt, Jan W; Bartsch, Ronny P; Riedl, Maik; Kraemer, Jan F; Wessel, Niels; Garcia, Carmen; Glos, Martin; Fietze, Ingo; Schöbel, Christoph

2016-01-01

The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However, their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG, and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability (HRV) analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave).

Comparison of automated measurements of electrocardiographic intervals and durations by computer-based algorithms of digital electrocardiographs.

PubMed

Kligfield, Paul; Badilini, Fabio; Rowlandson, Ian; Xue, Joel; Clark, Elaine; Devine, Brian; Macfarlane, Peter; de Bie, Johan; Mortara, David; Babaeizadeh, Saeed; Gregg, Richard; Helfenbein, Eric D; Green, Cynthia L

2014-02-01

Automated measurements of electrocardiographic (ECG) intervals are widely used by clinicians for individual patient diagnosis and by investigators in population studies. We examined whether clinically significant systematic differences exist in ECG intervals measured by current generation digital electrocardiographs from different manufacturers and whether differences, if present, are dependent on the degree of abnormality of the selected ECGs. Measurements of RR interval, PR interval, QRS duration, and QT interval were made blindly by 4 major manufacturers of digital electrocardiographs used in the United States from 600 XML files of ECG tracings stored in the US FDA ECG warehouse and released for the purpose of this study by the Cardiac Safety Research Consortium. Included were 3 groups based on expected QT interval and degree of repolarization abnormality, comprising 200 ECGs each from (1) placebo or baseline study period in normal subjects during thorough QT studies, (2) peak moxifloxacin effect in otherwise normal subjects during thorough QT studies, and (3) patients with genotyped variants of congenital long QT syndrome (LQTS). Differences of means between manufacturers were generally small in the normal and moxifloxacin subjects, but in the LQTS patients, differences of means ranged from 2.0 to 14.0 ms for QRS duration and from 0.8 to 18.1 ms for the QT interval. Mean absolute differences between algorithms were similar for QRS duration and QT intervals in the normal and in the moxifloxacin subjects (mean ≤6 ms) but were significantly larger in patients with LQTS. Small but statistically significant group differences in mean interval and duration measurements and means of individual absolute differences exist among automated algorithms of widely used, current generation digital electrocardiographs. Measurement differences, including QRS duration and the QT interval, are greatest for the most abnormal ECGs. © 2014.

Electrocardiographic Criteria for Left Ventricular Hypertrophy in Asians Differs from Criteria Derived from Western Populations--Community-based Data from an Asian Population.

PubMed

Xu, Chang Fen; Tan, Eugene S J; Feng, Liang; Santhanakrishnan, Rajalakshmi; Chan, Michelle M Y; Nyunt, Shwe Zin; Ng, Tze Pin; Ling, Lieng Hsi; Richards, A Mark; Lam, Carolyn S P; Lim, Toon Wei

2015-08-01

Electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH), such as the Cornell and Sokolow-Lyon voltage criteria were derived from Western populations. However, their utility and accuracy for diagnosing echocardiographic LVH in Asian populations is unclear. The objective of this study was to assess the accuracy of ECG criteria for LVH in Asians and to determine if alternative gender-specific ECG cut-offs may improve its diagnostic accuracy. ECG and echocardiographic assessments were performed on 668 community-dwelling Asian adults (50.9% women; 57 ± 10 years) in Singapore. The accuracy of ECG voltage criteria was compared to echocardiographic LVH criteria based on the American Society of Echocardiography guidelines, and Asian ethnicity and gender-specific partition values. Echocardiographic LVH was present in 93 (13.6%) adults. Cornell criteria had low sensitivity (5.5%) and high specificity (98.9%) for diagnosing LVH. Modified gender specific cut-offs (18 mm in women, 22 mm in men) improved sensitivity (8.8% to 17.5%, 0% to 14.7%, respectively) whilst preserving specificity (98.2% to 94.2%, 100% to 95.8%). Similarly, Sokolow-Lyon criteria had poor sensitivity (7.7%) and high specificity (96.1%) for diagnosing LVH. Lowering the cut-off value from 35 mm to 31 mm improved the sensitivity in women from 3.5% to 14% while preserving specificity at 94.2%. A cut-off of 36 mm was optimal in men (sensitivity of 14.7%, specificity of 95.5%). Current ECG criteria for LVH derived in Western cohorts have limited sensitivity in Asian populations. Our data suggests that ethnicity- and gender-specific ECG criteria may be needed.

Effects of cuff inflation and deflation on pulse transit time measured from ECG and multi-wavelength PPG.

PubMed

Liu, Jing; Li, Yao; Ding, Xiao-Rong; Dai, Wen-Xuan; Zhang, Yuan-Ting

2015-01-01

Pulse transit time (PTT), which refers to the time it takes a pulse wave to travel between two arterial sites is a promising index for cuff-less blood pressure (BP) estimation, as well as non-invasive assessment of arterial functions. However, it has not been investigated whether PTTs measured from ECG and different wavelength PPG are equally affected by the arterial status. Furthermore, comparison between the changes of different PTTs can provide enlightenment on the hardware implementation of the PTT-based BP estimation method. This work mainly studied the changes of PTTs calculated from electrocardiogram (ECG) and multi-wavelength photoplethysmogram (PPG) after exerting cuff pressure on the upper arm. A four-channel PPG acquisition system was developed to collect the multi-wavelength PPG signals of red, yellow, green and blue light at the fingertip simultaneously. Ten subjects participated in the experiment and their PTTs measured from different PPG and ECG signals before and after exerting cuff pressure were compared. This study found that within one minute after the four-minute cuff inflation and deflation process, the PTT measured from ECG and yellow PPG experienced a significant increase (p<;0.05) while the PTT from ECG and blue PPG had no statistical difference (p>0.9) compared with that before exerting cuff pressure. This indicates that PTTs calculated from different wavelength PPG have different recoverability from smooth muscle relaxation. Another interesting finding is that the PTT calculated from ECG and yellow PPG had a strong correlation (|r|>0.7) with the time difference between yellow PPG and other PPG signals, which implies the potential of the time difference between yellow PPG and other PPGs as a complementary to PTT-based model for blood pressure estimation.

Gender differences in the electrocardiogram screening of athletes.

PubMed

Bessem, Bram B; de Bruijn, Matthijs M C; Nieuwland, Wybe W

2017-02-01

Gender-related differences are frequently used in medicine. Electrocardiograms are also subject to such differences. This study evaluated gender differences in ECG parameters of young athletes, discussing the possible implications of these differences for ECG criteria used in the cardiovascular screening of young athletes. Observational cross-sectional study. In 2013 and 2014 all the ECGs from the cardiovascular screenings performed at University Sports Medical Centre in Groningen of the student athletes who wanted to participate in a college sports program were collected. The ECG characteristics were scored using computer-based measurements and the Seattle ECG criteria. The study population included 1436 athletes, of which 72% were male. Male athletes were older (19.3 years vs. 18.6 years), participated in sports more frequently (4.0/week vs. 3.8/week) and spent more hours per week practising sports (6.4h/week vs. 5.8h/week) than female athletes. Male athletes had significantly higher PR intervals (149ms vs. 141ms), lead voltages and QRS duration (98ms vs. 88ms). Female athletes had significantly higher resting heart rates (69/min vs. 64/min) and QTc intervals (407ms vs. 400ms). Male athletes also had significantly higher amounts of sinus bradycardia (38.3% vs. 23.0%), incomplete RBBB (15.0% vs. 3.7%), early repolarisation (4.5% vs. 1.0%) and isolated QRS voltage criteria for LVH (26.3% vs. 4.6%). All P-values were ≤0.001. ECGs of young athletes demonstrate gender-related differences. These differences could be considered in their cardiovascular screening. For the Seattle ECG criteria we advise additional research into the clinical implications of using gender-based cut-off values for the QRS duration in the intraventricular conduction delay criterion. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

A design of the u-health monitoring system using a Nintendo DS game machine.

PubMed

Lee, Sangjoon; Kim, Jinkwon; Kim, Jungkuk; Lee, Myoungho

2009-01-01

In this paper, we used the hand held type a Nintendo DS Game Machine for consisting of a u-Health Monitoring system. This system is consists of four parts. Biosignal acquire device is the first. The Second is a wireless sensor network device. The third is a wireless base-station for connecting internet network. Displaying units are the last part which were a personal computer and a Nintendo DS game machine. The bio-signal measurement device among the four parts the u-health monitoring system can acquire 7-channels data which have 3-channels ECG(Electrocardiogram), 3-axis accelerometer and tilting sensor data. Acquired data connect up the internet network throughout the wireless sensor network and a base-station. In the experiment, we concurrently display the bio-signals on to a monitor of personal computer and LCD of a Nintendo DS using wireless internet protocol and those monitoring devices placed off to the one side an office building. The result of the experiment, this proposed system effectively can transmit patient's biosignal data as a long time and a long distance. This suggestion of the u-health monitoring system need to operate in the ambulance, general hospitals and geriatric institutions as a u-health monitoring device.

Separation of electrocardiographic from electromyographic signals using dynamic filtration.

PubMed

Christov, Ivaylo; Raikova, Rositsa; Angelova, Silvija

2018-07-01

Trunk muscle electromyographic (EMG) signals are often contaminated by the electrical activity of the heart. During low or moderate muscle force, these electrocardiographic (ECG) signals disturb the estimation of muscle activity. Butterworth high-pass filters with cut-off frequency of up to 60 Hz are often used to suppress the ECG signal. Such filters disturb the EMG signal in both frequency and time domain. A new method based on the dynamic application of Savitzky-Golay filter is proposed. EMG signals of three left trunk muscles and pure ECG signal were recorded during different motor tasks. The efficiency of the method was tested and verified both with the experimental EMG signals and with modeled signals obtained by summing the pure ECG signal with EMG signals at different levels of signal-to-noise ratio. The results were compared with those obtained by application of high-pass, 4th order Butterworth filter with cut-off frequency of 30 Hz. The suggested method is separating the EMG signal from the ECG signal without EMG signal distortion across its entire frequency range regardless of amplitudes. Butterworth filter suppresses the signals in the 0-30 Hz range thus preventing the low-frequency analysis of the EMG signal. An additional disadvantage is that it passes high-frequency ECG signal components which is apparent at equal and higher amplitudes of the ECG signal as compared to the EMG signal. The new method was also successfully verified with abnormal ECG signals. Copyright © 2018. Published by Elsevier Ltd.

Robust QRS detection for HRV estimation from compressively sensed ECG measurements for remote health-monitoring systems.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2018-03-15

To present a new compressive sensing (CS)-based method for the acquisition of ECG signals and for robust estimation of heart-rate variability (HRV) parameters from compressively sensed measurements with high compression ratio. CS is used in the biosensor to compress the ECG signal. Estimation of the locations of QRS segments is carried out by applying two algorithms on the compressed measurements. The first algorithm reconstructs the ECG signal by enforcing a block-sparse structure on the first-order difference of the signal, so the transient QRS segments are significantly emphasized on the first-order difference of the signal. Multiple block-divisions of the signals are carried out with various block lengths, and multiple reconstructed signals are combined to enhance the robustness of the localization of the QRS segments. The second algorithm removes errors in the locations of QRS segments by applying low-pass filtering and morphological operations. The proposed CS-based method is found to be effective for the reconstruction of ECG signals by enforcing transient QRS structures on the first-order difference of the signal. It is demonstrated to be robust not only to high compression ratio but also to various artefacts present in ECG signals acquired by using on-body wireless sensors. HRV parameters computed by using the QRS locations estimated from the signals reconstructed with a compression ratio as high as 90% are comparable with that computed by using QRS locations estimated by using the Pan-Tompkins algorithm. The proposed method is useful for the realization of long-term HRV monitoring systems by using CS-based low-power wireless on-body biosensors.

Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by fibrillatory wave amplitude computation in multiple electrocardiogram leads.

PubMed

Zarzoso, Vicente; Latcu, Decebal G; Hidalgo-Muñoz, Antonio R; Meo, Marianna; Meste, Olivier; Popescu, Irina; Saoudi, Nadir

2016-12-01

Catheter ablation (CA) of persistent atrial fibrillation (AF) is challenging, and reported results are capable of improvement. A better patient selection for the procedure could enhance its success rate while avoiding the risks associated with ablation, especially for patients with low odds of favorable outcome. CA outcome can be predicted non-invasively by atrial fibrillatory wave (f-wave) amplitude, but previous works focused mostly on manual measures in single electrocardiogram (ECG) leads only. To assess the long-term prediction ability of f-wave amplitude when computed in multiple ECG leads. Sixty-two patients with persistent AF (52 men; mean age 61.5±10.4years) referred for CA were enrolled. A standard 1-minute 12-lead ECG was acquired before the ablation procedure for each patient. F-wave amplitudes in different ECG leads were computed by a non-invasive signal processing algorithm, and combined into a mutivariate prediction model based on logistic regression. During an average follow-up of 13.9±8.3months, 47 patients had no AF recurrence after ablation. A lead selection approach relying on the Wald index pointed to I, V1, V2 and V5 as the most relevant ECG leads to predict jointly CA outcome using f-wave amplitudes, reaching an area under the curve of 0.854, and improving on single-lead amplitude-based predictors. Analysing the f-wave amplitude in several ECG leads simultaneously can significantly improve CA long-term outcome prediction in persistent AF compared with predictors based on single-lead measures. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Design of a Biorthogonal Wavelet Transform Based R-Peak Detection and Data Compression Scheme for Implantable Cardiac Pacemaker Systems.

PubMed

Kumar, Ashish; Kumar, Manjeet; Komaragiri, Rama

2018-04-19

Bradycardia can be modulated using the cardiac pacemaker, an implantable medical device which sets and balances the patient's cardiac health. The device has been widely used to detect and monitor the patient's heart rate. The data collected hence has the highest authenticity assurance and is convenient for further electric stimulation. In the pacemaker, ECG detector is one of the most important element. The device is available in its new digital form, which is more efficient and accurate in performance with the added advantage of economical power consumption platform. In this work, a joint algorithm based on biorthogonal wavelet transform and run-length encoding (RLE) is proposed for QRS complex detection of the ECG signal and compressing the detected ECG data. Biorthogonal wavelet transform of the input ECG signal is first calculated using a modified demand based filter bank architecture which consists of a series combination of three lowpass filters with a highpass filter. Lowpass and highpass filters are realized using a linear phase structure which reduces the hardware cost of the proposed design approximately by 50%. Then, the location of the R-peak is found by comparing the denoised ECG signal with the threshold value. The proposed R-peak detector achieves the highest sensitivity and positive predictivity of 99.75 and 99.98 respectively with the MIT-BIH arrhythmia database. Also, the proposed R-peak detector achieves a comparatively low data error rate (DER) of 0.002. The use of RLE for the compression of detected ECG data achieves a higher compression ratio (CR) of 17.1. To justify the effectiveness of the proposed algorithm, the results have been compared with the existing methods, like Huffman coding/simple predictor, Huffman coding/adaptive, and slope predictor/fixed length packaging.

3D Finite Element Electrical Model of Larval Zebrafish ECG Signals

PubMed Central

Crowcombe, James; Dhillon, Sundeep Singh; Hurst, Rhiannon Mary; Egginton, Stuart; Müller, Ferenc; Sík, Attila; Tarte, Edward

2016-01-01

Assessment of heart function in zebrafish larvae using electrocardiography (ECG) is a potentially useful tool in developing cardiac treatments and the assessment of drug therapies. In order to better understand how a measured ECG waveform is related to the structure of the heart, its position within the larva and the position of the electrodes, a 3D model of a 3 days post fertilisation (dpf) larval zebrafish was developed to simulate cardiac electrical activity and investigate the voltage distribution throughout the body. The geometry consisted of two main components; the zebrafish body was modelled as a homogeneous volume, while the heart was split into five distinct regions (sinoatrial region, atrial wall, atrioventricular band, ventricular wall and heart chambers). Similarly, the electrical model consisted of two parts with the body described by Laplace’s equation and the heart using a bidomain ionic model based upon the Fitzhugh-Nagumo equations. Each region of the heart was differentiated by action potential (AP) parameters and activation wave conduction velocities, which were fitted and scaled based on previously published experimental results. ECG measurements in vivo at different electrode recording positions were then compared to the model results. The model was able to simulate action potentials, wave propagation and all the major features (P wave, R wave, T wave) of the ECG, as well as polarity of the peaks observed at each position. This model was based upon our current understanding of the structure of the normal zebrafish larval heart. Further development would enable us to incorporate features associated with the diseased heart and hence assist in the interpretation of larval zebrafish ECGs in these conditions. PMID:27824910

A ECG Signal Gathering and Displaying System Based on AVR

NASA Astrophysics Data System (ADS)

Ning, Li; Ruilan, Zhang; Jian, Liu; Xiaochen, Wang; Shuying, Chen; Zhuolin, Lang

2017-12-01

This article introduces a kind of system which is based on the AVR to acquire the data of ECG. Such system using the A/D function of ATmega8 chip and the lattice graph LCD to design ECG heart acquisition satisfies the demands above. This design gives a composition of hardware and programming of software about the system in detail which has mainly realized the real-time gathering, the amplifier, the filter, the A/D transformation and the LCD display. Since the AVR includes A/D transformation function and support embedded C language programming, it reduces the peripheral circuit, further more it also decreases the time to design and debug this system.

Electrocardiograms in Low-Risk Patients Undergoing an Annual Health Examination.

PubMed

Bhatia, R Sacha; Bouck, Zachary; Ivers, Noah M; Mecredy, Graham; Singh, Jasjit; Pendrith, Ciara; Ko, Dennis T; Martin, Danielle; Wijeysundera, Harindra C; Tu, Jack V; Wilson, Lynn; Wintemute, Kimberly; Dorian, Paul; Tepper, Joshua; Austin, Peter C; Glazier, Richard H; Levinson, Wendy

2017-09-01

Clinical guidelines advise against routine electrocardiograms (ECG) in low-risk, asymptomatic patients, but the frequency and impact of such ECGs are unknown. To assess the frequency of ECGs following an annual health examination (AHE) with a primary care physician among patients with no known cardiac conditions or risk factors, to explore factors predictive of receiving an ECG in this clinical scenario, and to compare downstream cardiac testing and clinical outcomes in low-risk patients who did and did not receive an ECG after their AHE. A population-based retrospective cohort study using administrative health care databases from Ontario, Canada, between 2010/2011 and 2014/2015 to identify low-risk primary care patients and to assess the subsequent outcomes of interest in this time frame. All patients 18 years or older who had no prior cardiac medical history or risk factors who received an AHE. Receipt of an ECG within 30 days of an AHE. Primary outcome was receipt of downstream cardiac testing or consultation with a cardiologist. Secondary outcomes were death, hospitalization, and revascularization at 12 months. A total of 3 629 859 adult patients had at least 1 AHE between fiscal years 2010/2011 and 2014/2015. Of these patients, 21.5% had an ECG within 30 days after an AHE. The proportion of patients receiving an ECG after an AHE varied from 1.8% to 76.1% among 679 primary care practices (coefficient of quartile dispersion [CQD], 0.50) and from 1.1% to 94.9% among 8036 primary care physicians (CQD, 0.54). Patients who had an ECG were significantly more likely to receive additional cardiac tests, visits, or procedures than those who did not (odds ratio [OR], 5.14; 95% CI, 5.07-5.21; P < .001). The rates of death (0.19% vs 0.16%), cardiac-related hospitalizations (0.46% vs 0.12%), and coronary revascularizations (0.20% vs 0.04%) were low in both the ECG and non-ECG cohorts. Despite recommendations to the contrary, ECG testing after an AHE is relatively common, with significant variation among primary care physicians. Routine ECG testing seems to increase risk for a subsequent cardiology testing and consultation cascade, even though the overall cardiac event rate in both groups was very low.

A new mobile phone-based ECG monitoring system.

PubMed

Iwamoto, Junichi; Yonezawa, Yoshiharu; Ogawa, Hiromichi Maki Hidekuni; Ninomiya, Ishio; Sada, Kouji; Hamada, Shingo; Hahn, Allen W; Caldwell, W Morton

2007-01-01

We have developed a system for monitoring a patient's electrocardiogram (ECG) and movement during daily activities. The complete system is mounted on chest electrodes and continuously samples the ECG and three axis accelerations. When the patient feels a heart discomfort, he or she pushes the data transmission switch on the recording system and the system sends the recorded ECG waveforms and three axis accelerations of the two prior minutes, and for two minutes after the switch is pressed. The data goes directly to a hospital server computer via a 2.4 GHz low power mobile phone. These data are stored on a server computer and downloaded to the physician's Java mobile phone. The physician can display the data on the phone's liquid crystal display.

Cardiac abnormality prediction using HMLP network

NASA Astrophysics Data System (ADS)

Adnan, Ja'afar; Ahmad, K. A.; Mat, Muhamad Hadzren; Rizman, Zairi Ismael; Ahmad, Shahril

2018-02-01

Cardiac abnormality often occurs regardless of gender, age and races but depends on the lifestyle. This problem sometimes does not show any symptoms and usually detected once it already critical which lead to a sudden death to the patient. Basically, cardiac abnormality is the irregular electrical signal that generate by the pacemaker of the heart. This paper attempts to develop a program that can detect cardiac abnormality activity through implementation of Hybrid Multilayer Perceptron (HMLP) network. A certain amount of data of the heartbeat signals from the electrocardiogram (ECG) will be used in this project to train the MLP and HMLP network by using Modified Recursive Prediction Error (MRPE) algorithm and to test the network performance.

Electrical performance of PEDOT:PSS-based textile electrodes for wearable ECG monitoring: a comparative study.

PubMed

Castrillón, Reinel; Pérez, Jairo J; Andrade-Caicedo, Henry

2018-04-02

Wearable textile electrodes for the detection of biopotentials are a promising tool for the monitoring and early diagnosis of chronic diseases. We present a comparative study of the electrical characteristics of four textile electrodes manufactured from common fabrics treated with a conductive polymer, a commercial fabric, and disposable Ag/AgCl electrodes. These characteristics will allow identifying the performance of the materials when used as ECG electrodes. The electrodes were subjected to different electrical tests, and complemented with conductivity calculations and microscopic images to determine their feasibility in the detection of ECG signals. We evaluated four electrical characteristics: contact impedance, electrode polarization, noise, and long-term performance. We analyzed PEDOT:PSS treated fabrics based on cotton, cotton-polyester, lycra and polyester; also a commercial fabric made of silver-plated nylon Shielde® Med-Tex P130, and commercial Ag/AgCl electrodes. We calculated conductivity from the surface resistance and, analyzed their surface at a microscopic level. Rwizard was used in the statistical analysis. The results showed that textile electrodes treated with PEDOT:PSS are suitable for the detection of ECG signals. The error detecting features of the ECG signal was lower than 2% and the electrodes kept working properly after 36 h of continuous use. Even though the contact impedance and the polarization level in textile electrodes were greater than in commercial electrodes, these parameters did not affect the acquisition of the ECG signals. Fabrics conductivity calculations were consistent to the contact impedance.

Proposed In-Training Electrocardiogram Interpretation Competencies for Undergraduate and Postgraduate Trainees.

PubMed

Antiperovitch, Pavel; Zareba, Wojciech; Steinberg, Jonathan S; Bacharova, Ljuba; Tereshchenko, Larisa G; Farre, Jeronimo; Nikus, Kjell; Ikeda, Takanori; Baranchuk, Adrian

2018-03-01

Despite its importance in everyday clinical practice, the ability of physicians to interpret electrocardiograms (ECGs) is highly variable. ECG patterns are often misdiagnosed, and electrocardiographic emergencies are frequently missed, leading to adverse patient outcomes. Currently, many medical education programs lack an organized curriculum and competency assessment to ensure trainees master this essential skill. ECG patterns that were previously mentioned in literature were organized into groups from A to D based on their clinical importance and distributed among levels of training. Incremental versions of this organization were circulated among members of the International Society of Electrocardiology and the International Society of Holter and Noninvasive Electrocardiology until complete consensus was reached. We present reasonably attainable ECG interpretation competencies for undergraduate and postgraduate trainees. Previous literature suggests that methods of teaching ECG interpretation are less important and can be selected based on the available resources of each education program and student preference. The evidence clearly favors summative trainee evaluation methods, which would facilitate learning and ensure that appropriate competencies are acquired. Resources should be allocated to ensure that every trainee reaches their training milestones and should ensure that no electrocardiographic emergency (class A condition) is ever missed. We hope that these guidelines will inform medical education programs and encourage them to allocate sufficient resources and develop organized curricula. Assessments must be in place to ensure trainees acquire the level-appropriate ECG interpretation skills that are required for safe clinical practice. © 2017 Society of Hospital Medicine.

An efficient coding algorithm for the compression of ECG signals using the wavelet transform.

PubMed

Rajoub, Bashar A

2002-04-01

A wavelet-based electrocardiogram (ECG) data compression algorithm is proposed in this paper. The ECG signal is first preprocessed, the discrete wavelet transform (DWT) is then applied to the preprocessed signal. Preprocessing guarantees that the magnitudes of the wavelet coefficients be less than one, and reduces the reconstruction errors near both ends of the compressed signal. The DWT coefficients are divided into three groups, each group is thresholded using a threshold based on a desired energy packing efficiency. A binary significance map is then generated by scanning the wavelet decomposition coefficients and outputting a binary one if the scanned coefficient is significant, and a binary zero if it is insignificant. Compression is achieved by 1) using a variable length code based on run length encoding to compress the significance map and 2) using direct binary representation for representing the significant coefficients. The ability of the coding algorithm to compress ECG signals is investigated, the results were obtained by compressing and decompressing the test signals. The proposed algorithm is compared with direct-based and wavelet-based compression algorithms and showed superior performance. A compression ratio of 24:1 was achieved for MIT-BIH record 117 with a percent root mean square difference as low as 1.08%.

[Detection of Heart Rate of Fetal ECG Based on STFT and BSS].

PubMed

Wang, Xu; Cai, Kun

2016-01-01

Changes in heart rate of fetal is function regulating performance of the circulatory system and the central nervous system, it is significant to detect heart rate of fetus in perinatal fetal. This paper puts forward the fetal heart rate detection method based on short time Fourier transform and blind source separation. First of all, the mixed ECG signal was preprocessed, and then the wavelet transform technique was used to separate the fetal ECG signal with noise from mixed ECG signal, after that, the short-time Fourier transform and the blind separation were carried on it, and then calculated the correlation coefficient of it, Finally, An independent component that it has strongest correlation with the original signal was selected to make FECG peak detection and calculated the fetal instantaneous heart rate. The experimental results show that the method can improve the detection rate of the FECG peak (R), and it has high accuracy in fixing peak(R) location in the case of low signal-noise ratio.

Characterization of dry biopotential electrodes.

PubMed

Xie, Li; Yang, Geng; Xu, Linlin; Seoane, Fernando; Chen, Qiang; Zheng, Lirong

2013-01-01

Driven by the increased interest in wearable long-term healthcare monitoring systems, varieties of dry electrodes are proposed based on different materials with different patterns and structures. Most of the studies reported in the literature focus on proposing new electrodes and comparing its performance with commercial electrodes. Few papers are about detailed comparison among different dry electrodes. In this paper, printed metal-plate electrodes, textile based electrodes, and spiked electrodes are for the first time evaluated and compared under the same experimental setup. The contact impedance and noise characterization are measured. The in-vivo electrocardiogram (ECG) measurement is applied to evaluate the overall performance of different electrodes. Textile electrodes and printed electrodes gain comparable high-quality ECG signals. The ECG signal obtained by spiked electrodes is noisier. However, a clear ECG envelope can be observed and the signal quality can be easily improved by backend signal processing. The features of each type of electrodes are analyzed and the suitable application scenario is addressed.

Labview Based ECG Patient Monitoring System for Cardiovascular Patient Using SMTP Technology.

PubMed

Singh, Om Prakash; Mekonnen, Dawit; Malarvili, M B

2015-01-01

This paper leads to developing a Labview based ECG patient monitoring system for cardiovascular patient using Simple Mail Transfer Protocol technology. The designed device has been divided into three parts. First part is ECG amplifier circuit, built using instrumentation amplifier (AD620) followed by signal conditioning circuit with the operation amplifier (lm741). Secondly, the DAQ card is used to convert the analog signal into digital form for the further process. Furthermore, the data has been processed in Labview where the digital filter techniques have been implemented to remove the noise from the acquired signal. After processing, the algorithm was developed to calculate the heart rate and to analyze the arrhythmia condition. Finally, SMTP technology has been added in our work to make device more communicative and much more cost-effective solution in telemedicine technology which has been key-problem to realize the telediagnosis and monitoring of ECG signals. The technology also can be easily implemented over already existing Internet.

Labview Based ECG Patient Monitoring System for Cardiovascular Patient Using SMTP Technology

PubMed Central

Singh, Om Prakash; Mekonnen, Dawit; Malarvili, M. B.

2015-01-01

This paper leads to developing a Labview based ECG patient monitoring system for cardiovascular patient using Simple Mail Transfer Protocol technology. The designed device has been divided into three parts. First part is ECG amplifier circuit, built using instrumentation amplifier (AD620) followed by signal conditioning circuit with the operation amplifier (lm741). Secondly, the DAQ card is used to convert the analog signal into digital form for the further process. Furthermore, the data has been processed in Labview where the digital filter techniques have been implemented to remove the noise from the acquired signal. After processing, the algorithm was developed to calculate the heart rate and to analyze the arrhythmia condition. Finally, SMTP technology has been added in our work to make device more communicative and much more cost-effective solution in telemedicine technology which has been key-problem to realize the telediagnosis and monitoring of ECG signals. The technology also can be easily implemented over already existing Internet. PMID:27006940

Ultrasound measurement of the brachial artery flow-mediated dilation without ECG gating.

PubMed

Gemignani, Vincenzo; Bianchini, Elisabetta; Faita, Francesco; Giannarelli, Chiara; Plantinga, Yvonne; Ghiadoni, Lorenzo; Demi, Marcello

2008-03-01

The methods commonly used for noninvasive ultrasound assessment of endothelium-dependent flow-mediated dilation (FMD) require an electrocardiogram (ECG) signal to synchronize the measurements with the cardiac cycle. In this article, we present a method for assessing FMD that does not require ECG gating. The approach is based on temporal filtering of the diameter-time curve, which is obtained by means of a B-mode image processing system. The method was tested on 22 healthy volunteers without cardiovascular risk factors. The measurements obtained with the proposed approach were compared with those obtained with ECG gating and with both systolic and end-diastolic measurements. Results showed good agreement between the methods and a higher precision of the new method due to the fact that it is based on a larger number of measurements. Further advantages were also found both in terms of reliability of the measure and simplification of the instrumentation. (E-mail: gemi@ifc.cnr.it).

Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea.

PubMed

de Chazal, Philip; Heneghan, Conor; Sheridan, Elaine; Reilly, Richard; Nolan, Philip; O'Malley, Mark

2003-06-01

A method for the automatic processing of the electrocardiogram (ECG) for the detection of obstructive apnoea is presented. The method screens nighttime single-lead ECG recordings for the presence of major sleep apnoea and provides a minute-by-minute analysis of disordered breathing. A large independently validated database of 70 ECG recordings acquired from normal subjects and subjects with obstructive and mixed sleep apnoea, each of approximately eight hours in duration, was used throughout the study. Thirty-five of these recordings were used for training and 35 retained for independent testing. A wide variety of features based on heartbeat intervals and an ECG-derived respiratory signal were considered. Classifiers based on linear and quadratic discriminants were compared. Feature selection and regularization of classifier parameters were used to optimize classifier performance. Results show that the normal recordings could be separated from the apnoea recordings with a 100% success rate and a minute-by-minute classification accuracy of over 90% is achievable.

A PDA-based electrocardiogram/blood pressure telemonitor for telemedicine.

PubMed

Bolanos, Marcos; Nazeran, Homayoun; Gonzalez, Izzac; Parra, Ricardo; Martinez, Christopher

2004-01-01

An electrocardiogram (ECG) / blood pressure (BP) telemonitor consisting of comprehensive integration of various electrical engineering concepts, devices, and methods was developed. This personal digital assistant-based (PDAbased) system focused on integration of biopotential amplifiers, photoplethysmographic measurement of blood pressure, microcontroller devices, programming methods, wireless transmission, signal filtering and analysis, interfacing, and long term memory devices (24 hours) to develop a state-of-the-art ECG/BP telemonitor. These instrumentation modules were developed and tested to realize a complete and compact system that could be deployed to assist in telemedicine applications and heart rate variability studies. The specific objective of this device was to facilitate the long term monitoring and recording of ECG and blood pressure signals. This device was able to acquire ECG/BP waveforms, transmit them wirelessly to a PDA, save them onto a compact flash memory, and display them on the LCD screen of the PDA. It was also capable of calculating the heart rate (HR) in beats per minute, and providing systolic and diastolic blood pressure values.

Educational technology improves ECG interpretation of acute myocardial infarction among medical students and emergency medicine residents.

PubMed

Pourmand, Ali; Tanski, Mary; Davis, Steven; Shokoohi, Hamid; Lucas, Raymond; Zaver, Fareen

2015-01-01

Asynchronous online training has become an increasingly popular educational format in the new era of technology-based professional development. We sought to evaluate the impact of an online asynchronous training module on the ability of medical students and emergency medicine (EM) residents to detect electrocardiogram (ECG) abnormalities of an acute myocardial infarction (AMI). We developed an online ECG training and testing module on AMI, with emphasis on recognizing ST elevation myocardial infarction (MI) and early activation of cardiac catheterization resources. Study participants included senior medical students and EM residents at all post-graduate levels rotating in our emergency department (ED). Participants were given a baseline set of ECGs for interpretation. This was followed by a brief interactive online training module on normal ECGs as well as abnormal ECGs representing an acute MI. Participants then underwent a post-test with a set of ECGs in which they had to interpret and decide appropriate intervention including catheterization lab activation. 148 students and 35 EM residents participated in this training in the 2012-2013 academic year. Students and EM residents showed significant improvements in recognizing ECG abnormalities after taking the asynchronous online training module. The mean score on the testing module for students improved from 5.9 (95% CI [5.7-6.1]) to 7.3 (95% CI [7.1-7.5]), with a mean difference of 1.4 (95% CI [1.12-1.68]) (p<0.0001). The mean score for residents improved significantly from 6.5 (95% CI [6.2-6.9]) to 7.8 (95% CI [7.4-8.2]) (p<0.0001). An online interactive module of training improved the ability of medical students and EM residents to correctly recognize the ECG evidence of an acute MI.

Threshold-based system for noise detection in multilead ECG recordings.

PubMed

Jekova, Irena; Krasteva, Vessela; Christov, Ivaylo; Abächerli, Roger

2012-09-01

This paper presents a system for detection of the most common noise types seen on the electrocardiogram (ECG) in order to evaluate whether an episode from 12-lead ECG is reliable for diagnosis. It implements criteria for estimation of the noise corruption level in specific frequency bands, aiming to identify the main sources of ECG quality disruption, such as missing signal or limited dynamics of the QRS components above 4 Hz; presence of high amplitude and steep artifacts seen above 1 Hz; baseline drift estimated at frequencies below 1 Hz; power-line interference in a band ±2 Hz around its central frequency; high-frequency and electromyographic noises above 20 Hz. All noise tests are designed to process the ECG series in the time domain, including 13 adjustable thresholds for amplitude and slope criteria which are evaluated in adjustable time intervals, as well as number of leads. The system allows flexible extension toward application-specific requirements for the noise levels in acceptable quality ECGs. Training of different thresholds' settings to determine different positive noise detection rates is performed with the annotated set of 1000 ECGs from the PhysioNet database created for the Computing in Cardiology Challenge 2011. Two implementations are highlighted on the receiver operating characteristic (area 0.968) to fit to different applications. The implementation with high sensitivity (Se = 98.7%, Sp = 80.9%) appears as a reliable alarm when there are any incidental problems with the ECG acquisition, while the implementation with high specificity (Sp = 97.8%, Se = 81.8%) is less susceptible to transient problems but rather validates noisy ECGs with acceptable quality during a small portion of the recording.

Accuracy of ECG indices for diagnosis of left ventricular hypertrophy in people >65 years: results from the ActiFE study.

PubMed

Laszlo, Roman; Kunz, Katia; Dallmeier, Dhayana; Klenk, Jochen; Denkinger, Michael; Koenig, Wolfgang; Rothenbacher, Dietrich; Steinacker, Juergen Michael

2017-10-01

The detection of left ventricular hypertrophy (LVH) is still a common objective of electrocardiography (ECG) in clinical practice. The aim of our study was to evaluate the accuracy of LVH ECG indices in people older than 65 recruited from a population-based cohort (ActiFE-Ulm study). In 432 subjects (mean age 76.2 ± 5.5 years, 51% male), left ventricular mass was echocardiographically determined (Devereux formula) and indexed (LVMI) to body surface area. Several LVH ECG indices (Lewis voltage, Gubner-Ungerleider voltage, Sokolow-Lyon voltage/product, Cornell voltage/product) were calculated with the help of resting ECG data and compared with the echocardiographic assessment. Despite echocardiographic signs of LVH [LVMI > 115 (♂) or >95 g/m 2 (♀)] in 47.5% of all subjects, diagnostic performance of all ECG indices was generally low. Magnitude of all LVH-indices was mainly predicted by frontal QRS axis in multivariate linear regression analysis. In comparison with the literature data from younger subjects, average frontal QRS axis turned counterclockwise. Most probably, age-related counterclockwise turn of frontal QRS axis is mainly explanatory for the decreased magnitude of LVH ECG indices and consecutive worse diagnostic performance of these indices in the elderly. ECG indices for detection of LVH have insufficient predictive values in geriatric subjects and should therefore not be used clinically for this purpose. Nevertheless, due to its established relevancy in cardiac risk stratification in this age group, usage of some established ECG indices might keep its significance even in the age of modern cardiac imaging.

Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression.

PubMed

Kumar, Ranjeet; Kumar, A; Singh, G K

2016-06-01

In the field of biomedical, it becomes necessary to reduce data quantity due to the limitation of storage in real-time ambulatory system and telemedicine system. Research has been underway since very beginning for the development of an efficient and simple technique for longer term benefits. This paper, presents an algorithm based on singular value decomposition (SVD), and embedded zero tree wavelet (EZW) techniques for ECG signal compression which deals with the huge data of ambulatory system. The proposed method utilizes the low rank matrix for initial compression on two dimensional (2-D) ECG data array using SVD, and then EZW is initiated for final compression. Initially, 2-D array construction has key issue for the proposed technique in pre-processing. Here, three different beat segmentation approaches have been exploited for 2-D array construction using segmented beat alignment with exploitation of beat correlation. The proposed algorithm has been tested on MIT-BIH arrhythmia record, and it was found that it is very efficient in compression of different types of ECG signal with lower signal distortion based on different fidelity assessments. The evaluation results illustrate that the proposed algorithm has achieved the compression ratio of 24.25:1 with excellent quality of signal reconstruction in terms of percentage-root-mean square difference (PRD) as 1.89% for ECG signal Rec. 100 and consumes only 162bps data instead of 3960bps uncompressed data. The proposed method is efficient and flexible with different types of ECG signal for compression, and controls quality of reconstruction. Simulated results are clearly illustrate the proposed method can play a big role to save the memory space of health data centres as well as save the bandwidth in telemedicine based healthcare systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A three-lead, programmable, and microcontroller-based electrocardiogram generator with frequency domain characteristics of heart rate variability.

PubMed

Wei, Ying-Chieh; Wei, Ying-Yu; Chang, Kai-Hsiung; Young, Ming-Shing

2012-04-01

The objective of this study is to design and develop a programmable electrocardiogram (ECG) generator with frequency domain characteristics of heart rate variability (HRV) which can be used to test the efficiency of ECG algorithms and to calibrate and maintain ECG equipment. We simplified and modified the three coupled ordinary differential equations in McSharry's model to a single differential equation to obtain the ECG signal. This system not only allows the signal amplitude, heart rate, QRS-complex slopes, and P- and T-wave position parameters to be adjusted, but can also be used to adjust the very low frequency, low frequency, and high frequency components of HRV frequency domain characteristics. The system can be tuned to function with HRV or not. When the HRV function is on, the average heart rate can be set to a value ranging from 20 to 122 beats per minute (BPM) with an adjustable variation of 1 BPM. When the HRV function is off, the heart rate can be set to a value ranging from 20 to 139 BPM with an adjustable variation of 1 BPM. The amplitude of the ECG signal can be set from 0.0 to 330 mV at a resolution of 0.005 mV. These parameters can be adjusted either via input through a keyboard or through a graphical user interface (GUI) control panel that was developed using LABVIEW. The GUI control panel depicts a preview of the ECG signal such that the user can adjust the parameters to establish a desired ECG morphology. A complete set of parameters can be stored in the flash memory of the system via a USB 2.0 interface. Our system can generate three different types of synthetic ECG signals for testing the efficiency of an ECG algorithm or calibrating and maintaining ECG equipment. © 2012 American Institute of Physics

Automated detection of ventricular pre-excitation in pediatric 12-lead ECG.

PubMed

Gregg, Richard E; Zhou, Sophia H; Dubin, Anne M

2016-01-01

With increased interest in screening of young people for potential causes of sudden death, accurate automated detection of ventricular pre-excitation (VPE) or Wolff-Parkinson-White syndrome (WPW) in the pediatric resting ECG is important. Several recent studies have shown interobserver variability when reading screening ECGs and thus an accurate automated reading for this potential cause of sudden death is critical. We designed and tested an automated algorithm to detect pediatric VPE optimized for low prevalence. Digital ECGs with 12 leads or 15 leads (12-lead plus V3R, V4R and V7) were selected from multiple hospitals and separated into a testing and training database. Inclusion criterion was age less than 16 years. The reference for algorithm detection of VPE was cardiologist annotation of VPE for each ECG. The training database (n=772) consisted of VPE ECGs (n=37), normal ECGs (n=492) and a high concentration of conduction defects, RBBB (n=232) and LBBB (n=11). The testing database was a random sample (n=763). All ECGs were analyzed with the Philips DXL ECG Analysis algorithm for basic waveform measurements. Additional ECG features specific to VPE, mainly delta wave scoring, were calculated from the basic measurements and the average beat. A classifier based on decision tree bootstrap aggregation (tree bagger) was trained in multiple steps to select the number of decision trees and the 10 best features. The classifier accuracy was measured on the test database. The new algorithm detected pediatric VPE with a sensitivity of 78%, a specificity of 99.9%, a positive predictive value of 88% and negative predictive value of 99.7%. This new algorithm for detection of pediatric VPE performs well with a reasonable positive and negative predictive value despite the low prevalence in the general population. Copyright © 2016 Elsevier Inc. All rights reserved.

A three-lead, programmable, and microcontroller-based electrocardiogram generator with frequency domain characteristics of heart rate variability

NASA Astrophysics Data System (ADS)

Wei, Ying-Chieh; Wei, Ying-Yu; Chang, Kai-Hsiung; Young, Ming-Shing

2012-04-01

The objective of this study is to design and develop a programmable electrocardiogram (ECG) generator with frequency domain characteristics of heart rate variability (HRV) which can be used to test the efficiency of ECG algorithms and to calibrate and maintain ECG equipment. We simplified and modified the three coupled ordinary differential equations in McSharry's model to a single differential equation to obtain the ECG signal. This system not only allows the signal amplitude, heart rate, QRS-complex slopes, and P- and T-wave position parameters to be adjusted, but can also be used to adjust the very low frequency, low frequency, and high frequency components of HRV frequency domain characteristics. The system can be tuned to function with HRV or not. When the HRV function is on, the average heart rate can be set to a value ranging from 20 to 122 beats per minute (BPM) with an adjustable variation of 1 BPM. When the HRV function is off, the heart rate can be set to a value ranging from 20 to 139 BPM with an adjustable variation of 1 BPM. The amplitude of the ECG signal can be set from 0.0 to 330 mV at a resolution of 0.005 mV. These parameters can be adjusted either via input through a keyboard or through a graphical user interface (GUI) control panel that was developed using LABVIEW. The GUI control panel depicts a preview of the ECG signal such that the user can adjust the parameters to establish a desired ECG morphology. A complete set of parameters can be stored in the flash memory of the system via a USB 2.0 interface. Our system can generate three different types of synthetic ECG signals for testing the efficiency of an ECG algorithm or calibrating and maintaining ECG equipment.

Seismocardiography-Based Cardiac Computed Tomography Gating Using Patient-Specific Template Identification and Detection

PubMed Central

Yao, Jingting; Tridandapani, Srini; Wick, Carson A.

2017-01-01

To more accurately trigger cardiac computed tomography angiography (CTA) than electrocardiography (ECG) alone, a sub-system is proposed as an intermediate step toward fusing ECG with seismocardiography (SCG). Accurate prediction of quiescent phases is crucial to prospectively gating CTA, which is susceptible to cardiac motion and, thus, can affect the diagnostic quality of images. The key innovation of this sub-system is that it identifies the SCG waveform corresponding to heart sounds and determines their phases within the cardiac cycles. Furthermore, this relationship is modeled as a linear function with respect to heart rate. For this paper, B-mode echocardiography is used as the gold standard for identifying the quiescent phases. We analyzed synchronous ECG, SCG, and echocardiography data acquired from seven healthy subjects (mean age: 31; age range: 22–48; males: 4) and 11 cardiac patients (mean age: 56; age range: 31–78; males: 6). On average, the proposed algorithm was able to successfully identify 79% of the SCG waveforms in systole and 68% in diastole. The simulated results show that SCG-based prediction produced less average phase error than that of ECG. It was found that the accuracy of ECG-based gating is more susceptible to increases in heart rate variability, while SCG-based gating is susceptible to high cycle to cycle variability in morphology. This pilot work of prediction using SCG waveforms enriches the framework of a comprehensive system with multiple modalities that could potentially, in real time, improve the image quality of CTA. PMID:28845370

Response of the ECG to short-term diuresis in patients with heart failure.

PubMed

Madias, John E; Song, Jessica; White, C Michael; Kalus, James S; Kluger, Jeffrey

2005-07-01

Increase in the amplitude of electrocardiogram (ECG) QRS complexes has been observed in patients treated for heart failure (HF), but the underlying mechanism has not been delineated. Also, correlation of augmentation of the QRS potentials with loss of weight has been noted in patients recovering from anasarca of varying etiology, or after hemodialysis. We assessed the effect of diuresis-based fluid loss in patients treated for HF on the amplitude of ECG QRS complexes. This is a cohort study based on ECG and other data from a previously published investigation of patients with HF conducted at a university affiliated hospital, which used new measurements and analysis, performed by a totally blinded investigator based at another institution. Twenty-one patients (10 men) aged 70.5+/-12.7 years, 13 with ischemic, and 8 with nonischemic cardiomyopathy, were admitted to the hospital for management of exacerbated HF and were observed for 48 hours. The patients received diuresis, and had routine laboratory testing, documentation of the net fluid lost, and recording of ECGs prior to the initiation of therapy and at 24 and 48 hours. Percent change (%Delta) over the course of observation in the sums of the amplitude of QRS complexes from 12 leads (SigmaQRS12), 6-limb leads (SigmaQRS6), and leads 1+2 (SigmaQRS2) in mm of standard ECGs were correlated with net fluid loss corrected for admission weight in mL/kg. Fluid loss amounted to 3204.9+/-1399.5 mL in the course of 40+/-23 hours of diuresis. SigmaQRS12 was 160.9+/-42.3 mm before and 170.0+/-50.7 mm after diuresis (P=0. 024). Percent change in SigmaQRS12, SigmaQRS6, and SigmaQRS2 correlated well with the net fluid loss (r=-0.70, -0.82, -0.61, and P=0.002, 0.0005, 0.001) correspondingly. Changes in sums of the amplitude of QRS complexes of the standard ECG correlates well with net fluid loss in response to short-term diuresis in patients with HF. Change in the SigmaQRS12, SigmaQRS6, and SigmaQRS2 from ECGs before and after diuresis can be used as an easily obtainable and universally available bedside index of the net fluid loss experienced by bedridden patients with HF undergoing therapy.

Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features

PubMed Central

Dandapat, Samarendra

2017-01-01

The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques. PMID:28894589

Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems.

PubMed

Elgendi, Mohamed; Eskofier, Björn; Dokos, Socrates; Abbott, Derek

2014-01-01

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.

Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems

PubMed Central

Elgendi, Mohamed; Eskofier, Björn; Dokos, Socrates; Abbott, Derek

2014-01-01

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices. PMID:24409290

Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies

NASA Astrophysics Data System (ADS)

Kather, Jakob Nikolas; Hermann, Thomas; Bukschat, Yannick; Kramer, Tilmann; Schad, Lothar R.; Zöllner, Frank Gerrit

2017-03-01

Electrocardiography (ECG) data are multidimensional temporal data with ubiquitous applications in the clinic. Conventionally, these data are presented visually. It is presently unclear to what degree data sonification (auditory display), can enable the detection of clinically relevant cardiac pathologies in ECG data. In this study, we introduce a method for polyphonic sonification of ECG data, whereby different ECG channels are simultaneously represented by sound of different pitch. We retrospectively applied this method to 12 samples from a publicly available ECG database. We and colleagues from our professional environment then analyzed these data in a blinded way. Based on these analyses, we found that the sonification technique can be intuitively understood after a short training session. On average, the correct classification rate for observers trained in cardiology was 78%, compared to 68% and 50% for observers not trained in cardiology or not trained in medicine at all, respectively. These values compare to an expected random guessing performance of 25%. Strikingly, 27% of all observers had a classification accuracy over 90%, indicating that sonification can be very successfully used by talented individuals. These findings can serve as a baseline for potential clinical applications of ECG sonification.

An Interoperable System toward Cardiac Risk Stratification from ECG Monitoring

PubMed Central

Mora-Jiménez, Inmaculada; Ramos-López, Javier; Quintanilla Fernández, Teresa; García-García, Antonio; Díez-Mazuela, Daniel; García-Alberola, Arcadi

2018-01-01

Many indices have been proposed for cardiovascular risk stratification from electrocardiogram signal processing, still with limited use in clinical practice. We created a system integrating the clinical definition of cardiac risk subdomains from ECGs and the use of diverse signal processing techniques. Three subdomains were defined from the joint analysis of the technical and clinical viewpoints. One subdomain was devoted to demographic and clinical data. The other two subdomains were intended to obtain widely defined risk indices from ECG monitoring: a simple-domain (heart rate turbulence (HRT)), and a complex-domain (heart rate variability (HRV)). Data provided by the three subdomains allowed for the generation of alerts with different intensity and nature, as well as for the grouping and scrutinization of patients according to the established processing and risk-thresholding criteria. The implemented system was tested by connecting data from real-world in-hospital electronic health records and ECG monitoring by considering standards for syntactic (HL7 messages) and semantic interoperability (archetypes based on CEN/ISO EN13606 and SNOMED-CT). The system was able to provide risk indices and to generate alerts in the health records to support decision-making. Overall, the system allows for the agile interaction of research and clinical practice in the Holter-ECG-based cardiac risk domain. PMID:29494497

Automatic detection of respiration rate from ambulatory single-lead ECG.

PubMed

Boyle, Justin; Bidargaddi, Niranjan; Sarela, Antti; Karunanithi, Mohan

2009-11-01

Ambulatory electrocardiography is increasingly being used in clinical practice to detect abnormal electrical behavior of the heart during ordinary daily activities. The utility of this monitoring can be improved by deriving respiration, which previously has been based on overnight apnea studies where patients are stationary, or the use of multilead ECG systems for stress testing. We compared six respiratory measures derived from a single-lead portable ECG monitor with simultaneously measured respiration air flow obtained from an ambulatory nasal cannula respiratory monitor. Ten controlled 1-h recordings were performed covering activities of daily living (lying, sitting, standing, walking, jogging, running, and stair climbing) and six overnight studies. The best method was an average of a 0.2-0.8 Hz bandpass filter and RR technique based on lengthening and shortening of the RR interval. Mean error rates with the reference gold standard were +/-4 breaths per minute (bpm) (all activities), +/-2 bpm (lying and sitting), and +/-1 breath per minute (overnight studies). Statistically similar results were obtained using heart rate information alone (RR technique) compared to the best technique derived from the full ECG waveform that simplifies data collection procedures. The study shows that respiration can be derived under dynamic activities from a single-lead ECG without significant differences from traditional methods.

Bluetooth telemetry system for a wearable electrocardiogram

NASA Astrophysics Data System (ADS)

Green, Ryan B.

The rise of wireless networks has led to a new market in medicine: remote patient monitoring. Practitioners now desire to monitor the health conditions of their patients after hospital release. With the large number of cardiac related deaths and this new demand in medicine being the motivation, this study developed a BluetoothRTM telemetry system for a wearable Electrocardiogram. This study also developed a compression t-shirt to hold the ECG and telemetry system. This device communicates the ECG signal of a patient to an Android device within the ISM frequency bands (2.4-2.48 GHz) where the data is displayed and stored in real time. This study is a stepping stone toward more portable heart monitoring that can communicate with the doctor in real time from remote locations.

Validation of PC-based Sound Card with Biopac for Digitalization of ECG Recording in Short-term HRV Analysis.

PubMed

Maheshkumar, K; Dilara, K; Maruthy, K N; Sundareswaren, L

2016-07-01

Heart rate variability (HRV) analysis is a simple and noninvasive technique capable of assessing autonomic nervous system modulation on heart rate (HR) in healthy as well as disease conditions. The aim of the present study was to compare (validate) the HRV using a temporal series of electrocardiograms (ECG) obtained by simple analog amplifier with PC-based sound card (audacity) and Biopac MP36 module. Based on the inclusion criteria, 120 healthy participants, including 72 males and 48 females, participated in the present study. Following standard protocol, 5-min ECG was recorded after 10 min of supine rest by Portable simple analog amplifier PC-based sound card as well as by Biopac module with surface electrodes in Leads II position simultaneously. All the ECG data was visually screened and was found to be free of ectopic beats and noise. RR intervals from both ECG recordings were analyzed separately in Kubios software. Short-term HRV indexes in both time and frequency domain were used. The unpaired Student's t-test and Pearson correlation coefficient test were used for the analysis using the R statistical software. No statistically significant differences were observed when comparing the values analyzed by means of the two devices for HRV. Correlation analysis revealed perfect positive correlation (r = 0.99, P < 0.001) between the values in time and frequency domain obtained by the devices. On the basis of the results of the present study, we suggest that the calculation of HRV values in the time and frequency domains by RR series obtained from the PC-based sound card is probably as reliable as those obtained by the gold standard Biopac MP36.

Portable electrocardiogram device using Android smartphone.

PubMed

Brucal, S G E; Clamor, G K D; Pasiliao, L A O; Soriano, J P F; Varilla, L P M

2016-08-01

Portable electrocardiogram (ECG) capturing device can be interfaced to a smart phone installed with an android-based application (app). This app processes and analyses the data sent by the device to provide an interpretation of the patient/user's heart current condition (e.g.: beats per minute, heart signal waveform, R-R interval). The ECG recorded by the app is stored in the smart phone's Secure Digital (SD) card and cloud storage which can be accessed remotely by a physician to aid in providing medical diagnosis. The project aims to help patients living at a far distance from hospitals and experience difficulty in consulting their physician for regular check-ups, and assist doctors in regularly monitoring their patient's heart condition. The hardware data acquisition device and software application were subjected to trials in a clinic with volunteer-patients to measure the ECG and heart rate, data saving speed on the SD card, success rate of the saved data and uploaded file. Different ECG tests using the project prototype were done for 12 patients/users and yielded a reading difference of 7.61% in an R-R interval reading and 5.35% in heart rate reading as compared with the cardiologist's conventional 12-electrode ECG machine. Using the developed ECG device, it took less than 5 seconds to save ECG reading using SD card and approximately 2 minutes to upload via cloud.

Real-Time 12-Lead High-Frequency QRS Electrocardiography for Enhanced Detection of Myocardial Ischemia and Coronary Artery Disease

NASA Technical Reports Server (NTRS)

Schlegel, Todd T.; Kulecz, Walter B.; DePalma, Jude L.; Feiveson, Alan H.; Wilson, John S.; Rahman, M. Atiar; Bungo, Michael W.

2004-01-01

Several studies have shown that diminution of the high-frequency (HF; 150-250 Hz) components present within the central portion of the QRS complex of an electrocardiogram (ECG) is a more sensitive indicator for the presence of myocardial ischemia than are changes in the ST segments of the conventional low-frequency ECG. However, until now, no device has been capable of displaying, in real time on a beat-to-beat basis, changes in these HF QRS ECG components in a continuously monitored patient. Although several software programs have been designed to acquire the HF components over the entire QRS interval, such programs have involved laborious off-line calculations and postprocessing, limiting their clinical utility. We describe a personal computer-based ECG software program developed recently at the National Aeronautics and Space Administration (NASA) that acquires, analyzes, and displays HF QRS components in each of the 12 conventional ECG leads in real time. The system also updates these signals and their related derived parameters in real time on a beat-to-beat basis for any chosen monitoring period and simultaneously displays the diagnostic information from the conventional (low-frequency) 12-lead ECG. The real-time NASA HF QRS ECG software is being evaluated currently in multiple clinical settings in North America. We describe its potential usefulness in the diagnosis of myocardial ischemia and coronary artery disease.

What adult electrocardiogram (ECG) diagnoses and/or findings do residents in emergency medicine need to know?

PubMed

Patocka, Catherine; Turner, Joel; Wiseman, Jeffrey

2015-11-01

There is no evidence-based description of electrocardiogram (ECG) interpretation competencies for emergency medicine (EM) trainees. The first step in defining these competencies is to develop a prioritized list of adult ECG findings relevant to EM contexts. The purpose of this study was to categorize the importance of various adult ECG diagnoses and/or findings for the EM trainee. We developed a list of potentially important adult ECG diagnoses/findings and conducted a Delphi opinion-soliciting process. Participants used a 4-point Likert scale to rate the importance of each diagnosis for EM trainees. Consensus was defined as a minimum of 75% agreement at the second round or later. In the absence of consensus, stability was defined as a shift of 20% or less after successive rounds. A purposive sampling of 22 emergency physicians participated in the Delphi process, and 16 (72%) completed the process. Of those, 15 were from 11 different EM training programs across Canada and one was an expert in EM electrocardiography. Overall, 78 diagnoses reached consensus, 42 achieved stability and one diagnosis achieved neither consensus nor stability. Out of 121 potentially important adult ECG diagnoses, 53 (44%) were considered "must know" diagnoses, 61 (50%) "should know" diagnoses, and 7 (6%) "nice to know" diagnoses. We have categorized adult ECG diagnoses within an EM training context, knowledge of which may allow clinical EM teachers to establish educational priorities. This categorization will also facilitate the development of an educational framework to establish EM trainee competency in ECG interpretation.

Electrocardiographic Characterization of Cardiac Hypertrophy in Mice that Overexpress the ErbB2 Receptor Tyrosine Kinase

PubMed Central

Sysa-Shah, Polina; Sørensen, Lars L; Abraham, M Roselle; Gabrielson, Kathleen L

2015-01-01

Electrocardiography is an important method for evaluation and risk stratification of patients with cardiac hypertrophy. We hypothesized that the recently developed transgenic mouse model of cardiac hypertrophy (ErbB2tg) will display distinct ECG features, enabling WT (wild type) mice to be distinguished from transgenic mice without using conventional PCR genotyping. We evaluated more than 2000 mice and developed specific criteria for genotype determination by using cageside ECG, during which unanesthetized mice were manually restrained for less than 1 min. Compared with those from WT counterparts, the ECG recordings of ErbB2tg mice were characterized by higher P- and R-wave amplitudes, broader QRS complexes, inverted T waves, and ST interval depression. Pearson's correlation matrix analysis of combined WT and ErbB2tg data revealed significant correlation between heart weight and the ECG parameters of QT interval (corrected for heart rate), QRS interval, ST height, R amplitude, P amplitude, and PR interval. In addition, the left ventricular posterior wall thickness as determined by echocardiography correlated with ECG-determined ST height, R amplitude, QRS interval; echocardiographic left ventricular mass correlated with ECG-determined ST height and PR interval. In summary, we have determined phenotypic ECG criteria to differentiate ErbB2tg from WT genotypes in 98.8% of mice. This inexpensive and time-efficient ECG-based phenotypic method might be applied to differentiate between genotypes in other rodent models of cardiac hypertrophy. Furthermore, with appropriate modifications, this method might be translated for use in other species. PMID:26310459

Singularity detection by wavelet approach: application to electrocardiogram signal

NASA Astrophysics Data System (ADS)

Jalil, Bushra; Beya, Ouadi; Fauvet, Eric; Laligant, Olivier

2010-01-01

In signal processing, the region of abrupt changes contains the most of the useful information about the nature of the signal. The region or the points where these changes occurred are often termed as singular point or singular region. The singularity is considered to be an important character of the signal, as it refers to the discontinuity and interruption present in the signal and the main purpose of the detection of such singular point is to identify the existence, location and size of those singularities. Electrocardiogram (ECG) signal is used to analyze the cardiovascular activity in the human body. However the presence of noise due to several reasons limits the doctor's decision and prevents accurate identification of different pathologies. In this work we attempt to analyze the ECG signal with energy based approach and some heuristic methods to segment and identify different signatures inside the signal. ECG signal has been initially denoised by empirical wavelet shrinkage approach based on Steins Unbiased Risk Estimate (SURE). At the second stage, the ECG signal has been analyzed by Mallat approach based on modulus maximas and Lipschitz exponent computation. The results from both approaches has been discussed and important aspects has been highlighted. In order to evaluate the algorithm, the analysis has been done on MIT-BIH Arrhythmia database; a set of ECG data records sampled at a rate of 360 Hz with 11 bit resolution over a 10mv range. The results have been examined and approved by medical doctors.

Association of Age, Sex, Body Size and Ethnicity with Electrocardiographic Values in Community-based Older Asian Adults.

PubMed

Tan, Eugene S J; Yap, Jonathan; Xu, Chang Fen; Feng, Liang; Nyunt, Shwe Zin; Santhanakrishnan, Rajalakshmi; Chan, Michelle M Y; Seow, Swee Chong; Ching, Chi Keong; Yeo, Khung Keong; Richards, A Mark; Ng, Tze Pin; Lim, Toon Wei; Lam, Carolyn S P

2016-07-01

Existing electrocardiographic (ECG) reference values were derived in middle-aged Caucasian adults. We aimed to assess the association of age, sex, body size and ethnicity on ECG parameters in a multi-ethnic Asian population. Resting 12-lead ECG and anthropometric measurements were performed in a community-based cohort of 3777 older Asians (age 64.7±9.1 years, 1467 men, 88.8% Chinese, 7.7% Malay, 3.5% Indian, body mass index [BMI] 24.0±3.9kg/m(2)). Men had longer PR interval, wider QRS, shorter QTc interval and taller SV3. In both sexes, older age was associated with longer PR interval, wider QRS, larger R aVL and more leftward QRS axis, while higher BMI was associated with longer PR interval, wider QRS, larger RaVL and more negative QRS axis. There were significant inter-ethnic differences in QRS duration among men, as well as in PR and QTc intervals among women (all adjusted p<0.05). Findings were similar in a healthy subset of 1158 adults (age 61.2±9.1 years, 365 men) without cardiovascular risk factors. These first community-based ECG data in multi-ethnic older Asians highlight the independent effects of age, sex, body size and ethnicity on ECG parameters. Copyright © 2016 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Published by Elsevier B.V. All rights reserved.

Wireless remote monitoring of myocardial ischemia using reconstructed 12-lead ECGs.

PubMed

Vukcevic, Vladan; Panescu, Dorin; Bojovic, Bosko; George, Samuel; Gussak, Ihor; Giga, Vojislav; Stankovic, Ivana

2010-01-01

CardioBip (CB) is a hand-held patient-activated device for recording and wireless transmission of reconstructed 12-lead ECG (12CB) based on patient specific matrices. It has 5 contact points: 3 precordial and 2 on the device top serving as limb leads when touched by index fingers. To determine whether CB could be used to monitor coronary disease (CAD) patients, we compared 12CB to simultaneous 12-lead ECGs (12L) in patients with CAD, pre-and post-exercise treadmill testing (ETT). The study goals were to assess: (1) whether 12CB can accurately reconstruct and wirelessly transmit 12-lead ECGs in CAD patients during ETT recovery; (2) whether 12CB can be used to evaluate ST segment changes in patients with exercise-induced ischemia.

Design, fabrication and skin-electrode contact analysis of polymer microneedle-based ECG electrodes

NASA Astrophysics Data System (ADS)

O'Mahony, Conor; Grygoryev, Konstantin; Ciarlone, Antonio; Giannoni, Giuseppe; Kenthao, Anan; Galvin, Paul

2016-08-01

Microneedle-based ‘dry’ electrodes have immense potential for use in diagnostic procedures such as electrocardiography (ECG) analysis, as they eliminate several of the drawbacks associated with the conventional ‘wet’ electrodes currently used for physiological signal recording. To be commercially successful in such a competitive market, it is essential that dry electrodes are manufacturable in high volumes and at low cost. In addition, the topographical nature of these emerging devices means that electrode performance is likely to be highly dependent on the quality of the skin-electrode contact. This paper presents a low-cost, wafer-level micromoulding technology for the fabrication of polymeric ECG electrodes that use microneedle structures to make a direct electrical contact to the body. The double-sided moulding process can be used to eliminate post-process via creation and wafer dicing steps. In addition, measurement techniques have been developed to characterize the skin-electrode contact force. We perform the first analysis of signal-to-noise ratio dependency on contact force, and show that although microneedle-based electrodes can outperform conventional gel electrodes, the quality of ECG recordings is significantly dependent on temporal and mechanical aspects of the skin-electrode interface.

Applied Neuroscience at the AFRL 711th Human Performance Wing

DTIC Science & Technology

2010-09-01

Support teaming and collaboration research performed by RHCPT 25 History of Applied Neuroscience Research First EEG studies of workload at AFRL...First to classify mental workload based on integrated EEG /ECG 26 First successful real- time workload classification Measured EEG workload in...complex tasks Closed-loop adaptive aiding based on EEG /ECG History of Applied Neuroscience Research 27 Current Applied Neuroscience Research • Mix of in

Trypanosoma cruzi infection and electrocardiographic findings among active manual workers. A population-based study in central Brazil.

PubMed

Zicker, F; Netto, J C; Zicker, E M; Oliveira, R M; Smith, P G

1990-03-01

In a cross sectional survey of the prevalence of Trypanosoma cruzi infection among urban unskilled workers in Goiânia, Brazil, blood samples from 6222 manual workers from seven institutions were examined for anti-Trypanosoma cruzi antibodies by immunofluorescence, ELISA and haemagglutination tests. ECGs were performed and a clinical history was taken from 624 seropositive and a random sample of 529 seronegative subjects. Abnormal ECGs were found in 15.1% of individuals without Trypanosoma cruzi antibodies and in 44.4% of those with antibodies (p less than 0.001). In general, cardiovascular symptoms reported were not associated with seropositivity nor with ECG alterations but dizziness and dyspnoea were more often reported among those with an abnormal tracing (p less than 0.01). The prevalence of ECG abnormalities increased with age in both groups but was higher among those seropositive in all age groups. An odds ratio of 2.0 (95% Cl 1.2-3.1) and 2.9 (95% Cl 1.5-6.3) of ECG abnormalities, for each decade of life, was estimated for seropositive and seronegative subjects, respectively. Relative risks (based on the odds ratios) for various specific ECG abnormalities, comparing seropositive to seronegative individuals, were calculated after adjustment for age, sex and institution. The odds ratio for complete right bundle branch block was 49.9 (95% CL 12.2-203.4); for left anterior hemiblock was 4.1 (2.8-6.0); for large Q/QS waves was 4.2 (2.4-7.3) and for first degree A-V block was 8.5 (2.6-28.1).

The "Chaos Theory" and nonlinear dynamics in heart rate variability analysis: does it work in short-time series in patients with coronary heart disease?

PubMed

Krstacic, Goran; Krstacic, Antonija; Smalcelj, Anton; Milicic, Davor; Jembrek-Gostovic, Mirjana

2007-04-01

Dynamic analysis techniques may quantify abnormalities in heart rate variability (HRV) based on nonlinear and fractal analysis (chaos theory). The article emphasizes clinical and prognostic significance of dynamic changes in short-time series applied on patients with coronary heart disease (CHD) during the exercise electrocardiograph (ECG) test. The subjects were included in the series after complete cardiovascular diagnostic data. Series of R-R and ST-T intervals were obtained from exercise ECG data after sampling digitally. The range rescaled analysis method determined the fractal dimension of the intervals. To quantify fractal long-range correlation's properties of heart rate variability, the detrended fluctuation analysis technique was used. Approximate entropy (ApEn) was applied to quantify the regularity and complexity of time series, as well as unpredictability of fluctuations in time series. It was found that the short-term fractal scaling exponent (alpha(1)) is significantly lower in patients with CHD (0.93 +/- 0.07 vs 1.09 +/- 0.04; P < 0.001). The patients with CHD had higher fractal dimension in each exercise test program separately, as well as in exercise program at all. ApEn was significant lower in CHD group in both RR and ST-T ECG intervals (P < 0.001). The nonlinear dynamic methods could have clinical and prognostic applicability also in short-time ECG series. Dynamic analysis based on chaos theory during the exercise ECG test point out the multifractal time series in CHD patients who loss normal fractal characteristics and regularity in HRV. Nonlinear analysis technique may complement traditional ECG analysis.

ECG-based 4D-dose reconstruction of cardiac arrhythmia ablation with carbon ion beams: application in a porcine model

NASA Astrophysics Data System (ADS)

Richter, Daniel; Immo Lehmann, H.; Eichhorn, Anna; Constantinescu, Anna M.; Kaderka, Robert; Prall, Matthias; Lugenbiel, Patrick; Takami, Mitsuru; Thomas, Dierk; Bert, Christoph; Durante, Marco; Packer, Douglas L.; Graeff, Christian

2017-09-01

Noninvasive ablation of cardiac arrhythmia by scanned particle radiotherapy is highly promising, but especially challenging due to cardiac and respiratory motion. Irradiations for catheter-free ablation in intact pigs were carried out at the GSI Helmholtz Center in Darmstadt using scanned carbon ions. Here, we present real-time electrocardiogram (ECG) data to estimate time-resolved (4D) delivered dose. For 11 animals, surface ECGs and temporal structure of beam delivery were acquired during irradiation. R waves were automatically detected from surface ECGs. Pre-treatment ECG-triggered 4D-CT phases were synchronized to the R-R interval. 4D-dose calculation was performed using GSI’s in-house 4D treatment planning system. Resulting dose distributions were assessed with respect to coverage (D95 and V95), heterogeneity (HI  =  D5-D95) and normal tissue exposure. Final results shown here were performed offline, but first calculations were started shortly after irradiation The D95 for TV and PTV was above 95% for 10 and 8 out of 11 animals, respectively. HI was reduced for PTV versus TV volumes, especially for some of the animals targeted at the atrioventricular junction, indicating residual interplay effects due to cardiac motion. Risk structure exposure was comparable to static and 4D treatment planning simulations. ECG-based 4D-dose reconstruction is technically feasible in a patient treatment-like setting. Further development of the presented approach, such as real-time dose calculation, may contribute to safe, successful treatments using scanned ion beams for cardiac arrhythmia ablation.

Mobile patient monitoring based on impedance-loaded SAW-sensors.

PubMed

Karilainen, Anna; Finnberg, Thomas; Uelzen, Thorsten; Dembowski, Klaus; Müller, Jörg

2004-11-01

A remotely requestable, passive, short-range sensor network for measuring small voltages is presented. The sensor system is able to simultaneously monitor six small voltages in millivolt-range, and it can be used for Holter-electrocardiogram (ECG) and other biopotential monitoring, or in industrial applications. The sensors are based on a surface acoustic wave (SAW) delay line with voltage-dependent, impedance loading on a reflector interdigital transducer (IDT). The load circuit impedance is varied by the capacitance of the voltage-controlled varactor. High resolution is achieved by developing a MOS-capacitor with a thin oxide, low flat-band voltage, and zero-voltage capacitance in the space-charge region, as well as a high-Q-microcoil by thick metal electroplating. Simultaneous monitoring of multiple potentials is realized by time-division-multiplexing of different sensor signals.

High Frequency QRS ECG Accurately Detects Cardiomyopathy

NASA Technical Reports Server (NTRS)

Schlegel, Todd T.; Arenare, Brian; Poulin, Gregory; Moser, Daniel R.; Delgado, Reynolds

2005-01-01

High frequency (HF, 150-250 Hz) analysis over the entire QRS interval of the ECG is more sensitive than conventional ECG for detecting myocardial ischemia. However, the accuracy of HF QRS ECG for detecting cardiomyopathy is unknown. We obtained simultaneous resting conventional and HF QRS 12-lead ECGs in 66 patients with cardiomyopathy (EF = 23.2 plus or minus 6.l%, mean plus or minus SD) and in 66 age- and gender-matched healthy controls using PC-based ECG software recently developed at NASA. The single most accurate ECG parameter for detecting cardiomyopathy was an HF QRS morphological score that takes into consideration the total number and severity of reduced amplitude zones (RAZs) present plus the clustering of RAZs together in contiguous leads. This RAZ score had an area under the receiver operator curve (ROC) of 0.91, and was 88% sensitive, 82% specific and 85% accurate for identifying cardiomyopathy at optimum score cut-off of 140 points. Although conventional ECG parameters such as the QRS and QTc intervals were also significantly longer in patients than controls (P less than 0.001, BBBs excluded), these conventional parameters were less accurate (area under the ROC = 0.77 and 0.77, respectively) than HF QRS morphological parameters for identifying underlying cardiomyopathy. The total amplitude of the HF QRS complexes, as measured by summed root mean square voltages (RMSVs), also differed between patients and controls (33.8 plus or minus 11.5 vs. 41.5 plus or minus 13.6 mV, respectively, P less than 0.003), but this parameter was even less accurate in distinguishing the two groups (area under ROC = 0.67) than the HF QRS morphologic and conventional ECG parameters. Diagnostic accuracy was optimal (86%) when the RAZ score from the HF QRS ECG and the QTc interval from the conventional ECG were used simultaneously with cut-offs of greater than or equal to 40 points and greater than or equal to 445 ms, respectively. In conclusion 12-lead HF QRS ECG employing RAZ scoring is a simple, accurate and inexpensive screening technique for cardiomyopathy. Although HF QRS ECG is highly sensitive for cardiomyopathy, its specificity may be compromised in patients with cardiac pathologies other than cardiomyopathy, such as uncomplicated coronary artery disease or multiple coronary disease risk factors. Further studies are required to determine whether HF QRS might be useful for monitoring cardiomyopathy severity or the efficacy of therapy in a longitudinal fashion.

An ANN-based HRV classifier for cardiac health prognosis.

PubMed

Sunkaria, Ramesh Kumar; Kumar, Vinod; Saxena, Suresh Chandra; Singhal, Achala M

2014-01-01

A multi-layer artificial neural network (ANN)-based heart rate variability (HRV) classifier has been proposed, which gives the cardiac health status as the output based on HRV of the patients independently of the cardiologists' view. The electrocardiogram (ECG) data of 46 patients were recorded in the out-patient department (OPD) of a hospital and HRV was evaluated using self-designed autoregressive-model-based technique. These patients suspected to be suffering from cardiac abnormalities were thoroughly examined by experienced cardiologists. On the basis of symptoms and other investigations, the attending cardiologists advised them to be classified into four categories as per the severity of cardiac health. Out of 46, the HRV data of 28 patients were used for training and data of 18 patients were used for testing of the proposed classifier. The cardiac health classification of each tested patient with the proposed classifier matches with the medical opinion of the cardiologists.

[Synchronous playing and acquiring of heart sounds and electrocardiogram based on labVIEW].

PubMed

Dan, Chunmei; He, Wei; Zhou, Jing; Que, Xiaosheng

2008-12-01

In this paper is described a comprehensive system, which can acquire heart sounds and electrocardiogram (ECG) in parallel, synchronize the display; and play of heart sound and make auscultation and check phonocardiogram to tie in. The hardware system with C8051F340 as the core acquires the heart sound and ECG synchronously, and then sends them to indicators, respectively. Heart sounds are displayed and played simultaneously by controlling the moment of writing to indicator and sound output device. In clinical testing, heart sounds can be successfully located with ECG and real-time played.

Classic electrocardiogram-based and mobile technology derived approaches to heart rate variability are not equivalent.

PubMed

Guzik, Przemyslaw; Piekos, Caroline; Pierog, Olivia; Fenech, Naiman; Krauze, Tomasz; Piskorski, Jaroslaw; Wykretowicz, Andrzej

2018-05-01

We compared classic ECG-derived versus a mobile approach to heart rate variability (HRV) measurement. 29 young adult healthy volunteers underwent a simultaneous recording of heart rate using an ECG and a chest heart rate monitor at supine rest, during mental stress and active standing. Mean RR interval, Standard Deviation of Normal-to-Normal (SDNN) of RR intervals, and Root Mean Square of the Successive Differences (RMSSD) between RR intervals were computed in 168 pairs of 5-minute epochs by in-house software on a PC (only sinus beats) and by mobile application "ELITEHRV" on a smartphone (no beat type identification). ECG analysis showed that 33.9% of the recordings contained at least one non-sinus beat or artefact, the mobile app did not report this. The mean RR intervals were significantly longer (p = 0.0378), while SDNN (p = 0.0001) and RMSSD (p = 0.0199) were smaller for the mobile approach. Measures of identical HRV parameters by ECG-based and mobile approaches are not equivalent. Copyright © 2018 Elsevier B.V. All rights reserved.

Real-Time Analytics for the Healthcare Industry: Arrhythmia Detection.

PubMed

Agneeswaran, Vijay Srinivas; Mukherjee, Joydeb; Gupta, Ashutosh; Tonpay, Pranay; Tiwari, Jayati; Agarwal, Nitin

2013-09-01

It is time for the healthcare industry to move from the era of "analyzing our health history" to the age of "managing the future of our health." In this article, we illustrate the importance of real-time analytics across the healthcare industry by providing a generic mechanism to reengineer traditional analytics expressed in the R programming language into Storm-based real-time analytics code. This is a powerful abstraction, since most data scientists use R to write the analytics and are not clear on how to make the data work in real-time and on high-velocity data. Our paper focuses on the applications necessary to a healthcare analytics scenario, specifically focusing on the importance of electrocardiogram (ECG) monitoring. A physician can use our framework to compare ECG reports by categorization and consequently detect Arrhythmia. The framework can read the ECG signals and uses a machine learning-based categorizer that runs within a Storm environment to compare different ECG signals. The paper also presents some performance studies of the framework to illustrate the throughput and accuracy trade-off in real-time analytics.

Evaluation of in-hospital electrocardiography versus 24-hour Holter for rate control in dogs with atrial fibrillation.

PubMed

Gelzer, A R; Kraus, M S; Rishniw, M

2015-07-01

To determine if the in-clinic ECG-derived heart rate could predict the at-home Holter-derived 24-hour average heart rate (Holter24h ), and whether it is useful to identify slow versus fast atrial fibrillation in dogs. 82 pairs of 1-minute ECGs and 24-hour Holter recordings were acquired in 34 dogs with atrial fibrillation. The initial 24-hour Holter was used to test if the ECG heart rate can identify dogs with "slow" versus "fast" atrial fibrillation based on a Holter24h threshold value of 140 bpm. ECG heart rate overestimated Holter24h by 26 bpm (95% CI: 3 bpm, 48 bpm; P < 0 · 015) with a 95% limit of agreement of -21 to 83 bpm. The in-clinic ECG-derived heart rate Ä155 bpm had a sensitivity of 73% and a specificity of 100% for identifying a Holter24h HR Ä140 bpm; an in-clinic ECG-derived HR <160 bpm had a sensitivity and specificity of 91% each. In-clinic ECG assessment of heart rate in dogs with atrial fibrillation does not reliably predict the heart rate in their home environment. However, an in-clinic heart rate greater than 155 bpm is useful in identifying "fast" atrial fibrillation, allowing clinicians to stratify which case may benefit from antiarrhythmic therapy. © 2015 British Small Animal Veterinary Association.

ECG fiducial point extraction using switching Kalman filter.

PubMed

Akhbari, Mahsa; Ghahjaverestan, Nasim Montazeri; Shamsollahi, Mohammad B; Jutten, Christian

2018-04-01

In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called "switch" is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and compared among two consecutive modes and a path is estimated, which shows the relation of each part of the ECG signal to the mode with the maximum probability. ECG FPs are found from the estimated path. For performance evaluation, the Physionet QT database is used and the proposed method is compared with methods based on wavelet transform, partially collapsed Gibbs sampler (PCGS) and extended Kalman filter. For our proposed method, the mean error and the root mean square error across all FPs are 2 ms (i.e. less than one sample) and 14 ms, respectively. These errors are significantly smaller than those obtained using other methods. The proposed method achieves lesser RMSE and smaller variability with respect to others. Copyright © 2018 Elsevier B.V. All rights reserved.

Cardiovascular screening in adolescents and young adults: a prospective study comparing the Pre-participation Physical Evaluation Monograph 4th Edition and ECG

PubMed Central

Fudge, Jessie; Harmon, Kimberly G; Owens, David S; Prutkin, Jordan M; Salerno, Jack C; Asif, Irfan M; Haruta, Alison; Pelto, Hank; Rao, Ashwin L; Toresdahl, Brett G; Drezner, Jonathan A

2015-01-01

Background This study compares the accuracy of cardiovascular screening in active adolescents and young adults using a standardised history, physical examination and resting 12-lead ECG. Methods Participants were prospectively screened using a standardised questionnaire based on the Pre-participation Physical Evaluation Monograph 4th Edition (PPE-4), physical examination and ECG interpreted using modern standards. Participants with abnormal findings had focused echocardiography and further evaluation. Primary outcomes included disorders associated with sudden cardiac arrest (SCA). Results From September 2010 to July 2011, 1339 participants underwent screening: age 13–24 (mean 16) years, 49% male, 68% Caucasian, 17% African-American and 1071 (80%) participating in organised sports. Abnormal history responses were reported on 916 (68%) questionnaires. After physician review, 495/ 916 (54%) participants with positive questionnaires were thought to have non-cardiac symptoms and/or a benign family history and did not warrant additional evaluation. Physical examination was abnormal in 124 (9.3%) participants, and 72 (5.4%) had ECG abnormalities. Echocardiograms were performed in 586 (44%) participants for abnormal history (31%), physical examination (8%) or ECG (5%). Five participants (0.4%) were identified with a disorder associated with SCA, all with ECG-detected Wolff-Parkinson-White. The false-positive rates for history, physical examination and ECG were 31.3%, 9.3% and 5%, respectively. Conclusions A standardised history and physical examination using the PPE-4 yields a high false-positive rate in a young active population with limited sensitivity to identify those at risk for SCA. ECG screening has a low false-positive rate using modern interpretation standards and improves detection of primary electrical disease at risk of SCA. PMID:24948082

ECG denoising using angular velocity as a state and an observation in an Extended Kalman Filter framework.

PubMed

Akhbari, Mahsa; Shamsollahi, Mohammad B; Jutten, Christian; Coppa, Bertrand

2012-01-01

In this paper an efficient filtering procedure based on Extended Kalman Filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. The proposed method considers the angular velocity of ECG signal, as one of the states of an EKF. We have considered two cases for observation equations, in one case we have assumed a corresponding observation to angular velocity state and in the other case, we have not assumed any observations for it. Quantitative evaluation of the proposed algorithm on the MIT-BIH Normal Sinus Rhythm Database (NSRDB) shows that an average SNR improvement of 8 dB is achieved for an input signal of -4 dB.

A cancelable biometric scheme based on multi-lead ECGs.

PubMed

Peng-Tzu Chen; Shun-Chi Wu; Jui-Hsuan Hsieh

2017-07-01

Biometric technologies offer great advantages over other recognition methods, but there are concerns that they may compromise the privacy of individuals. In this paper, an electrocardiogram (ECG)-based cancelable biometric scheme is proposed to relieve such concerns. In this scheme, distinct biometric templates for a given beat bundle are constructed via "subspace collapsing." To determine the identity of any unknown beat bundle, the multiple signal classification (MUSIC) algorithm, incorporating a "suppression and poll" strategy, is adopted. Unlike the existing cancelable biometric schemes, knowledge of the distortion transform is not required for recognition. Experiments with real ECGs from 285 subjects are presented to illustrate the efficacy of the proposed scheme. The best recognition rate of 97.58 % was achieved under the test condition N train = 10 and N test = 10.

Diffuse Myocardial Fibrosis Reduces Electrocardiographic Voltage Measures of Left Ventricular Hypertrophy Independent of Left Ventricular Mass.

PubMed

Maanja, Maren; Wieslander, Björn; Schlegel, Todd T; Bacharova, Ljuba; Abu Daya, Hussein; Fridman, Yaron; Wong, Timothy C; Schelbert, Erik B; Ugander, Martin

2017-01-22

Myocardial fibrosis quantified by myocardial extracellular volume fraction (ECV) and left ventricular mass (LVM) index (LVMI) measured by cardiovascular magnetic resonance might represent independent and opposing contributors to ECG voltage measures of left ventricular hypertrophy (LVH). Diffuse myocardial fibrosis can occur in LVH and interfere with ECG voltage measures. This phenomenon could explain the decreased sensitivity of LVH detectable by ECG, a fundamental diagnostic tool in cardiology. We identified 77 patients (median age, 53 [interquartile range, 26-60] years; 49% female) referred for contrast-enhanced cardiovascular magnetic resonance with ECV measures and 12-lead ECG. Exclusion criteria included clinical confounders that might influence ECG measures of LVH. We evaluated ECG voltage-based LVH measures, including Sokolow-Lyon index, Cornell voltage, 12-lead voltage, and the vectorcardiogram spatial QRS voltage, with respect to LVMI and ECV. ECV and LVMI were not correlated (R 2 =0.02; P=0.25). For all voltage-related parameters, higher LVMI resulted in greater voltage (r=0.33-0.49; P<0.05 for all), whereas increased ECV resulted in lower voltage (r=-0.32 to -0.57; P<0.05 for all). When accounting for body fat, LV end-diastolic volume, and mass-to-volume ratio, both LVMI (β=0.58, P=0.03) and ECV (β=-0.46, P<0.001) were independent predictors of QRS voltage (multivariate adjusted R 2 =0.39; P<0.001). Myocardial mass and diffuse myocardial fibrosis have independent and opposing effects upon ECG voltage measures of LVH. Diffuse myocardial fibrosis quantified by ECV can obscure the ECG manifestations of increased LVM. This provides mechanistic insight, which can explain the limited sensitivity of the ECG for detecting increased LVM. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

An ultra-low power (ULP) bandage-type ECG sensor for efficient cardiac disease management.

PubMed

Shin, Kunsoo; Park, G G; Kim, J P; Lee, T H; Ko, B H; Kim, Y H

2013-01-01

This paper proposed an ultra-low power bandage-type ECG sensor (the size: 76 × 34 × 3 (mm(3)) and the power consumption: 1 mW) which allows for a continuous and real-time monitoring of a user's ECG signals over 24h during daily activities. For its compact size and lower power consumption, we designed the analog front-end, the SRP (Samsung Reconfigurable Processor) based DSP of 30 uW/MHz, and the ULP wireless RF of 1 nJ/bit. Also, to tackle motion artifacts(MA), a MA monitoring technique based on the HCP (Half-cell Potential) is proposed which resulted in the high correlation between the MA and the HCP, the correlation coefficient of 0.75 ± 0.18. To assess its feasibility and validity as a wearable health monitor, we performed the comparison of two ECG signals recorded form it and a conventional Holter device. As a result, the performance of the former is a little lower as compared with the latter, although showing no statistical significant difference (the quality of the signal: 94.3% vs 99.4%; the accuracy of arrhythmia detection: 93.7% vs 98.7%). With those results, it has been confirmed that it can be used as a wearable health monitor due to its comfortability, its long operation lifetime and the good quality of the measured ECG signal.

ECG biometric identification: A compression based approach.

PubMed

Bras, Susana; Pinho, Armando J

2015-08-01

Using the electrocardiogram signal (ECG) to identify and/or authenticate persons are problems still lacking satisfactory solutions. Yet, ECG possesses characteristics that are unique or difficult to get from other signals used in biometrics: (1) it requires contact and liveliness for acquisition (2) it changes under stress, rendering it potentially useless if acquired under threatening. Our main objective is to present an innovative and robust solution to the above-mentioned problem. To successfully conduct this goal, we rely on information-theoretic data models for data compression and on similarity metrics related to the approximation of the Kolmogorov complexity. The proposed measure allows the comparison of two (or more) ECG segments, without having to follow traditional approaches that require heartbeat segmentation (described as highly influenced by external or internal interferences). As a first approach, the method was able to cluster the data in three groups: identical record, same participant, different participant, by the stratification of the proposed measure with values near 0 for the same participant and closer to 1 for different participants. A leave-one-out strategy was implemented in order to identify the participant in the database based on his/her ECG. A 1NN classifier was implemented, using as distance measure the method proposed in this work. The classifier was able to identify correctly almost all participants, with an accuracy of 99% in the database used.

[Practical experience about the compatibility of PDF converter in ECG information system].

PubMed

Yang, Gang; Lu, Weishi; Zhou, Jiacheng

2009-11-01

To find a way to view ECG from different manufacturers in electrocardiogram information system. Different format ECG data were transmitted to ECG center by different ways. Corresponding analysis software was used to make the diagnosis reports in the center. Then we use PDF convert to change all ECG reports into PDF format. The electrocardiogram information system manage these PDF format ECG data for clinic user. The ECG reports form several major ECG manufacturers were transformed to PDF format successfully. In the electrocardiogram information system it is freely to view the ECG figure. PDF format ECG report is a practicable way to solve the compatibility problem in electrocardiogram information system.

PDF-ECG in clinical practice: A model for long-term preservation of digital 12-lead ECG data.

PubMed

Sassi, Roberto; Bond, Raymond R; Cairns, Andrew; Finlay, Dewar D; Guldenring, Daniel; Libretti, Guido; Isola, Lamberto; Vaglio, Martino; Poeta, Roberto; Campana, Marco; Cuccia, Claudio; Badilini, Fabio

In clinical practice, data archiving of resting 12-lead electrocardiograms (ECGs) is mainly achieved by storing a PDF report in the hospital electronic health record (EHR). When available, digital ECG source data (raw samples) are only retained within the ECG management system. The widespread availability of the ECG source data would undoubtedly permit successive analysis and facilitate longitudinal studies, with both scientific and diagnostic benefits. PDF-ECG is a hybrid archival format which allows to store in the same file both the standard graphical report of an ECG together with its source ECG data (waveforms). Using PDF-ECG as a model to address the challenge of ECG data portability, long-term archiving and documentation, a real-world proof-of-concept test was conducted in a northern Italy hospital. A set of volunteers undertook a basic ECG using routine hospital equipment and the source data captured. Using dedicated web services, PDF-ECG documents were then generated and seamlessly uploaded in the hospital EHR, replacing the standard PDF reports automatically generated at the time of acquisition. Finally, the PDF-ECG files could be successfully retrieved and re-analyzed. Adding PDF-ECG to an existing EHR had a minimal impact on the hospital's workflow, while preserving the ECG digital data. Copyright © 2017 Elsevier Inc. All rights reserved.

Differentiating Obstructive from Central and Complex Sleep Apnea Using an Automated Electrocardiogram-Based Method

PubMed Central

Thomas, Robert Joseph; Mietus, Joseph E.; Peng, Chung-Kang; Gilmartin, Geoffrey; Daly, Robert W.; Goldberger, Ary L.; Gottlieb, Daniel J.

2007-01-01

Study Objectives: Complex sleep apnea is defined as sleep disordered breathing secondary to simultaneous upper airway obstruction and respiratory control dysfunction. The objective of this study was to assess the utility of an electrocardiogram (ECG)-based cardiopulmonary coupling technique to distinguish obstructive from central or complex sleep apnea. Design: Analysis of archived polysomnographic datasets. Setting: A laboratory for computational signal analysis. Interventions: None. Measurements and Results: The PhysioNet Sleep Apnea Database, consisting of 70 polysomnograms including single-lead ECG signals of approximately 8 hours duration, was used to train an ECG-based measure of autonomic and respiratory interactions (cardiopulmonary coupling) to detect periods of apnea and hypopnea, based on the presence of elevated low-frequency coupling (e-LFC). In the PhysioNet BIDMC Congestive Heart Failure Database (ECGs of 15 subjects), a pattern of “narrow spectral band” e-LFC was especially common. The algorithm was then applied to the Sleep Heart Health Study–I dataset, to select the 15 records with the highest amounts of broad and narrow spectral band e-LFC. The latter spectral characteristic seemed to detect not only periods of central apnea, but also obstructive hypopneas with a periodic breathing pattern. Applying the algorithm to 77 sleep laboratory split-night studies showed that the presence of narrow band e-LFC predicted an increased sensitivity to induction of central apneas by positive airway pressure. Conclusions: ECG-based spectral analysis allows automated, operator-independent characterization of probable interactions between respiratory dyscontrol and upper airway anatomical obstruction. The clinical utility of spectrographic phenotyping, especially in predicting failure of positive airway pressure therapy, remains to be more thoroughly tested. Citation: Thomas RJ; Mietus JE; Peng CK; Gilmartin G; Daly RW; Goldberger AL; Gottlieb DJ. Differentiating obstructive from central and complex sleep apnea using an automated electrocardiogram-based method. SLEEP 2007;30(12):1756-1769. PMID:18246985

Electrocardiogram reading: a randomized study comparing 2 e‑learning methods for medical students.

PubMed

Kopeć, Grzegorz; Waligóra, Marcin; Pacia, Michał; Chmielak, Wojciech; Stępień, Agnieszka; Janiec, Sebastian; Magoń, Wojciech; Jonas, Kamil; Podolec, Piotr

2018-02-28

INTRODUCTION    Interpretation of the electrocardiogram (ECG) is an essential skill in most medical specialties; however, the best method of teaching how to read ECGs has not been determined. OBJECTIVES    The aim of the study was to compare the effectiveness of collaborative (C‑eL) and self (S‑eL) e‑learning of ECG reading among medical students. PATIENTS AND METHODS    A total of 60 fifth‑year medical students were randomly assigned to the C‑eL and S‑eL groups. S‑eL students received 15 ECG recordings with a comprehensive description by email (one every 48 hours), while C‑eL students received the same ECG recordings without description. C‑eL students were expected to analyze each ECG together within the subgroups using an internet platform and to submit the interpretation within 48 hours. Afterwards, they received a description of each ECG. C‑eL students' activity was assessed based on the number of words written on the internet platform during discussion. A final test consisted of 10 theoretical questions and 10 ECG recordings. The final score was a sum of points obtained for the interpretation of ECG recordings. The main endpoint of the study was the number of students whose final score was 56% or higher. RESULTS    The final test was completed by 53 students (88.3%). The main endpoint was achieved in 20 C‑eL students (77%) and in 13 S‑eL students (48.1%), P = 0.03. The final score was 6.4 (interquartile range [IQR], 5.8-7.6) in the C‑eL group and 5.6 (IQR, 4.2-7.2) in the S‑eL group, P = 0.04. It correlated with the results of the theoretical test and students' activity during C‑eL (r = 0.42, P = 0.002 and r = 0.4, P = 0.04, respectively). CONCLUSIONS    C‑eL of ECG reading among fifth‑year medical students is superior to S‑eL.

Patients With Type A Acute Aortic Dissection Presenting With an Abnormal Electrocardiogram.

PubMed

Costin, Nathaniel I; Korach, Amit; Loor, Gabriel; Peterson, Mark D; Desai, Nimesh D; Trimarchi, Santi; de Vincentiis, Carlo; Ota, Takeyoshi; Reece, T Brett; Sundt, Thoralf M; Patel, Himanshu J; Chen, Edward P; Montgomery, Dan G; Nienaber, Christoph A; Isselbacher, Eric M; Eagle, Kim A; Gleason, Thomas G

2018-01-01

The electrocardiogram (ECG) is often used in the diagnosis of patients presenting with chest pain to emergency departments. Because chest pain is a common manifestation of type A acute aortic dissection (TAAAD), ECGs are obtained in much of this population. We evaluated the effect of particular ECG patterns on the diagnosis and treatment of TAAAD. TAAAD patients (N = 2,765) enrolled in the International Registry of Acute Aortic Dissection were stratified based on normal (n = 1,094 [39.6%]) and abnormal (n = 1,671 [60.4%]) findings on presenting ECGs and further subdivided according to specific ECG findings. Time data are presented in hours as medians (quartile 1 to quartile 3). Patients with ECGs with abnormal findings presented to the hospital sooner after symptom onset than those with ECGs with normal findings (1.4 [0.8 to 3.3] vs 2.0 [1.0 to 3.3]; p = 0.005). Specifically, this was seen in patients with infarction with new Q waves or ST elevation (1.3 [0.6 to 2.7] vs 1.5 [0.8 to 3.3]; p = 0.049). Interestingly, the time between symptom onset and diagnosis was longer with infarction with old Q waves (6.7 [3.2 to 18.4] vs 5.0 [2.9 to 11.8]; p = 0.034) and nonspecific ST-T changes (5.8 [3.0 to 13.8] vs 4.5 [2.8 to 10.5]; p = 0.002). Surgical mortality was higher in patients with abnormal ECG findings (20.6% vs 11.9%, p < 0.001), especially in those with ischemia by ECG (25.7% vs 16.8%, p < 0.001) and infarction with new Q waves or ST elevation (30.1% vs 17.1%, p < 0.001). TAAAD patients presenting with abnormal ECG results are sicker, have more in-hospital complications, and are more likely to die. The frequency of nonspecific ST-T abnormalities and its association with delay in diagnosis and treatment presents an opportunity for practice improvement. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

A Comprehensive Ubiquitous Healthcare Solution on an Android™ Mobile Device

PubMed Central

Hii, Pei-Cheng; Chung, Wan-Young

2011-01-01

Provision of ubiquitous healthcare solutions which provide healthcare services at anytime anywhere has become more favorable nowadays due to the emphasis on healthcare awareness and also the growth of mobile wireless technologies. Following this approach, an Android™ smart phone device is proposed as a mobile monitoring terminal to observe and analyze ECG (electrocardiography) waveforms from wearable ECG devices in real time under the coverage of a wireless sensor network (WSN). The exploitation of WSN in healthcare is able to substitute the complicated wired technology, moving healthcare away from a fixed location setting. As an extension to the monitoring scheme, medicine care is taken into consideration by utilizing the mobile phone as a barcode decoder, to verify and assist out-patients in the medication administration process, providing a better and more comprehensive healthcare service. PMID:22163986

Reflections on the role of open source in health information system interoperability.

PubMed

Sfakianakis, S; Chronaki, C E; Chiarugi, F; Conforti, F; Katehakis, D G

2007-01-01

This paper reflects on the role of open source in health information system interoperability. Open source is a driving force in computer science research and the development of information systems. It facilitates the sharing of information and ideas, enables evolutionary development and open collaborative testing of code, and broadens the adoption of interoperability standards. In health care, information systems have been developed largely ad hoc following proprietary specifications and customized design. However, the wide deployment of integrated services such as Electronic Health Records (EHRs) over regional health information networks (RHINs) relies on interoperability of the underlying information systems and medical devices. This reflection is built on the experiences of the PICNIC project that developed shared software infrastructure components in open source for RHINs and the OpenECG network that offers open source components to lower the implementation cost of interoperability standards such as SCP-ECG, in electrocardiography. Open source components implementing standards and a community providing feedback from real-world use are key enablers of health care information system interoperability. Investing in open source is investing in interoperability and a vital aspect of a long term strategy towards comprehensive health services and clinical research.

Assessing computerized eye tracking technology for gaining insight into expert interpretation of the 12-lead electrocardiogram: an objective quantitative approach.

PubMed

Bond, R R; Zhu, T; Finlay, D D; Drew, B; Kligfield, P D; Guldenring, D; Breen, C; Gallagher, A G; Daly, M J; Clifford, G D

2014-01-01

It is well known that accurate interpretation of the 12-lead electrocardiogram (ECG) requires a high degree of skill. There is also a moderate degree of variability among those who interpret the ECG. While this is the case, there are no best practice guidelines for the actual ECG interpretation process. Hence, this study adopts computerized eye tracking technology to investigate whether eye-gaze can be used to gain a deeper insight into how expert annotators interpret the ECG. Annotators were recruited in San Jose, California at the 2013 International Society of Computerised Electrocardiology (ISCE). Each annotator was recruited to interpret a number of 12-lead ECGs (N=12) while their eye gaze was recorded using a Tobii X60 eye tracker. The device is based on corneal reflection and is non-intrusive. With a sampling rate of 60Hz, eye gaze coordinates were acquired every 16.7ms. Fixations were determined using a predefined computerized classification algorithm, which was then used to generate heat maps of where the annotators looked. The ECGs used in this study form four groups (3=ST elevation myocardial infarction [STEMI], 3=hypertrophy, 3=arrhythmias and 3=exhibiting unique artefacts). There was also an equal distribution of difficulty levels (3=easy to interpret, 3=average and 3=difficult). ECGs were displayed using the 4x3+1 display format and computerized annotations were concealed. Precisely 252 expert ECG interpretations (21 annotators×12 ECGs) were recorded. Average duration for ECG interpretation was 58s (SD=23). Fleiss' generalized kappa coefficient (Pa=0.56) indicated a moderate inter-rater reliability among the annotators. There was a 79% inter-rater agreement for STEMI cases, 71% agreement for arrhythmia cases, 65% for the lead misplacement and dextrocardia cases and only 37% agreement for the hypertrophy cases. In analyzing the total fixation duration, it was found that on average annotators study lead V1 the most (4.29s), followed by leads V2 (3.83s), the rhythm strip (3.47s), II (2.74s), V3 (2.63s), I (2.53s), aVL (2.45s), V5 (2.27s), aVF (1.74s), aVR (1.63s), V6 (1.39s), III (1.32s) and V4 (1.19s). It was also found that on average the annotator spends an equal amount of time studying leads in the frontal plane (15.89s) when compared to leads in the transverse plane (15.70s). It was found that on average the annotators fixated on lead I first followed by leads V2, aVL, V1, II, aVR, V3, rhythm strip, III, aVF, V5, V4 and V6. We found a strong correlation (r=0.67) between time to first fixation on a lead and the total fixation duration on each lead. This indicates that leads studied first are studied the longest. There was a weak negative correlation between duration and accuracy (r=-0.2) and a strong correlation between age and accuracy (r=0.67). Eye tracking facilitated a deeper insight into how expert annotators interpret the 12-lead ECG. As a result, the authors recommend ECG annotators to adopt an initial first impression/pattern recognition approach followed by a conventional systematic protocol to ECG interpretation. This recommendation is based on observing misdiagnoses given due to first impression only. In summary, this research presents eye gaze results from expert ECG annotators and provides scope for future work that involves exploiting computerized eye tracking technology to further the science of ECG interpretation. Copyright © 2014 Elsevier Inc. All rights reserved.

Using Intracardiac Vectorcardiographic Loop for Surface ECG Synthesis

NASA Astrophysics Data System (ADS)

Kachenoura, A.; Porée, F.; Hernández, A. I.; Carrault, G.

2008-12-01

Current cardiac implantable devices offer improved processing power and recording capabilities. Some of these devices already provide basic telemonitoring features that may help to reduce health care expenditure. A challenge is posed in particular for the telemonitoring of the patient's cardiac electrical activity. Indeed, only intracardiac electrograms (EGMs) are acquired by the implanted device and these signals are difficult to analyze directly by clinicians. In this paper, we propose a patient-specific method to synthesize the surface electrocardiogram (ECG) from a set of EGM signals, based on a 3D representation of the cardiac electrical activity and principal component analysis (PCA). The results, in the case of sinus rhythm, show a correlation coefficient between the real ECG and the synthesized ECG of about 0.85. Moreover, the application of the proposed method to the patients who present an abnormal heart rhythm exhibits promising results, especially for characterizing the bundle branch blocs. Finally, in order to evaluate the behavior of our procedure in some practical situations, the quality of the ECG reconstruction is studied as a function of the number of EGM electrodes provided by the CIDs.

Issues in implementing a knowledge-based ECG analyzer for personal mobile health monitoring.

PubMed

Goh, K W; Kim, E; Lavanya, J; Kim, Y; Soh, C B

2006-01-01

Advances in sensor technology, personal mobile devices, and wireless broadband communications are enabling the development of an integrated personal mobile health monitoring system that can provide patients with a useful tool to assess their own health and manage their personal health information anytime and anywhere. Personal mobile devices, such as PDAs and mobile phones, are becoming more powerful integrated information management tools and play a major role in many people's lives. We focus on designing a health-monitoring system for people who suffer from cardiac arrhythmias. We have developed computer simulation models to evaluate the performance of appropriate electrocardiogram (ECG) analysis techniques that can be implemented on personal mobile devices. This paper describes an ECG analyzer to perform ECG beat and episode detection and classification. We have obtained promising preliminary results from our study. Also, we discuss several key considerations when implementing a mobile health monitoring solution. The mobile ECG analyzer would become a front-end patient health data acquisition module, which is connected to the Personal Health Information Management System (PHIMS) for data repository.

Wireless Sensor-Based Smart-Clothing Platform for ECG Monitoring

PubMed Central

Lin, Chung-Chih; Yu, Yan-Shuo

2015-01-01

The goal of this study is to use wireless sensor technologies to develop a smart clothes service platform for health monitoring. Our platform consists of smart clothes, a sensor node, a gateway server, and a health cloud. The smart clothes have fabric electrodes to detect electrocardiography (ECG) signals. The sensor node improves the accuracy of QRS complexes detection by morphology analysis and reduces power consumption by the power-saving transmission functionality. The gateway server provides a reconfigurable finite state machine (RFSM) software architecture for abnormal ECG detection to support online updating. Most normal ECG can be filtered out, and the abnormal ECG is further analyzed in the health cloud. Three experiments are conducted to evaluate the platform's performance. The results demonstrate that the signal-to-noise ratio (SNR) of the smart clothes exceeds 37 dB, which is within the “very good signal” interval. The average of the QRS sensitivity and positive prediction is above 99.5%. Power-saving transmission is reduced by nearly 1980 times the power consumption in the best-case analysis. PMID:26640512

Wireless Sensor-Based Smart-Clothing Platform for ECG Monitoring.

PubMed

Wang, Jie; Lin, Chung-Chih; Yu, Yan-Shuo; Yu, Tsang-Chu

2015-01-01

The goal of this study is to use wireless sensor technologies to develop a smart clothes service platform for health monitoring. Our platform consists of smart clothes, a sensor node, a gateway server, and a health cloud. The smart clothes have fabric electrodes to detect electrocardiography (ECG) signals. The sensor node improves the accuracy of QRS complexes detection by morphology analysis and reduces power consumption by the power-saving transmission functionality. The gateway server provides a reconfigurable finite state machine (RFSM) software architecture for abnormal ECG detection to support online updating. Most normal ECG can be filtered out, and the abnormal ECG is further analyzed in the health cloud. Three experiments are conducted to evaluate the platform's performance. The results demonstrate that the signal-to-noise ratio (SNR) of the smart clothes exceeds 37 dB, which is within the "very good signal" interval. The average of the QRS sensitivity and positive prediction is above 99.5%. Power-saving transmission is reduced by nearly 1980 times the power consumption in the best-case analysis.

Washable and Reliable Textile Electrodes Embedded into Underwear Fabric for Electrocardiography (ECG) Monitoring

PubMed Central

Ankhili, Amale; Tao, Xuyuan; Cochrane, Cédric; Coulon, David; Koncar, Vladan

2018-01-01

A medical quality electrocardiogram (ECG) signal is necessary for permanent monitoring, and an accurate heart examination can be obtained from instrumented underwear only if it is equipped with high-quality, flexible, textile-based electrodes guaranteeing low contact resistance with the skin. The main objective of this article is to develop reliable and washable ECG monitoring underwear able to record and wirelessly send an ECG signal in real time to a smart phone and further to a cloud. The article focuses on textile electrode design and production guaranteeing optimal contact impedance. Therefore, different types of textile fabrics were coated with modified poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) in order to develop and manufacture reliable and washable textile electrodes assembled to female underwear (bras), by sewing using commercially available conductive yarns. Washability tests of connected underwear containing textile electrodes and conductive threads were carried out up to 50 washing cycles. The influence of standardized washing cycles on the quality of ECG signals and the electrical properties of the textile electrodes were investigated and characterized. PMID:29414849

Washable and Reliable Textile Electrodes Embedded into Underwear Fabric for Electrocardiography (ECG) Monitoring.

PubMed

Ankhili, Amale; Tao, Xuyuan; Cochrane, Cédric; Coulon, David; Koncar, Vladan

2018-02-07

A medical quality electrocardiogram (ECG) signal is necessary for permanent monitoring, and an accurate heart examination can be obtained from instrumented underwear only if it is equipped with high-quality, flexible, textile-based electrodes guaranteeing low contact resistance with the skin. The main objective of this article is to develop reliable and washable ECG monitoring underwear able to record and wirelessly send an ECG signal in real time to a smart phone and further to a cloud. The article focuses on textile electrode design and production guaranteeing optimal contact impedance. Therefore, different types of textile fabrics were coated with modified poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) in order to develop and manufacture reliable and washable textile electrodes assembled to female underwear (bras), by sewing using commercially available conductive yarns. Washability tests of connected underwear containing textile electrodes and conductive threads were carried out up to 50 washing cycles. The influence of standardized washing cycles on the quality of ECG signals and the electrical properties of the textile electrodes were investigated and characterized.

Person identification in irregular cardiac conditions using electrocardiogram signals.

PubMed

Sidek, Khairul Azami; Khalil, Ibrahim

2011-01-01

This paper presents a person identification mechanism in irregular cardiac conditions using ECG signals. A total of 30 subjects were used in the study from three different public ECG databases containing various abnormal heart conditions from the Paroxysmal Atrial Fibrillation Predicition Challenge database (AFPDB), MIT-BIH Supraventricular Arrthymia database (SVDB) and T-Wave Alternans Challenge database (TWADB). Cross correlation (CC) was used as the biometric matching algorithm with defined threshold values to evaluate the performance. In order to measure the efficiency of this simple yet effective matching algorithm, two biometric performance metrics were used which are false acceptance rate (FAR) and false reject rate (FRR). Our experimentation results suggest that ECG based biometric identification with irregular cardiac condition gives a higher recognition rate of different ECG signals when tested for three different abnormal cardiac databases yielding false acceptance rate (FAR) of 2%, 3% and 2% and false reject rate (FRR) of 1%, 2% and 0% for AFPDB, SVDB and TWADB respectively. These results also indicate the existence of salient biometric characteristics in the ECG morphology within the QRS complex that tends to differentiate individuals.

Monitoring activities of daily living based on wearable wireless body sensor network.

PubMed

Kańtoch, E; Augustyniak, P; Markiewicz, M; Prusak, D

2014-01-01

With recent advances in microprocessor chip technology, wireless communication, and biomedical engineering it is possible to develop miniaturized ubiquitous health monitoring devices that are capable of recording physiological and movement signals during daily life activities. The aim of the research is to implement and test the prototype of health monitoring system. The system consists of the body central unit with Bluetooth module and wearable sensors: the custom-designed ECG sensor, the temperature sensor, the skin humidity sensor and accelerometers placed on the human body or integrated with clothes and a network gateway to forward data to a remote medical server. The system includes custom-designed transmission protocol and remote web-based graphical user interface for remote real time data analysis. Experimental results for a group of humans who performed various activities (eg. working, running, etc.) showed maximum 5% absolute error compared to certified medical devices. The results are promising and indicate that developed wireless wearable monitoring system faces challenges of multi-sensor human health monitoring during performing daily activities and opens new opportunities in developing novel healthcare services.

Panoramic ECG display versus conventional ECG: ischaemia detection by critical care nurses.

PubMed

Wilson, Nick; Hassani, Aimen; Gibson, Vanessa; Lightfoot, Timothy; Zizzo, Claudio

2012-01-01

To compare accuracy and certainty of diagnosis of cardiac ischaemia using the Panoramic ECG display tool plus conventional 12-lead electrocardiogram (ECG) versus 12-lead ECG alone by UK critical care nurses who were members of the British Association of Critical Care Nurses (BACCN). Critically ill patients are prone to myocardial ischaemia. Symptoms may be masked by sedation or analgesia, and ECG changes may be the only sign. Critical care nurses have an essential role in detecting ECG changes promptly. Despite this, critical care nurses may lack expertise in interpreting ECGs and myocardial ischaemia often goes undetected by critical care staff. British Association of Critical Care Nurses (BACCN) members were invited to complete an online survey to evaluate the analysis of two sets of eight ECGs displayed alone and with the new display device. Data from 82 participants showed diagnostic accuracy improved from 67·1% reading ECG traces alone, to 96·0% reading ECG plus Panoramic ECG display tool (P < 0·01, significance level α = 0·05). Participants' diagnostic certainty score rose from 41·7% reading ECG alone to 66·8% reading ECG plus Panoramic ECG display tool (P < 0·01, α = 0·05). The Panoramic ECG display tool improves both accuracy and certainty of detecting ST segment changes among critical care nurses, when compared to conventional 12-lead ECG alone. This benefit was greatest with early ischaemic changes. Critical care nurses who are least confident in reading conventional ECGs benefit the most from the new display. Critical care nurses have an essential role in the monitoring of critically ill patients. However, nurses do not always have the expertise to detect subtle ischaemic ECG changes promptly. Introduction of the Panoramic ECG display tool into clinical practice could lead to patients receiving treatment for myocardial ischaemia sooner with the potential for reduction in morbidity and mortality. © 2012 The Authors. Nursing in Critical Care © 2012 British Association of Critical Care Nurses.

False Positive Stress Testing: Does Endothelial Vascular Dysfunction Contribute to ST-Segment Depression in Women? A Pilot Study.

PubMed

Sharma, Shilpa; Mehta, Puja K; Arsanjani, Reza; Sedlak, Tara; Hobel, Zachary; Shufelt, Chrisandra; Jones, Erika; Kligfield, Paul; Mortara, David; Laks, Michael; Diniz, Marcio; Bairey Merz, C Noel

2018-06-19

The utility of exercise-induced ST-segment depression for diagnosing ischemic heart disease (IHD) in women is unclear. Based on evidence that IHD pathophysiology in women involves coronary vascular dysfunction, we hypothesized that coronary vascular dysfunction contributes to exercise electrocardiography (Ex-ECG) ST-depression in the absence of obstructive CAD, so-called "false positive" results. We tested our hypothesis in a pilot study evaluating the relationship between peripheral vascular endothelial function and Ex-ECG. Twenty-nine asymptomatic women without cardiac risk factors underwent maximal Bruce protocol exercise treadmill testing and peripheral endothelial function assessment using peripheral arterial tonometry (Itamar EndoPAT 2000) to measure reactive hyperemia index (RHI). The relationship between RHI and Ex-ECG ST-segment depression was evaluated using logistic regression and differences in subgroups using two-tailed t-tests. Mean age was 54 ± 7 years, body mass index 25 ± 4 kg/m 2 , and RHI 2.51 ± 0.66. Three women (10%) had RHI less than 1.68, consistent with abnormal peripheral endothelial function, while 18 women (62%) met criteria for a positive Ex-ECG based on ST-segment depression in contiguous leads. Women with and without ST-segment depression had similar baseline and exercise vital signs, metabolic equivalents (METS) achieved, and RHI (all p>0.05). RHI did not predict ST-segment depression. Our pilot study demonstrates a high prevalence of exercise-induced ST-segment depression in asymptomatic, middle-aged, overweight women. Peripheral vascular endothelial dysfunction did not predict Ex-ECG ST-segment depression. Further work is needed to investigate the utility of vascular endothelial testing and Ex-ECG for IHD diagnostic and management purposes in women. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Accuracy of ECG interpretation in competitive athletes: the impact of using standised ECG criteria.

PubMed

Drezner, Jonathan A; Asif, Irfan M; Owens, David S; Prutkin, Jordan M; Salerno, Jack C; Fean, Robyn; Rao, Ashwin L; Stout, Karen; Harmon, Kimberly G

2012-04-01

Interpretation of ECGs in athletes is complicated by physiological changes related to training. The purpose of this study was to determine the accuracy of ECG interpretation in athletes among different physician specialties, with and without use of a standised ECG criteria tool. Physicians were asked to interpret 40 ECGs (28 normal ECGs from college athletes randomised with 12 abnormal ECGs from individuals with known ciovascular pathology) and classify each ECG as (1) 'normal or variant--no further evaluation and testing needed' or (2) 'abnormal--further evaluation and testing needed.' After reading the ECGs, participants received a two-page ECG criteria tool to guide interpretation of the ECGs again. A total of 60 physicians participated: 22 primary care (PC) residents, 16 PC attending physicians, 12 sports medicine (SM) physicians and 10 ciologists. At baseline, the total number of ECGs correctly interpreted was PC residents 73%, PC attendings 73%, SM physicians 78% and ciologists 85%. With use of the ECG criteria tool, all physician groups significantly improved their accuracy (p<0.0001): PC residents 92%, PC attendings 90%, SM physicians 91% and ciologists 96%. With use of the ECG criteria tool, specificity improved from 70% to 91%, sensitivity improved from 89% to 94% and there was no difference comparing ciologists versus all other physicians (p=0.053). Providing standised criteria to assist ECG interpretation in athletes significantly improves the ability to accurately distinguish normal from abnormal findings across physician specialties, even in physicians with little or no experience.

T-wave loop area from a pre-implant 12-lead ECG is associated with appropriate ICD shocks

PubMed Central

Hnatkova, Katerina; Friede, Tim; Malik, Marek; Zabel, Markus

2017-01-01

Aims In implantable cardioverter-defibrillator (ICD) patients, predictors of ICD shocks and mortality are needed to improve patient selection. Electrocardiographic (ECG) markers are simple to obtain and have been demonstrated to predict mortality. We aimed to assess the association of T-wave loop area and circularity with ICD shocks. Methods The study investigated patients with ICDs implanted between 1998 and 2010 for whom digital 12-lead ECGs (Schiller CS200 ECG-Network) of sufficient quality were obtained within 1 month prior to the implantation. T-wave loop area and circularity were calculated. Follow-up data of appropriate shocks were obtained during ICD clinic visits that included reviews of device stored electrograms. Results A total of 605 patients (82% males) were included; 68% had ischemic cardiomyopathy and 72% were treated for primary prevention. Over 3.8±1.4 years of follow-up, 114 patients (19%) experienced appropriate shock(s). Those with smaller T-wave loop area received fewer shocks (TLA, hazard ratio, HR, per increase of 1 technical unit, 0.71; [95% confidence interval, 0.53–0.94]; P = 0.02) and those with larger T-wave loop circularity (TLC) representing rounder T wave loop received more shocks (HR per 1% TLC increase 2.96; [0.85–10.36]; P = 0.09). When the quartile containing the largest TLA and TLC values, respectively, were compared to the remaining cases, TLA remained significantly associated with fewer and TLC with more frequent shocks also after multivariate adjustment for clinical variables (HR, 0.59 [0.35–0.99], P = 0.044; and 1.64 [1.08–2.49], P = 0.021, respectively). Conclusions The size and shape of the T-wave loop calculated from pre-implantation 12-lead ECGs are associated with appropriate ICD shocks. PMID:28291831

Expert opinion paper on atrial fibrillation detection after ischemic stroke.

PubMed

Haeusler, Karl Georg; Gröschel, Klaus; Köhrmann, Martin; Anker, Stefan D; Brachmann, Johannes; Böhm, Michael; Diener, Hans-Christoph; Doehner, Wolfram; Endres, Matthias; Gerloff, Christian; Huttner, Hagen B; Kaps, Manfred; Kirchhof, Paulus; Nabavi, Darius Günther; Nolte, Christian H; Pfeilschifter, Waltraud; Pieske, Burkert; Poli, Sven; Schäbitz, Wolf Rüdiger; Thomalla, Götz; Veltkamp, Roland; Steiner, Thorsten; Laufs, Ulrich; Röther, Joachim; Wachter, Rolf; Schnabel, Renate

2018-04-27

This expert opinion paper on atrial fibrillation detection after ischemic stroke includes a statement of the "Heart and Brain" consortium of the German Cardiac Society and the German Stroke Society. This paper was endorsed by the Stroke Unit-Commission of the German Stroke Society and the German Atrial Fibrillation NETwork. In patients with ischemic stroke, detection of atrial fibrillation should usually lead to a change in secondary stroke prevention, since oral anticoagulation is superior to antiplatelet drugs. The detection of previously undiagnosed atrial fibrillation can be improved in patients with ischemic stroke to optimize stroke prevention. This paper summarizes the present knowledge on atrial fibrillation detection after ischemic stroke. We propose an interdisciplinary standard for a "structured analysis of ECG monitoring" on the stroke unit as well as a staged diagnostic scheme for the detection of atrial fibrillation. Since the optimal duration and mode of ECG monitoring has not yet been finally established, this paper is intended to give advice to physicians who are involved in stroke care. In line with the nature of an expert opinion paper, labeling of classes of recommendations is not provided, since many statements are based on the expert opinion, reported case series and clinical experience. Therefore, this paper is not intended as a guideline.

QRS complex detection based on continuous density hidden Markov models using univariate observations

NASA Astrophysics Data System (ADS)

Sotelo, S.; Arenas, W.; Altuve, M.

2018-04-01

In the electrocardiogram (ECG), the detection of QRS complexes is a fundamental step in the ECG signal processing chain since it allows the determination of other characteristics waves of the ECG and provides information about heart rate variability. In this work, an automatic QRS complex detector based on continuous density hidden Markov models (HMM) is proposed. HMM were trained using univariate observation sequences taken either from QRS complexes or their derivatives. The detection approach is based on the log-likelihood comparison of the observation sequence with a fixed threshold. A sliding window was used to obtain the observation sequence to be evaluated by the model. The threshold was optimized by receiver operating characteristic curves. Sensitivity (Sen), specificity (Spc) and F1 score were used to evaluate the detection performance. The approach was validated using ECG recordings from the MIT-BIH Arrhythmia database. A 6-fold cross-validation shows that the best detection performance was achieved with 2 states HMM trained with QRS complexes sequences (Sen = 0.668, Spc = 0.360 and F1 = 0.309). We concluded that these univariate sequences provide enough information to characterize the QRS complex dynamics from HMM. Future works are directed to the use of multivariate observations to increase the detection performance.

Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering.

PubMed

Xia, Yong; Han, Junze; Wang, Kuanquan

2015-01-01

Based on the idea of telemedicine, 24-hour uninterrupted monitoring on electrocardiograms (ECG) has started to be implemented. To create an intelligent ECG monitoring system, an efficient and quick detection algorithm for the characteristic waveforms is needed. This paper aims to give a quick and effective method for detecting QRS-complexes and R-waves in ECGs. The real ECG signal from the MIT-BIH Arrhythmia Database is used for the performance evaluation. The method proposed combined a wavelet transform and the K-means clustering algorithm. A wavelet transform is adopted in the data analysis and preprocessing. Then, based on the slope information of the filtered data, a segmented K-means clustering method is adopted to detect the QRS region. Detection of the R-peak is based on comparing the local amplitudes in each QRS region, which is different from other approaches, and the time cost of R-wave detection is reduced. Of the tested 8 records (total 18201 beats) from the MIT-BIH Arrhythmia Database, an average R-peak detection sensitivity of 99.72 and a positive predictive value of 99.80% are gained; the average time consumed detecting a 30-min original signal is 5.78s, which is competitive with other methods.

A markup language for electrocardiogram data acquisition and analysis (ecgML)

PubMed Central

Wang, Haiying; Azuaje, Francisco; Jung, Benjamin; Black, Norman

2003-01-01

Background The storage and distribution of electrocardiogram data is based on different formats. There is a need to promote the development of standards for their exchange and analysis. Such models should be platform-/ system- and application-independent, flexible and open to every member of the scientific community. Methods A minimum set of information for the representation and storage of electrocardiogram signals has been synthesised from existing recommendations. This specification is encoded into an XML-vocabulary. The model may aid in a flexible exchange and analysis of electrocardiogram information. Results Based on advantages of XML technologies, ecgML has the ability to present a system-, application- and format-independent solution for representation and exchange of electrocardiogram data. The distinction between the proposal developed by the U.S Food and Drug Administration and ecgML model is given. A series of tools, which aim to facilitate ecgML-based applications, are presented. Conclusions The models proposed here can facilitate the generation of a data format, which opens ways for better and clearer interpretation by both humans and machines. Its structured and transparent organisation will allow researchers to expand and test its capabilities in different application domains. The specification and programs for this protocol are publicly available. PMID:12735790

Diagnostic accuracy of a smartphone electrocardiograph in dogs: Comparison with standard 6-lead electrocardiography.

PubMed

Vezzosi, T; Buralli, C; Marchesotti, F; Porporato, F; Tognetti, R; Zini, E; Domenech, O

2016-10-01

The diagnostic accuracy of a smartphone electrocardiograph (ECG) in evaluating heart rhythm and ECG measurements was evaluated in 166 dogs. A standard 6-lead ECG was acquired for 1 min in each dog. A smartphone ECG tracing was simultaneously recorded using a single-lead bipolar ECG recorder. All ECGs were reviewed by one blinded operator, who judged if tracings were acceptable for interpretation and assigned an electrocardiographic diagnosis. Agreement between smartphone and standard ECG in the interpretation of tracings was evaluated. Sensitivity and specificity for the detection of arrhythmia were calculated for the smartphone ECG. Smartphone ECG tracings were interpretable in 162/166 (97.6%) tracings. A perfect agreement between the smartphone and standard ECG was found in detecting bradycardia, tachycardia, ectopic beats and atrioventricular blocks. A very good agreement was found in detecting sinus rhythm versus non-sinus rhythm (100% sensitivity and 97.9% specificity). The smartphone ECG provided tracings that were adequate for analysis in most dogs, with an accurate assessment of heart rate, rhythm and common arrhythmias. The smartphone ECG represents an additional tool in the diagnosis of arrhythmias in dogs, but is not a substitute for a 6-lead ECG. Arrhythmias identified by the smartphone ECG should be followed up with a standard ECG before making clinical decisions. Copyright © 2016 Elsevier Ltd. All rights reserved.

An ECG storage and retrieval system embedded in client server HIS utilizing object-oriented DB.

PubMed

Wang, C; Ohe, K; Sakurai, T; Nagase, T; Kaihara, S

1996-02-01

In the University of Tokyo Hospital, the improved client server HIS has been applied to clinical practice and physicians can order prescription, laboratory examination, ECG examination and radiographic examination, etc. directly by themselves and read results of these examinations, except medical signal waves, schema and image, on UNIX workstations. Recently, we designed and developed an ECG storage and retrieval system embedded in the client server HIS utilizing object-oriented database to take the first step in dealing with digitized signal, schema and image data and show waves, graphics, and images directly to physicians by the client server HIS. The system was developed based on object-oriented analysis and design, and implemented with object-oriented database management system (OODMS) and C++ programming language. In this paper, we describe the ECG data model, functions of the storage and retrieval system, features of user interface and the result of its implementation in the HIS.

Wavelet compression of multichannel ECG data by enhanced set partitioning in hierarchical trees algorithm.

PubMed

Sharifahmadian, Ershad

2006-01-01

The set partitioning in hierarchical trees (SPIHT) algorithm is very effective and computationally simple technique for image and signal compression. Here the author modified the algorithm which provides even better performance than the SPIHT algorithm. The enhanced set partitioning in hierarchical trees (ESPIHT) algorithm has performance faster than the SPIHT algorithm. In addition, the proposed algorithm reduces the number of bits in a bit stream which is stored or transmitted. I applied it to compression of multichannel ECG data. Also, I presented a specific procedure based on the modified algorithm for more efficient compression of multichannel ECG data. This method employed on selected records from the MIT-BIH arrhythmia database. According to experiments, the proposed method attained the significant results regarding compression of multichannel ECG data. Furthermore, in order to compress one signal which is stored for a long time, the proposed multichannel compression method can be utilized efficiently.

ECG denoising with adaptive bionic wavelet transform.

PubMed

Sayadi, Omid; Shamsollahi, Mohammad Bagher

2006-01-01

In this paper a new ECG denoising scheme is proposed using a novel adaptive wavelet transform, named bionic wavelet transform (BWT), which had been first developed based on a model of the active auditory system. There has been some outstanding features with the BWT such as nonlinearity, high sensitivity and frequency selectivity, concentrated energy distribution and its ability to reconstruct signal via inverse transform but the most distinguishing characteristic of BWT is that its resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential. Besides by optimizing the BWT parameters parallel to modifying a new threshold value, one can handle ECG denoising with results comparing to those of wavelet transform (WT). Preliminary tests of BWT application to ECG denoising were constructed on the signals of MIT-BIH database which showed high performance of noise reduction.

Wavelet-based watermarking and compression for ECG signals with verification evaluation.

PubMed

Tseng, Kuo-Kun; He, Xialong; Kung, Woon-Man; Chen, Shuo-Tsung; Liao, Minghong; Huang, Huang-Nan

2014-02-21

In the current open society and with the growth of human rights, people are more and more concerned about the privacy of their information and other important data. This study makes use of electrocardiography (ECG) data in order to protect individual information. An ECG signal can not only be used to analyze disease, but also to provide crucial biometric information for identification and authentication. In this study, we propose a new idea of integrating electrocardiogram watermarking and compression approach, which has never been researched before. ECG watermarking can ensure the confidentiality and reliability of a user's data while reducing the amount of data. In the evaluation, we apply the embedding capacity, bit error rate (BER), signal-to-noise ratio (SNR), compression ratio (CR), and compressed-signal to noise ratio (CNR) methods to assess the proposed algorithm. After comprehensive evaluation the final results show that our algorithm is robust and feasible.

[Biometric identification method for ECG based on the piecewise linear representation (PLR) and dynamic time warping (DTW)].

PubMed

Yang, Licai; Shen, Jun; Bao, Shudi; Wei, Shoushui

2013-10-01

To treat the problem of identification performance and the complexity of the algorithm, we proposed a piecewise linear representation and dynamic time warping (PLR-DTW) method for ECG biometric identification. Firstly we detected R peaks to get the heartbeats after denoising preprocessing. Then we used the PLR method to keep important information of an ECG signal segment while reducing the data dimension at the same time. The improved DTW method was used for similarity measurements between the test data and the templates. The performance evaluation was carried out on the two ECG databases: PTB and MIT-BIH. The analystic results showed that compared to the discrete wavelet transform method, the proposed PLR-DTW method achieved a higher accuracy rate which is nearly 8% of rising, and saved about 30% operation time, and this demonstrated that the proposed method could provide a better performance.

Angle-independent measure of motion for image-based gating in 3D coronary angiography

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

Lehmann, Glen C.; Holdsworth, David W.; Drangova, Maria

2006-05-15

The role of three-dimensional (3D) image guidance for interventional procedures and minimally invasive surgeries is increasing for the treatment of vascular disease. Currently, most interventional procedures are guided by two-dimensional x-ray angiography, but computed rotational angiography has the potential to provide 3D geometric information about the coronary arteries. The creation of 3D angiographic images of the coronary arteries requires synchronization of data acquisition with respect to the cardiac cycle, in order to minimize motion artifacts. This can be achieved by inferring the extent of motion from a patient's electrocardiogram (ECG) signal. However, a direct measurement of motion (from the 2Dmore » angiograms) has the potential to improve the 3D angiographic images by ensuring that only projections acquired during periods of minimal motion are included in the reconstruction. This paper presents an image-based metric for measuring the extent of motion in 2D x-ray angiographic images. Adaptive histogram equalization was applied to projection images to increase the sharpness of coronary arteries and the superior-inferior component of the weighted centroid (SIC) was measured. The SIC constitutes an image-based metric that can be used to track vessel motion, independent of apparent motion induced by the rotational acquisition. To evaluate the technique, six consecutive patients scheduled for routine coronary angiography procedures were studied. We compared the end of the SIC rest period ({rho}) to R-waves (R) detected in the patient's ECG and found a mean difference of 14{+-}80 ms. Two simultaneous angular positions were acquired and {rho} was detected for each position. There was no statistically significant difference (P=0.79) between {rho} in the two simultaneously acquired angular positions. Thus we have shown the SIC to be independent of view angle, which is critical for rotational angiography. A preliminary image-based gating strategy that employed the SIC was compared to an ECG-based gating strategy in a porcine model. The image-based gating strategy selected 61 projection images, compared to 45 selected by the ECG-gating strategy. Qualitative comparison revealed that although both the SIC-based and ECG-gated reconstructions decreased motion artifact compared to reconstruction with no gating, the SIC-based gating technique increased the conspicuity of smaller vessels when compared to ECG gating in maximum intensity projections of the reconstructions and increased the sharpness of a vessel cross section in multi-planar reformats of the reconstruction.« less

Alexander fractional differential window filter for ECG denoising.

PubMed

Verma, Atul Kumar; Saini, Indu; Saini, Barjinder Singh

2018-06-01

The electrocardiogram (ECG) non-invasively monitors the electrical activities of the heart. During the process of recording and transmission, ECG signals are often corrupted by various types of noises. Minimizations of these noises facilitate accurate detection of various anomalies. In the present paper, Alexander fractional differential window (AFDW) filter is proposed for ECG signal denoising. The designed filter is based on the concept of generalized Alexander polynomial and the R-L differential equation of fractional calculus. This concept is utilized to formulate a window that acts as a forward filter. Thereafter, the backward filter is constructed by reversing the coefficients of the forward filter. The proposed AFDW filter is then obtained by averaging of the forward and backward filter coefficients. The performance of the designed AFDW filter is validated by adding the various type of noise to the original ECG signal obtained from MIT-BIH arrhythmia database. The two non-diagnostic measure, i.e., SNR, MSE, and one diagnostic measure, i.e., wavelet energy based diagnostic distortion (WEDD) have been employed for the quantitative evaluation of the designed filter. Extensive experimentations on all the 48-records of MIT-BIH arrhythmia database resulted in average SNR of 22.014 ± 3.806365, 14.703 ± 3.790275, 13.3183 ± 3.748230; average MSE of 0.001458 ± 0.00028, 0.0078 ± 0.000319, 0.01061 ± 0.000472; and average WEDD value of 0.020169 ± 0.01306, 0.1207 ± 0.061272, 0.1432 ± 0.073588, for ECG signal contaminated by the power line, random, and the white Gaussian noise respectively. A new metric named as morphological power preservation measure (MPPM) is also proposed that account for the power preservance (as indicated by PSD plots) and the QRS morphology. The proposed AFDW filter retained much of the original (clean) signal power without any significant morphological distortion as validated by MPPM measure that were 0.0126, 0.08493, and 0.10336 for the ECG signal corrupted by the different type of noises. The versatility of the proposed AFDW filter is also validated by its application on the ECG signal from MIT-BIH database corrupted by the combination of the noises as well as on the real noisy ECG signals are taken from MIT-BIH ID database. Furthermore, the comparative study has also been done between the proposed AFDW filter and existing state of the art denoising algorithms. The results clearly prove the supremacy of our proposed AFDW filter.

A portable ECG monitoring device with Bluetooth and Holter capabilities for telemedicine applications.

PubMed

Lucani, Daniel; Cataldo, Giancarlos; Cruz, Julio; Villegas, Guillermo; Wong, Sara

2006-01-01

A prototype of a portable ECG-monitoring device has been developed for clinical and non-clinical environments as part of a telemedicine system to provide remote and continuous surveillance of patients. The device can acquire, store and/or transmit ECG signals to computer-based platforms or specially configured access points (AP) with Intranet/Internet capabilities in order to reach remote monitoring stations. Acquired data can be stored in a flash memory card in FAT16 format for later recovery, or transmitted via Bluetooth or USB to a local station or AP. This data acquisition module (DAM) operates in two modes: Holter and on-line transmission.

Hybrid Nanostructured Textile Bioelectrode for Unobtrusive Health Monitoring

NASA Astrophysics Data System (ADS)

Rai, Pratyush

Coronary heart disease, cardiovascular diseases and strokes are the leading causes of mortality in United States of America. Timely point-of-care health diagnostics and therapeutics for person suffering from these diseases can save thousands of lives. However, lack of accessible minimally intrusive health monitoring systems makes timely diagnosis difficult and sometimes impossible. To remedy this problem, a textile based nano-bio-sensor was developed and evaluated in this research. The sensor was made of novel array of vertically standing nanostructures that are conductive nano-fibers projecting from a conductive fabric. These sensor electrodes were tested for the quality of electrical contact that they made with the skin based on the fundamental skin impedance model and electromagnetic theory. The hybrid nanostructured dry electrodes provided large surface area and better contact with skin that improved electrode sensitivity and reduced the effect of changing skin properties, which are the problems usually faced by conventional dry textile electrodes. The dry electrodes can only register strong physiological signals because of high background noise levels, thus limiting the use of existing dry electrodes to heart rate measurement and respiration. Therefore, dry electrode systems cannot be used for recording complete ECG waveform, EEG or measurement of bioimpedance. Because of their improved sensitivity these hybrid nanostructured dry electrodes can be applied to measurement of ECG and bioimpedance with very low baseline noise. These textile based electrodes can be seamlessly integrated into garments of daily use such as vests and bra. In combination with embedded wireless network device that can communicate with smart phone, laptop or GPRS, they can function as wearable wireless health diagnostic systems.

E-bra with nanosensors, smart electronics and smart phone communication network for heart rate monitoring

NASA Astrophysics Data System (ADS)

Varadan, Vijay K.; Kumar, Prashanth S.; Oh, Sechang; Mathur, Gyanesh N.; Rai, Pratyush; Kegley, Lauren

2011-04-01

Heart related ailments have been a major cause for deaths in both men and women in United States. Since 1985, more women than men have died due to cardiac or cardiovascular ailments for reasons that are not well understood as yet. Lack of a deterministic understanding of this phenomenon makes continuous real time monitoring of cardiovascular health the best approach for both early detection of pathophysiological changes and events indicative of chronic cardiovascular diseases in women. This approach requires sensor systems to be seamlessly mounted on day to day clothing for women. With this application in focus, this paper describes a e-bra platform for sensors towards heart rate monitoring. The sensors, nanomaterial or textile based dry electrodes, capture the heart activity signals in form Electrocardiograph (ECG) and relay it to a compact textile mountable amplifier-wireless transmitter module for relay to a smart phone. The ECG signal, acquired on the smart phone, can be transmitted to the cyber space for post processing. As an example, the paper discusses the heart rate estimation and heart rate variability. The data flow from sensor to smart phone to server (cyber infrastructure) has been discussed. The cyber infrastructure based signal post processing offers an opportunity for automated emergency response that can be initiated from the server or the smartphone itself. Detailed protocols for both the scenarios have been presented and their relevance to the present emergency healthcare response system has been discussed.

[Screening for asymptomatic left ventricular systolic dysfunction in high-risk patients. Preliminary experience with a program based on the use of ECG and natriuretic peptide].

PubMed

Tarantini, Luigi; Cioffi, Giovanni; Di Lenarda, Andrea; Valle, Roberto; Pulignano, Giovanni; Del Sindaco, Donatella; Frigo, Gianfranco; Soravia, Giorgio; Tessier, Renato; Catania, Giuseppe

2008-12-01

Patients with asymptomatic left ventricular systolic dysfunction (ALVSD) have an increased risk of heart failure (HF) and a worse life expectancy. Since valuable therapies may prevent such dismal evolution, screening programs for ALVSD have recently been advocated to detect as early as possible such ominous condition. Echocardiography represents the gold standard for the assessment of ALVSD but its indiscriminate use in screening programs is impractical. Clinical multivariate risk assessment associated with ECG and serum brain natriuretic peptide (BNP) may be a feasible strategy to screen ALVSD. We prospectively sought to investigate the feasibility and effectiveness of a screening program for ALVSD based on ECG and BNP used in a hierarchical sequence in patients at high risk for HF. Patients > or =55 years old with > or =2 risk factors for HF or > or =70 years old with > or =1 risk factor for HF entered the study performing sequentially ECG, BNP and echocardiographic evaluation. ALVSD was defined as a left ventricular ejection fraction < or =50%. Thirty-three of 122 enrolled patients (27%) had ALVSD. They were older, presented more frequently a history of chemotherapy exposure, had often bundle branch block and higher BNP levels. No patient without any major abnormalities (atrial fibrillation, left ventricular hypertrophy, STT alterations of ischemic/strain origin, pathologic Q wave, bundle branch block) on ECG (n=31, 24.4%) had ALVSD. Among the 91 patients with abnormal ECG, ALVSD was observed in 33 (36%). The area under the receiver operating characteristic curve to detect ALVSD by BNP was 0.86 (confidence interval 0.79-0.94, p<0.0001) and BNP values of > or =43 pg/ml showed a sensitivity and a specificity of 94% and 57%, respectively. The proposed screening program was able to identify 95% (31/33) of patients with ALVSD saving 53% of echocardiographic examinations with a substantial reduction of the costs to diagnose ALVSD. Our prospective investigation confirms that ECG and BNP may be useful in detecting ALVSD in high-risk patients. A cost-effective screening program based on such simple and low-cost diagnostic tests might be employed for the prevention of HF in primary and secondary prevention programs in high-risk patients.

Sensium: an ultra-low-power wireless body sensor network platform: design & application challenges.

PubMed

Wong, A W; McDonagh, D; Omeni, O; Nunn, C; Hernandez-Silveira, M; Burdett, A J

2009-01-01

In this paper we present a system-on-chip for wireless body sensor networks, which integrates a transceiver, hardware MAC protocol, microprocessor, IO peripherals, memories, ADC and custom sensor interfaces. Addressing the challenges in the design, this paper will continue to discuss the issues in the applications of this technology to body worn monitoring for real-time measurement of ECG, heart rate, physical activity, respiration and/or skin temperature. Two application challenges are described; the real-time measurement of energy expenditure using the LifePebble, and; the development issues surrounding the 'Digital Patch'.

Diagnostic grade wireless ECG monitoring.

PubMed

Garudadri, Harinath; Chi, Yuejie; Baker, Steve; Majumdar, Somdeb; Baheti, Pawan K; Ballard, Dan

2011-01-01

In remote monitoring of Electrocardiogram (ECG), it is very important to ensure that the diagnostic integrity of signals is not compromised by sensing artifacts and channel errors. It is also important for the sensors to be extremely power efficient to enable wearable form factors and long battery life. We present an application of Compressive Sensing (CS) as an error mitigation scheme at the application layer for wearable, wireless sensors in diagnostic grade remote monitoring of ECG. In our previous work, we described an approach to mitigate errors due to packet losses by projecting ECG data to a random space and recovering a faithful representation using sparse reconstruction methods. Our contributions in this work are twofold. First, we present an efficient hardware implementation of random projection at the sensor. Second, we validate the diagnostic integrity of the reconstructed ECG after packet loss mitigation. We validate our approach on MIT and AHA databases comprising more than 250,000 normal and abnormal beats using EC57 protocols adopted by the Food and Drug Administration (FDA). We show that sensitivity and positive predictivity of a state-of-the-art ECG arrhythmia classifier is essentially invariant under CS based packet loss mitigation for both normal and abnormal beats even at high packet loss rates. In contrast, the performance degrades significantly in the absence of any error mitigation scheme, particularly for abnormal beats such as Ventricular Ectopic Beats (VEB).

Individual identification via electrocardiogram analysis.

PubMed

Fratini, Antonio; Sansone, Mario; Bifulco, Paolo; Cesarelli, Mario

2015-08-14

During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.

WaveformECG: A Platform for Visualizing, Annotating, and Analyzing ECG Data

PubMed Central

Winslow, Raimond L.; Granite, Stephen; Jurado, Christian

2017-01-01

The electrocardiogram (ECG) is the most commonly collected data in cardiovascular research because of the ease with which it can be measured and because changes in ECG waveforms reflect underlying aspects of heart disease. Accessed through a browser, WaveformECG is an open source platform supporting interactive analysis, visualization, and annotation of ECGs. PMID:28642673

Is screening for abnormal ECG patterns justified in long-term follow-up of childhood cancer survivors treated with anthracyclines?

PubMed

Pourier, Milanthy S; Mavinkurve-Groothuis, Annelies M C; Loonen, Jacqueline; Bökkerink, Jos P M; Roeleveld, Nel; Beer, Gil; Bellersen, Louise; Kapusta, Livia

2017-03-01

ECG and echocardiography are noninvasive screening tools to detect subclinical cardiotoxicity in childhood cancer survivors (CCSs). Our aims were as follows: (1) assess the prevalence of abnormal ECG patterns, (2) determine the agreement between abnormal ECG patterns and echocardiographic abnormalities; and (3) determine whether ECG screening for subclinical cardiotoxicity in CCSs is justified. We retrospectively studied ECG and echocardiography in asymptomatic CCSs more than 5 years after anthracycline treatment. Exclusion criteria were abnormal ECG and/or echocardiogram at the start of therapy, incomplete follow-up data, clinical heart failure, cardiac medication, and congenital heart disease. ECG abnormalities were classified using the Minnesota Code. Level of agreement between ECG and echocardiography was calculated with Cohen kappa. We included 340 survivors with a mean follow-up of 14.5 years (range 5-32). ECG was abnormal in 73 survivors (21.5%), with ventricular conduction disorders, sinus bradycardia, and high-amplitude R waves being most common. Prolonged QTc (>0.45 msec) was found in two survivors, both with a cumulative anthracycline dose of 300 mg/m 2 or higher. Echocardiography showed abnormalities in 44 survivors (12.9%), mostly mild valvular abnormalities. The level of agreement between ECG and echocardiography was low (kappa 0.09). Male survivors more often had an abnormal ECG (corrected odds ratio: 3.00, 95% confidence interval: 1.68-5.37). Abnormal ECG patterns were present in 21% of asymptomatic long-term CCSs. Lack of agreement between abnormal ECG patterns and echocardiographic abnormalities may suggest that ECG is valuable in long-term follow-up of CCSs. However, it is not clear whether these abnormal ECG patterns will be clinically relevant. © 2016 Wiley Periodicals, Inc.

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

PubMed Central

Sivaraks, Haemwaan

2015-01-01

Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods. PMID:25688284

Continuous control systems for non-contact ECG

NASA Astrophysics Data System (ADS)

Kodkin, Vladimir L.; Yakovleva, Galina V.; Smirnov, Alexey S.

2017-03-01

South Ural State University is still conducting the research work dedicated to innovations in biomedicine. Development of system for continuous control and diagnosis of the functional state in large groups of people is based on studies of non-contact ECG recording reported by the authors at the SPIE conference in 2016. The next stage of studies has been performed this year.

Evaluation of an electrocardiogram on QR code.

PubMed

Nakayama, Masaharu; Shimokawa, Hiroaki

2013-01-01

An electrocardiogram (ECG) is an indispensable tool to diagnose cardiac diseases, such as ischemic heart disease, myocarditis, arrhythmia, and cardiomyopathy. Since ECG patterns vary depend on patient status, it is also used to monitor patients during treatment and comparison with ECGs with previous results is important for accurate diagnosis. However, the comparison requires connection to ECG data server in a hospital and the availability of data connection among hospitals is limited. To improve the portability and availability of ECG data regardless of server connection, we here introduce conversion of ECG data into 2D barcodes as text data and decode of the QR code for drawing ECG with Google Chart API. Fourteen cardiologists and six general physicians evaluated the system using iPhone and iPad. Overall, they were satisfied with the system in usability and accuracy of decoded ECG compared to the original ECG. This new coding system may be useful in utilizing ECG data irrespective of server connections.

The future of remote ECG monitoring systems.

PubMed

Guo, Shu-Li; Han, Li-Na; Liu, Hong-Wei; Si, Quan-Jin; Kong, De-Feng; Guo, Fu-Su

2016-09-01

Remote ECG monitoring systems are becoming commonplace medical devices for remote heart monitoring. In recent years, remote ECG monitoring systems have been applied in the monitoring of various kinds of heart diseases, and the quality of the transmission and reception of the ECG signals during remote process kept advancing. However, there remains accompanying challenges. This report focuses on the three components of the remote ECG monitoring system: patient (the end user), the doctor workstation, and the remote server, reviewing and evaluating the imminent challenges on the wearable systems, packet loss in remote transmission, portable ECG monitoring system, patient ECG data collection system, and ECG signals transmission including real-time processing ST segment, R wave, RR interval and QRS wave, etc. This paper tries to clarify the future developmental strategies of the ECG remote monitoring, which can be helpful in guiding the research and development of remote ECG monitoring.

T wave abnormalities, high body mass index, current smoking and high lipoprotein (a) levels predict the development of major abnormal Q/QS patterns 20 years later. A population-based study

PubMed Central

Moller, Christina Strom; Byberg, Liisa; Sundstrom, Johan; Lind, Lars

2006-01-01

Background Most studies on risk factors for development of coronary heart disease (CHD) have been based on the clinical outcome of CHD. Our aim was to identify factors that could predict the development of ECG markers of CHD, such as abnormal Q/QS patterns, ST segment depression and T wave abnormalities, in 70-year-old men, irrespective of clinical outcome. Methods Predictors for development of different ECG abnormalities were identified in a population-based study using stepwise logistic regression. Anthropometrical and metabolic factors, ECG abnormalities and vital signs from a health survey of men at age 50 were related to ECG abnormalities identified in the same cohort 20 years later. Results At the age of 70, 9% had developed a major abnormal Q/QS pattern, but 63% of these subjects had not been previously hospitalized due to MI, while 57% with symptomatic MI between age 50 and 70 had no major Q/QS pattern at age 70. T wave abnormalities (Odds ratio 3.11, 95% CI 1.18–8.17), high lipoprotein (a) levels, high body mass index (BMI) and smoking were identified as significant independent predictors for the development of abnormal major Q/QS patterns. T wave abnormalities and high fasting glucose levels were significant independent predictors for the development of ST segment depression without abnormal Q/QS pattern. Conclusion T wave abnormalities on resting ECG should be given special attention and correlated with clinical information. Risk factors for major Q/QS patterns need not be the same as traditional risk factors for clinically recognized CHD. High lipoprotein (a) levels may be a stronger risk factor for silent myocardial infarction (MI) compared to clinically recognized MI. PMID:16519804

Graphene oxide based contacts as probes of biomedical signals

NASA Astrophysics Data System (ADS)

Hallfors, N. G.; Devarajan, A.; Farhat, I. A. H.; Abdurahman, A.; Liao, K.; Gater, D. L.; Elnaggar, M. I.; Isakovic, A. F.

We have developed a series of graphene oxide (GOx) on polymer contacts and have demonstrated these to be useful for collection of standard biomedically relevant signals, such as electrocardiogram (ECG). The process is wet solution-based and allows for control and tuning of the basic physical parameters of GOx, such as electrical and optical properties, simply by choosing the number of GOx layers. Our GOx characterization measurements show spectral (FTIR, XPS, IR absorbance) features most relevant to such performance, and point towards the likely explanations about the mechanisms for controlling the physical properties relevant for the contact performance. Structural (X-ray topography) and surface characterization (AFM, SEM) indicates to what degree these contacts can be considered homogeneous and therefore provide information on yield and repeatability. We compare the ECG signals recorded by standard commercial probes (Ag/AgCl) and GOx probes, displaying minor differences the solution to which may lead to a whole new way we perform ECG data collection, including wearable electronics and IoT friendly ECG monitoring. We acknowledge support from Mubadala-SRC AC4ES and from SRC 2011-KJ-2190. We thank J. B. Warren and G. L. Carr (BNL) for assistance.

A web-based approach for electrocardiogram monitoring in the home.

PubMed

Magrabi, F; Lovell, N H; Celler, B G

1999-05-01

A Web-based electrocardiogram (ECG) monitoring service in which a longitudinal clinical record is used for management of patients, is described. The Web application is used to collect clinical data from the patient's home. A database on the server acts as a central repository where this clinical information is stored. A Web browser provides access to the patient's records and ECG data. We discuss the technologies used to automate the retrieval and storage of clinical data from a patient database, and the recording and reviewing of clinical measurement data. On the client's Web browser, ActiveX controls embedded in the Web pages provide a link between the various components including the Web server, Web page, the specialised client side ECG review and acquisition software, and the local file system. The ActiveX controls also implement FTP functions to retrieve and submit clinical data to and from the server. An intelligent software agent on the server is activated whenever new ECG data is sent from the home. The agent compares historical data with newly acquired data. Using this method, an optimum patient care strategy can be evaluated, a summarised report along with reminders and suggestions for action is sent to the doctor and patient by email.

Comparison of Digital 12-Lead ECG and Digital 12-Lead Holter ECG Recordings in Healthy Male Subjects: Results from a Randomized, Double-Blinded, Placebo-Controlled Clinical Trial.

PubMed

Wang, Duolao; Bakhai, Ameet; Arezina, Radivoj; Täubel, Jörg

2016-11-01

Electrocardiogram (ECG) variability is greatly affected by the ECG recording method. This study aims to compare Holter and standard ECG recording methods in terms of central locations and variations of ECG data. We used the ECG data from a double-blinded, placebo-controlled, randomized clinical trial and used a mixed model approach to assess the agreement between two methods in central locations and variations of eight ECG parameters (Heart Rate, PR, QRS, QT, RR, QTcB, QTcF, and QTcI intervals). A total of 34 heathy male subjects with mean age of 25.7 ± 4.78 years were randomized to receive either active drug or placebo. Digital 12-lead ECG and digital 12-lead Holter ECG recordings were performed to assess ECG variability. There are no significant differences in least square mean between the Holter and the standard method for all ECG parameters. The total variance is consistently higher for the Holter method than the standard method for all ECG parameters except for QRS. The intraclass correlation coefficient (ICC) values for the Holter method are consistently lower than those for the standard method for all ECG parameters except for QRS, in particular, the ICC for QTcF is reduced from 0.86 for the standard method to 0.67 for the Holter method. This study suggests that Holter ECGs recorded in a controlled environment are not significantly different but more variable than those from the standard method. © 2016 Wiley Periodicals, Inc.

Mobile, Cloud, and Big Data Computing: Contributions, Challenges, and New Directions in Telecardiology

PubMed Central

Hsieh, Jui-Chien; Li, Ai-Hsien; Yang, Chung-Chi

2013-01-01

Many studies have indicated that computing technology can enable off-site cardiologists to read patients’ electrocardiograph (ECG), echocardiography (ECHO), and relevant images via smart phones during pre-hospital, in-hospital, and post-hospital teleconsultation, which not only identifies emergency cases in need of immediate treatment, but also prevents the unnecessary re-hospitalizations. Meanwhile, several studies have combined cloud computing and mobile computing to facilitate better storage, delivery, retrieval, and management of medical files for telecardiology. In the future, the aggregated ECG and images from hospitals worldwide will become big data, which should be used to develop an e-consultation program helping on-site practitioners deliver appropriate treatment. With information technology, real-time tele-consultation and tele-diagnosis of ECG and images can be practiced via an e-platform for clinical, research, and educational purposes. While being devoted to promote the application of information technology onto telecardiology, we need to resolve several issues: (1) data confidentiality in the cloud, (2) data interoperability among hospitals, and (3) network latency and accessibility. If these challenges are overcome, tele-consultation will be ubiquitous, easy to perform, inexpensive, and beneficial. Most importantly, these services will increase global collaboration and advance clinical practice, education, and scientific research in cardiology. PMID:24232290

Demonstration of a fully differential VGA chip with small THD for ECG acquisition system

NASA Astrophysics Data System (ADS)

Gongli, Xiao; Yuliang, Qin; Weilin, Xu; Baolin, Wei; Jihai, Duan; Xueming, Wei

2015-10-01

We present both a theoretical and experimental demonstration of a fully differential variable gain amplifier (VGA) with small total harmonic distortion (THD) for an electrocardiogram (ECG) acquisition system. Capacitive feedback technology is adopted to reduce the nonlinearity of VGA. The fully differential VGA has been fabricated in SMIC 0.18-μm CMOS process, and it only occupies 0.11 mm2. The measurements are in good agreement with simulation results. Experimental results show that the gain of VGA changes from 6.17 to 43.75 dB with a gain step of 3 dB. The high-pass corner frequency and low-pass corner frequency are around 0.22 Hz and 7.9 kHz, respectively. For each gain configuration, a maximal THD of 0.13% is obtained. The fully differential VGA has a low THD and its key performance parameters are well satisfied with the demands of ECG acquisition system application in the UWB wireless body area network. Project supported by the National Natural Science Foundation of China (Nos. 61264001, 61465004, 61161003, 61166004), the Guangxi Natural Science Foundation (Nos. 2013GXNSFAA019333, 2013GXNSFAA019338), the Science and Technology Research Key Project of Guangxi Department of Education (No. 2013ZD026), and the Innovation Project of GUET Graduate Education (No. GDYCSZ201457).

Mobile, cloud, and big data computing: contributions, challenges, and new directions in telecardiology.

PubMed

Hsieh, Jui-Chien; Li, Ai-Hsien; Yang, Chung-Chi

2013-11-13

Many studies have indicated that computing technology can enable off-site cardiologists to read patients' electrocardiograph (ECG), echocardiography (ECHO), and relevant images via smart phones during pre-hospital, in-hospital, and post-hospital teleconsultation, which not only identifies emergency cases in need of immediate treatment, but also prevents the unnecessary re-hospitalizations. Meanwhile, several studies have combined cloud computing and mobile computing to facilitate better storage, delivery, retrieval, and management of medical files for telecardiology. In the future, the aggregated ECG and images from hospitals worldwide will become big data, which should be used to develop an e-consultation program helping on-site practitioners deliver appropriate treatment. With information technology, real-time tele-consultation and tele-diagnosis of ECG and images can be practiced via an e-platform for clinical, research, and educational purposes. While being devoted to promote the application of information technology onto telecardiology, we need to resolve several issues: (1) data confidentiality in the cloud, (2) data interoperability among hospitals, and (3) network latency and accessibility. If these challenges are overcome, tele-consultation will be ubiquitous, easy to perform, inexpensive, and beneficial. Most importantly, these services will increase global collaboration and advance clinical practice, education, and scientific research in cardiology.

Optimal weighted combinatorial forecasting model of QT dispersion of ECGs in Chinese adults.

PubMed

Wen, Zhang; Miao, Ge; Xinlei, Liu; Minyi, Cen

2016-07-01

This study aims to provide a scientific basis for unifying the reference value standard of QT dispersion of ECGs in Chinese adults. Three predictive models including regression model, principal component model, and artificial neural network model are combined to establish the optimal weighted combination model. The optimal weighted combination model and single model are verified and compared. Optimal weighted combinatorial model can reduce predicting risk of single model and improve the predicting precision. The reference value of geographical distribution of Chinese adults' QT dispersion was precisely made by using kriging methods. When geographical factors of a particular area are obtained, the reference value of QT dispersion of Chinese adults in this area can be estimated by using optimal weighted combinatorial model and reference value of the QT dispersion of Chinese adults anywhere in China can be obtained by using geographical distribution figure as well.

Evaluation of a realtime, remote monitoring telemedicine system using the Bluetooth protocol and a mobile phone network.

PubMed

Jasemian, Yousef; Arendt-Nielsen, Lars

2005-01-01

A generic, realtime wireless telemedicine system has been developed that uses the Bluetooth protocol and the general packet radio service for mobile phones. The system was tested on 10 healthy volunteers, by continuous monitoring of their electrocardiograms (ECGs). Under realistic conditions, the system had 96.5% uptime, a data throughput of 3.3 kbit/s, a mean packet error rate of 8.5x10(-3) packet/s and a mean packet loss rate of 8.2x10(-3) packet/s. During 24 h testing, the total average downtime was 66 min and 90% of the periods of downtime were of only 1-3 min duration. Less than 10% of the ECGs were of unacceptable quality. Thus, the generic telemedicine system showed high reliability and performance, and the design may provide a foundation for realtime monitoring in clinical practice, for example in cardiology.

Monitoring and detection platform to prevent anomalous situations in home care.

PubMed

Villarrubia, Gabriel; Bajo, Javier; De Paz, Juan F; Corchado, Juan M

2014-06-05

Monitoring and tracking people at home usually requires high cost hardware installations, which implies they are not affordable in many situations. This study/paper proposes a monitoring and tracking system for people with medical problems. A virtual organization of agents based on the PANGEA platform, which allows the easy integration of different devices, was created for this study. In this case, a virtual organization was implemented to track and monitor patients carrying a Holter monitor. The system includes the hardware and software required to perform: ECG measurements, monitoring through accelerometers and WiFi networks. Furthermore, the use of interactive television can moderate interactivity with the user. The system makes it possible to merge the information and facilitates patient tracking efficiently with low cost.

Remote health monitoring system for detecting cardiac disorders.

PubMed

Bansal, Ayush; Kumar, Sunil; Bajpai, Anurag; Tiwari, Vijay N; Nayak, Mithun; Venkatesan, Shankar; Narayanan, Rangavittal

2015-12-01

Remote health monitoring system with clinical decision support system as a key component could potentially quicken the response of medical specialists to critical health emergencies experienced by their patients. A monitoring system, specifically designed for cardiac care with electrocardiogram (ECG) signal analysis as the core diagnostic technique, could play a vital role in early detection of a wide range of cardiac ailments, from a simple arrhythmia to life threatening conditions such as myocardial infarction. The system that the authors have developed consists of three major components, namely, (a) mobile gateway, deployed on patient's mobile device, that receives 12-lead ECG signals from any ECG sensor, (b) remote server component that hosts algorithms for accurate annotation and analysis of the ECG signal and (c) point of care device of the doctor to receive a diagnostic report from the server based on the analysis of ECG signals. In the present study, their focus has been toward developing a system capable of detecting critical cardiac events well in advance using an advanced remote monitoring system. A system of this kind is expected to have applications ranging from tracking wellness/fitness to detection of symptoms leading to fatal cardiac events.

Evaluation of heart rate variability indices using a real-time handheld remote ECG monitor.

PubMed

Singh, Swaroop S; Carlson, Barbara W; Hsiao, Henry S

2007-12-01

Studies on retrospective electrocardiogram (ECG) recordings of patients during cardiac arrest have shown significant changes in heart rate variability (HRV) indices prior to the onset of cardiac arrhythmia. The early detection of these changes in HRV indices increases the chances for a successful medical intervention by increasing the response time window. A portable, handheld remote ECG monitor designed in this research detects the QRS complex and calculates short-term HRV indices in real-time. The QRS detection of the ECG recordings of subjects from the MIT-Arrhythmia database yielded a mean sensitivity of 99.34% and a specificity of 99.31%. ECG recordings from normal subjects and subjects with congestive heart failure were used to identify the differences in HRV indices. An increase in heart rate, high-frequency spectral power (HFP), total spectral power, the ratio of HFP to low-frequency spectral power (LFP), and a decrease in root mean square sum of RR differences were observed. No difference was found on comparison of the standard deviation of normal to normal interval between adjacent R-waves, LFP, and very-low-frequency spectral power. Based on these, additional analytical calculations could be made to provide early warnings of impending cardiac conditions.

Missing RRI interpolation for HRV analysis using locally-weighted partial least squares regression.

PubMed

Kamata, Keisuke; Fujiwara, Koichi; Yamakawa, Toshiki; Kano, Manabu

2016-08-01

The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV). Since HRV reflects autonomic nervous function, HRV-based health monitoring services, such as stress estimation, drowsy driving detection, and epileptic seizure prediction, have been proposed. In these HRV-based health monitoring services, precise R wave detection from ECG is required; however, R waves cannot always be detected due to ECG artifacts. Missing RRI data should be interpolated appropriately for HRV analysis. The present work proposes a missing RRI interpolation method by utilizing using just-in-time (JIT) modeling. The proposed method adopts locally weighted partial least squares (LW-PLS) for RRI interpolation, which is a well-known JIT modeling method used in the filed of process control. The usefulness of the proposed method was demonstrated through a case study of real RRI data collected from healthy persons. The proposed JIT-based interpolation method could improve the interpolation accuracy in comparison with a static interpolation method.

Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition.

PubMed

Gutta, Sandeep; Cheng, Qi

2016-03-01

Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database.

A protocol for a prospective observational study using chest and thumb ECG: transient ECG assessment in stroke evaluation (TEASE) in Sweden.

PubMed

Magnusson, Peter; Koyi, Hirsh; Mattsson, Gustav

2018-04-03

Atrial fibrillation (AF) causes ischaemic stroke and based on risk factor evaluation warrants anticoagulation therapy. In stroke survivors, AF is typically detected with short-term ECG monitoring in the stroke unit. Prolonged continuous ECG monitoring requires substantial resources while insertable cardiac monitors are invasive and costly. Chest and thumb ECG could provide an alternative for AF detection poststroke.The primary objective of our study is to assess the incidence of newly diagnosed AF during 28 days of chest and thumb ECG monitoring in cryptogenic stroke. Secondary objectives are to assess health-related quality of life (HRQoL) using short-form health survey (SF-36) and the feasibility of the Coala Heart Monitor in patients who had a stroke. Stroke survivors in Region Gävleborg, Sweden, will be eligible for the study from October 2017. Patients with a history of ischaemic stroke without documented AF before or during ECG evaluation in the stroke unit will be evaluated by the chest and thumb ECG system Coala Heart Monitor. The monitoring system is connected to a smartphone application which allows for remote monitoring and prompt advice on clinical management. Over a period of 28 days, patients will be monitored two times a day and may activate the ECG recording at symptoms. On completion, the system is returned by mail. This system offers a possibility to evaluate the presence of AF poststroke, but the feasibility of this system in patients who recently suffered from a stroke is unknown. In addition, HRQoL using SF-36 in comparison to Swedish population norms will be assessed. The feasibility of the Coala Heart Monitor will be assessed by a self-developed questionnaire. The study was approved by The Regional Ethical Committee in Uppsala (2017/321). The database will be closed after the last follow-up, followed by statistical analyses, interpretation of results and dissemination to a scientific journal. NCT03301662; 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.

Grid mapping: a novel method of signal quality evaluation on a single lead electrocardiogram.

PubMed

Li, Yanjun; Tang, Xiaoying

2017-12-01

Diagnosis of long-term electrocardiogram (ECG) calls for automatic and accurate methods of ECG signal quality estimation, not only to lighten the burden of the doctors but also to avoid misdiagnoses. In this paper, a novel waveform-based method of phase-space reconstruction for signal quality estimation on a single lead ECG was proposed by projecting the amplitude of the ECG and its first order difference into grid cells. The waveform of a single lead ECG was divided into non-overlapping episodes (T s  = 10, 20, 30 s), and the number of grids in both the width and the height of each map are in the range [20, 100] (N X  = N Y  = 20, 30, 40, … 90, 100). The blank pane ratio (BPR) and the entropy were calculated from the distribution of ECG sampling points which were projected into the grid cells. Signal Quality Indices (SQI) bSQI and eSQI were calculated according to the BPR and the entropy, respectively. The MIT-BIH Noise Stress Test Database was used to test the performance of bSQI and eSQI on ECG signal quality estimation. The signal-to-noise ratio (SNR) during the noisy segments of the ECG records in the database is 24, 18, 12, 6, 0 and - 6 dB, respectively. For the SQI quantitative analysis, the records were divided into three groups: good quality group (24, 18 dB), moderate group (12, 6 dB) and bad quality group (0, - 6 dB). The classification among good quality group, moderate quality group and bad quality group were made by linear support-vector machine with the combination of the BPR, the entropy, the bSQI and the eSQI. The classification accuracy was 82.4% and the Cohen's Kappa coefficient was 0.74 on a scale of N X  = 40 and T s  = 20 s. In conclusion, the novel grid mapping offers an intuitive and simple approach to achieving signal quality estimation on a single lead ECG.

Inter-Rater Reliability and Downstream Financial Implications of Electrocardiography Screening in Young Athletes.

PubMed

Dhutia, Harshil; Malhotra, Aneil; Yeo, Tee Joo; Ster, Irina Chis; Gabus, Vincent; Steriotis, Alexandros; Dores, Helder; Mellor, Greg; García-Corrales, Carmen; Ensam, Bode; Jayalapan, Viknesh; Ezzat, Vivienne Anne; Finocchiaro, Gherardo; Gati, Sabiha; Papadakis, Michael; Tome-Esteban, Maria; Sharma, Sanjay

2017-08-01

Preparticipation screening for cardiovascular disease in young athletes with electrocardiography is endorsed by the European Society of Cardiology and several major sporting organizations. One of the concerns of the ECG as a screening test in young athletes relates to the potential for variation in interpretation. We investigated the degree of variation in ECG interpretation in athletes and its financial impact among cardiologists of differing experience. Eight cardiologists (4 with experience in screening athletes) each reported 400 ECGs of consecutively screened young athletes according to the 2010 European Society of Cardiology recommendations, Seattle criteria, and refined criteria. Cohen κ coefficient was used to calculate interobserver reliability. Cardiologists proposed secondary investigations after ECG interpretation, the costs of which were based on the UK National Health Service tariffs. Inexperienced cardiologists were more likely to classify an ECG as abnormal compared with experienced cardiologists (odds ratio, 1.44; 95% confidence interval, 1.03-2.02). Modification of ECG interpretation criteria improved interobserver reliability for categorizing an ECG as abnormal from poor (2010 European Society of Cardiology recommendations; κ=0.15) to moderate (refined criteria; κ=0.41) among inexperienced cardiologists; however, interobserver reliability was moderate for all 3 criteria among experienced cardiologists (κ=0.40-0.53). Inexperienced cardiologists were more likely to refer athletes for further evaluation compared with experienced cardiologists (odds ratio, 4.74; 95% confidence interval, 3.50-6.43) with poorer interobserver reliability (κ=0.22 versus κ=0.47). Interobserver reliability for secondary investigations after ECG interpretation ranged from poor to fair among inexperienced cardiologists (κ=0.15-0.30) and fair to moderate among experienced cardiologists (κ=0.21-0.46). The cost of cardiovascular evaluation per athlete was $175 (95% confidence interval, $142-$228) and $101 (95% confidence interval, $83-$131) for inexperienced and experienced cardiologists, respectively. Interpretation of the ECG in athletes and the resultant cascade of investigations are highly physician dependent even in experienced hands with important downstream financial implications, emphasizing the need for formal training and standardized diagnostic pathways. © 2017 American Heart Association, Inc.

Design of portable electrocardiogram device using DSO138

NASA Astrophysics Data System (ADS)

Abuzairi, Tomy; Matondang, Josef Stevanus; Purnamaningsih, Retno Wigajatri; Basari, Ratnasari, Anita

2018-02-01

Cardiovascular disease has been one of the leading causes of sudden cardiac deaths in many countries, covering Indonesia. Electrocardiogram (ECG) is a medical test to detect cardiac abnormalities by measuring the electrical activity generated by the heart, as the heart contracts. By using ECG, we can observe anomaly at the time of heart abnormalities. In this paper, design of portable ECG device is presented. The portable ECG device was designed to easily use in the village clinic or houses, due to the small size device and other benefits. The device was designed by using four units: (1) ECG electrode; (2) ECG analog front-end; (3) DSO138; and (4) battery. To create a simple electrode system in the portable ECG, 1-lead ECG with two electrodes were applied. The analog front-end circuitry consists of three integrated circuits, an instrumentation amplifier AD820AN, a low noise operational amplifier OPA134, and a low offset operational amplifier TL082. Digital ECG data were transformed to graphical data on DSO138. The results show that the portable ECG is successfully read the signal from 1-lead ECG system.

Effects of eCG and FSH on ovarian response, recovery rate and number and quality of oocytes obtained by ovum pick-up in Holstein cows.

PubMed

Sendag, Sait; Cetin, Yunus; Alan, Muhammet; Hadeler, Klaus-Gerd; Niemann, Heiner

2008-06-01

The goal of the present study was to compare the ovarian response, oocyte yields per animal, and the morphological quality of oocytes collected by ultrasound guided follicular aspiration from Holstein cows treated either with FSH or eCG. Twenty four normal cyclic, German Holstein cows were randomly divided into two groups. Fourteen cows received 3000 IU eCG on day-4 prior to ovum pick-up (OPU) (day 0), 2 days later (day-2), 625 microg cloprostenol was administered. On day-1 GnRH was administered i.m. and 24h later OPU (day 0) was performed. In ten cows a total dose of 500 IU follicle stimulating hormone (Pluset) was administered intramuscularly in a constant dosage for 4 days with intervals of 12h, starting on day-5. Luteolysis was induced by application of 625 microg cloprostenol on day-2. On day-1 (24h after the last FSH treatment) GnRH was administered i.m. and 24h later OPU (day 0) was performed. Ovarian follicles were visualized on the ultrasound monitor, counted and recorded. All visible antral follicles were punctured. Recovered oocytes were graded morphologically based on the cumulus investment. Average follicle number in ovaries was higher in FSH group than eCG group (p<0.05). Oocyte yields per animal did not differ between FSH and eCG groups. The proportion of grade A oocytes was higher in the FSH group in the than eCG group (p<0.05). Likewise, rate of grade C oocytes in FSH group were lower than eCG group (p<0.05). In conclusion, these results suggest that ovarian response, follicle number in ovaries and oocyte quality are affected by the type of gonadotropin and FSH is better alternative than eCG for OPU treatment.

Cardiovascular screening in adolescents and young adults: a prospective study comparing the Pre-participation Physical Evaluation Monograph 4th Edition and ECG.

PubMed

Fudge, Jessie; Harmon, Kimberly G; Owens, David S; Prutkin, Jordan M; Salerno, Jack C; Asif, Irfan M; Haruta, Alison; Pelto, Hank; Rao, Ashwin L; Toresdahl, Brett G; Drezner, Jonathan A

2014-08-01

This study compares the accuracy of cardiovascular screening in active adolescents and young adults using a standardised history, physical examination and resting 12-lead ECG. Participants were prospectively screened using a standardised questionnaire based on the Pre-participation Physical Evaluation Monograph 4th Edition (PPE-4), physical examination and ECG interpreted using modern standards. Participants with abnormal findings had focused echocardiography and further evaluation. Primary outcomes included disorders associated with sudden cardiac arrest (SCA). From September 2010 to July 2011, 1339 participants underwent screening: age 13-24 (mean 16) years, 49% male, 68% Caucasian, 17% African-American and 1071 (80%) participating in organised sports. Abnormal history responses were reported on 916 (68%) questionnaires. After physician review, 495/916 (54%) participants with positive questionnaires were thought to have non-cardiac symptoms and/or a benign family history and did not warrant additional evaluation. Physical examination was abnormal in 124 (9.3%) participants, and 72 (5.4%) had ECG abnormalities. Echocardiograms were performed in 586 (44%) participants for abnormal history (31%), physical examination (8%) or ECG (5%). Five participants (0.4%) were identified with a disorder associated with SCA, all with ECG-detected Wolff-Parkinson-White. The false-positive rates for history, physical examination and ECG were 31.3%, 9.3% and 5%, respectively. A standardised history and physical examination using the PPE-4 yields a high false-positive rate in a young active population with limited sensitivity to identify those at risk for SCA. ECG screening has a low false-positive rate using modern interpretation standards and improves detection of primary electrical disease at risk of SCA. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

System light-loading technology for mHealth: Manifold-learning-based medical data cleansing and clinical trials in WE-CARE Project.

PubMed

Huang, Anpeng; Xu, Wenyao; Li, Zhinan; Xie, Linzhen; Sarrafzadeh, Majid; Li, Xiaoming; Cong, Jason

2014-09-01

Cardiovascular disease (CVD) is a major issue to public health. It contributes 41% to the Chinese death rate each year. This huge loss encouraged us to develop a Wearable Efficient teleCARdiology systEm (WE-CARE) for early warning and prevention of CVD risks in real time. WE-CARE is expected to work 24/7 online for mobile health (mHealth) applications. Unfortunately, this purpose is often disrupted in system experiments and clinical trials, even if related enabling technologies work properly. This phenomenon is rooted in the overload issue of complex Electrocardiogram (ECG) data in terms of system integration. In this study, our main objective is to get a system light-loading technology to enable mHealth with a benchmarked ECG anomaly recognition rate. To achieve this objective, we propose an approach to purify clinical features from ECG raw data based on manifold learning, called the Manifold-based ECG-feature Purification algorithm. Our clinical trials verify that our proposal can detect anomalies with a recognition rate of up to 94% which is highly valuable in daily public health-risk alert applications based on clinical criteria. Most importantly, the experiment results demonstrate that the WE-CARE system enabled by our proposal can enhance system reliability by at least two times and reduce false negative rates to 0.76%, and extend the battery life by 40.54%, in the system integration level.

Heart Rate Variability and Wavelet-based Studies on ECG Signals from Smokers and Non-smokers

NASA Astrophysics Data System (ADS)

Pal, K.; Goel, R.; Champaty, B.; Samantray, S.; Tibarewala, D. N.

2013-12-01

The current study deals with the heart rate variability (HRV) and wavelet-based ECG signal analysis of smokers and non-smokers. The results of HRV indicated dominance towards the sympathetic nervous system activity in smokers. The heart rate was found to be higher in case of smokers as compared to non-smokers ( p < 0.05). The frequency domain analysis showed an increase in the LF and LF/HF components with a subsequent decrease in the HF component. The HRV features were analyzed for classification of the smokers from the non-smokers. The results indicated that when RMSSD, SD1 and RR-mean features were used concurrently a classification efficiency of > 90 % was achieved. The wavelet decomposition of the ECG signal was done using the Daubechies (db 6) wavelet family. No difference was observed between the smokers and non-smokers which apparently suggested that smoking does not affect the conduction pathway of heart.

A new approach based on the median filter to T-wave detection in ECG signal.

PubMed

Kholkhal, Mourad; Bereksi Reguig, Fethi

2014-07-01

The electrocardiogram (ECG) is one of the most used signals in the diagnosis of heart disease. It contains different waves which directly correlate to heart activity. Different methods have been used in order to detect these waves and consequently lead to heart activity diagnosis. This paper is interested more particularly to the detection of the T-wave. Such a wave represents the re-polarization state of the heart activity. The proposed approach is based on the algorithm procedure which allows the detection of the T-wave using a lot of filter including mean and median filter. The proposed algorithm is implemented and tested on a set of ECG recordings taken from, respectively, the European STT, MITBIH and MITBIH ST databases. The results are found to be very satisfactory in terms of sensitivity, predictivity and error compared to other works in the field.

Coronary heart disease index based on longitudinal electrocardiography

NASA Technical Reports Server (NTRS)

Townsend, J. C.; Cronin, J. P.

1977-01-01

A coronary heart disease index was developed from longitudinal ECG (LCG) tracings to serve as a cardiac health measure in studies of working and, essentially, asymptomatic populations, such as pilots and executives. For a given subject, the index consisted of a composite score based on the presence of LCG aberrations and weighted values previously assigned to them. The index was validated by correlating it with the known presence or absence of CHD as determined by a complete physical examination, including treadmill, resting ECG, and risk factor information. The validating sample consisted of 111 subjects drawn by a stratified-random procedure from 5000 available case histories. The CHD index was found to be significantly more valid as a sole indicator of CHD than the LCG without the use of the index. The index consistently produced higher validity coefficients in identifying CHD than did treadmill testing, resting ECG, or risk factor analysis.

VLSI implementation of a new LMS-based algorithm for noise removal in ECG signal

NASA Astrophysics Data System (ADS)

Satheeskumaran, S.; Sabrigiriraj, M.

2016-06-01

Least mean square (LMS)-based adaptive filters are widely deployed for removing artefacts in electrocardiogram (ECG) due to less number of computations. But they posses high mean square error (MSE) under noisy environment. The transform domain variable step-size LMS algorithm reduces the MSE at the cost of computational complexity. In this paper, a variable step-size delayed LMS adaptive filter is used to remove the artefacts from the ECG signal for improved feature extraction. The dedicated digital Signal processors provide fast processing, but they are not flexible. By using field programmable gate arrays, the pipelined architectures can be used to enhance the system performance. The pipelined architecture can enhance the operation efficiency of the adaptive filter and save the power consumption. This technique provides high signal-to-noise ratio and low MSE with reduced computational complexity; hence, it is a useful method for monitoring patients with heart-related problem.

Derivation of a respiration trigger signal in small animal list-mode PET based on respiration-induced variations of the ECG signal.

PubMed

Todica, Andrei; Lehner, Sebastian; Wang, Hao; Zacherl, Mathias J; Nekolla, Katharina; Mille, Erik; Xiong, Guoming; Bartenstein, Peter; la Fougère, Christian; Hacker, Marcus; Böning, Guido

2016-02-01

Raw PET list-mode data contains motion artifacts causing image blurring and decreased spatial resolution. Unless corrected, this leads to underestimation of the tracer uptake and overestimation of the lesion size, as well as inaccuracies with regard to left ventricular volume and ejection fraction (LVEF), especially in small animal imaging. A respiratory trigger signal from respiration-induced variations in the electro-cardiogram (ECG) was detected. Original and revised list-mode PET data were used for calculation of left ventricular function parameters using both respiratory gating techniques. For adequately triggered datasets we saw no difference in mean respiratory cycle period between the reference standard (RRS) and the ECG-based (ERS) methods (1120 ± 159 ms vs 1120 ± 159 ms; P = n.s.). While the ECG-based method showed somewhat higher signal noise (66 ± 22 ms vs 51 ± 29 ms; P < .001), both respiratory triggering techniques yielded similar estimates for EDV, ESV, LVEF (RRS: 387 ± 56 µL, 162 ± 34 µL, 59 ± 5%; ERS: 389 ± 59 µL, 163 ± 35 µL, 59 ± 4%; P = n.s.). This study showed that respiratory gating signals can be accurately derived from cardiac trigger information alone, without the additional requirement for dedicated measurement of the respiratory motion in rats.

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

Xie, Xueqian; Greuter, Marcel J. W.; Groen, Jaap M.

Purpose: Coronary artery calcium score, traditionally based on electrocardiography (ECG)-triggered computed tomography (CT), predicts cardiovascular risk. However, nontriggered CT is extensively utilized. The study-purpose is to evaluate the in vitro agreement in coronary calcium score between nontriggered thoracic CT and ECG-triggered cardiac CT.Methods: Three artificial coronary arteries containing calcifications of different densities (high, medium, and low), and sizes (large, medium, and small), were studied in a moving cardiac phantom. Two 64-detector CT systems were used. The phantom moved at 0–90 mm/s in nontriggered low-dose CT as index test, and at 0–30 mm/s in ECG-triggered CT as reference. Differences in calciummore » scores between nontriggered and ECG-triggered CT were analyzed by t-test and 95% confidence interval. The sensitivity to detect calcification was calculated as the percentage of positive calcium scores.Results: Overall, calcium scores in nontriggered CT were not significantly different to those in ECG-triggered CT (p > 0.05). Calcium scores in nontriggered CT were within the 95% confidence interval of calcium scores in ECG-triggered CT, except predominantly at higher velocities (≥50 mm/s) for the high-density and large-size calcifications. The sensitivity for a nonzero calcium score was 100% for large calcifications, but 46%± 11% for small calcifications in nontriggered CT.Conclusions: When performing multiple measurements, good agreement in positive calcium scores is found between nontriggered thoracic and ECG-triggered cardiac CT. Agreement decreases with increasing coronary velocity. From this phantom study, it can be concluded that a high calcium score can be detected by nontriggered CT, and thus, that nontriggered CT likely can identify individuals at high risk of cardiovascular disease. On the other hand, a zero calcium score in nontriggered CT does not reliably exclude coronary calcification.« less

Robust detection of heart beats in multimodal records using slope- and peak-sensitive band-pass filters.

PubMed

Pangerc, Urška; Jager, Franc

2015-08-01

In this work, we present the development, architecture and evaluation of a new and robust heart beat detector in multimodal records. The detector uses electrocardiogram (ECG) signals, and/or pulsatile (P) signals, such as: blood pressure, artery blood pressure and pulmonary artery pressure, if present. The base approach behind the architecture of the detector is collecting signal energy (differentiating and low-pass filtering, squaring, integrating). To calculate the detection and noise functions, simple and fast slope- and peak-sensitive band-pass digital filters were designed. By using morphological smoothing, the detection functions were further improved and noise intervals were estimated. The detector looks for possible pacemaker heart rate patterns and repairs the ECG signals and detection functions. Heart beats are detected in each of the ECG and P signals in two steps: a repetitive learning phase and a follow-up detecting phase. The detected heart beat positions from the ECG signals are merged into a single stream of detected ECG heart beat positions. The merged ECG heart beat positions and detected heart beat positions from the P signals are verified for their regularity regarding the expected heart rate. The detected heart beat positions of a P signal with the best match to the merged ECG heart beat positions are selected for mapping into the noise and no-signal intervals of the record. The overall evaluation scores in terms of average sensitivity and positive predictive values obtained on databases that are freely available on the Physionet website were as follows: the MIT-BIH Arrhythmia database (99.91%), the MGH/MF Waveform database (95.14%), the augmented training set of the follow-up phase of the PhysioNet/Computing in Cardiology Challenge 2014 (97.67%), and the Challenge test set (93.64%).

Use of equine chorionic gonadotropin to control reproduction of the dairy cow: a review.

PubMed

De Rensis, F; López-Gatius, F

2014-04-01

Equine chorionic gonadotropin (eCG) is a member of the glycoprotein family of hormones along with LH, FSH and thyroid-stimulating hormone. In non-equid species, eCG shows high LH- and FSH-like activities and has a high affinity for both FSH and LH receptors in the ovaries. On the granulosa and thecal cells of the follicle, eCG has long-lasting LH- and FSH-like effects that stimulate oestradiol and progesterone secretion. Thus, eCG administration in dairy cattle results in fewer atretic follicles, the recruitment of more small follicles showing an elevated growth rate, the sustained growth of medium and large follicles and improved development of the dominant and pre-ovulatory follicle. In consequence, the quality of the ensuing CL is improved, and thereby progesterone secretion increased. Based on these characteristics, eCG treatment is utilized in veterinary medicine to control the reproductive activity of the cow by i) improving reproductive performance during early post-partum stages; ii) increasing ovulation and pregnancy rates in non-cyclic cows; iii) improving the conception rate in cows showing delayed ovulation; and finally, iv) eCG is currently included in protocols for fixed-time artificial insemination since after inducing the synchrony of ovulation using a progesterone-releasing device, eCG has beneficial effects on embryo development and survival. The above effects are not always observed in cyclic animals, but they are evident in animals in which LH secretion and ovarian activity are reduced or compromised, for instance, during the early post-partum period, under seasonal heat stress, in anoestrus animals or in animals with a low body condition score. © 2014 Blackwell Verlag GmbH.

Sudden cardiac death and pump failure death prediction in chronic heart failure by combining ECG and clinical markers in an integrated risk model

PubMed Central

Orini, Michele; Mincholé, Ana; Monasterio, Violeta; Cygankiewicz, Iwona; Bayés de Luna, Antonio; Martínez, Juan Pablo

2017-01-01

Background Sudden cardiac death (SCD) and pump failure death (PFD) are common endpoints in chronic heart failure (CHF) patients, but prevention strategies are different. Currently used tools to specifically predict these endpoints are limited. We developed risk models to specifically assess SCD and PFD risk in CHF by combining ECG markers and clinical variables. Methods The relation of clinical and ECG markers with SCD and PFD risk was assessed in 597 patients enrolled in the MUSIC (MUerte Súbita en Insuficiencia Cardiaca) study. ECG indices included: turbulence slope (TS), reflecting autonomic dysfunction; T-wave alternans (TWA), reflecting ventricular repolarization instability; and T-peak-to-end restitution (ΔαTpe) and T-wave morphology restitution (TMR), both reflecting changes in dispersion of repolarization due to heart rate changes. Standard clinical indices were also included. Results The indices with the greatest SCD prognostic impact were gender, New York Heart Association (NYHA) class, left ventricular ejection fraction, TWA, ΔαTpe and TMR. For PFD, the indices were diabetes, NYHA class, ΔαTpe and TS. Using a model with only clinical variables, the hazard ratios (HRs) for SCD and PFD for patients in the high-risk group (fifth quintile of risk score) with respect to patients in the low-risk group (first and second quintiles of risk score) were both greater than 4. HRs for SCD and PFD increased to 9 and 11 when using a model including only ECG markers, and to 14 and 13, when combining clinical and ECG markers. Conclusion The inclusion of ECG markers capturing complementary pro-arrhythmic and pump failure mechanisms into risk models based only on standard clinical variables substantially improves prediction of SCD and PFD in CHF patients. PMID:29020031

Sudden cardiac death and pump failure death prediction in chronic heart failure by combining ECG and clinical markers in an integrated risk model.

PubMed

Ramírez, Julia; Orini, Michele; Mincholé, Ana; Monasterio, Violeta; Cygankiewicz, Iwona; Bayés de Luna, Antonio; Martínez, Juan Pablo; Laguna, Pablo; Pueyo, Esther

2017-01-01

Sudden cardiac death (SCD) and pump failure death (PFD) are common endpoints in chronic heart failure (CHF) patients, but prevention strategies are different. Currently used tools to specifically predict these endpoints are limited. We developed risk models to specifically assess SCD and PFD risk in CHF by combining ECG markers and clinical variables. The relation of clinical and ECG markers with SCD and PFD risk was assessed in 597 patients enrolled in the MUSIC (MUerte Súbita en Insuficiencia Cardiaca) study. ECG indices included: turbulence slope (TS), reflecting autonomic dysfunction; T-wave alternans (TWA), reflecting ventricular repolarization instability; and T-peak-to-end restitution (ΔαTpe) and T-wave morphology restitution (TMR), both reflecting changes in dispersion of repolarization due to heart rate changes. Standard clinical indices were also included. The indices with the greatest SCD prognostic impact were gender, New York Heart Association (NYHA) class, left ventricular ejection fraction, TWA, ΔαTpe and TMR. For PFD, the indices were diabetes, NYHA class, ΔαTpe and TS. Using a model with only clinical variables, the hazard ratios (HRs) for SCD and PFD for patients in the high-risk group (fifth quintile of risk score) with respect to patients in the low-risk group (first and second quintiles of risk score) were both greater than 4. HRs for SCD and PFD increased to 9 and 11 when using a model including only ECG markers, and to 14 and 13, when combining clinical and ECG markers. The inclusion of ECG markers capturing complementary pro-arrhythmic and pump failure mechanisms into risk models based only on standard clinical variables substantially improves prediction of SCD and PFD in CHF patients.

Cost Implications of Using Different ECG Criteria for Screening Young Athletes in the United Kingdom.

PubMed

Dhutia, Harshil; Malhotra, Aneil; Gabus, Vincent; Merghani, Ahmed; Finocchiaro, Gherardo; Millar, Lynne; Narain, Rajay; Papadakis, Michael; Naci, Huseyin; Tome, Maite; Sharma, Sanjay

2016-08-16

High false-positive rates and cost of additional investigations are an obstacle to electrocardiographic (ECG) screening of young athletes for cardiac disease. However, ECG screening costs have never been systematically assessed in a large cohort of athletes. This study investigated the costs of ECG screening in athletes according to the 2010 European Society of Cardiology (ESC) recommendations and the Seattle and refined interpretation criteria. Between 2011 and 2014, 4,925 previously unscreened athletes aged 14 to 35 years were prospectively evaluated with history, physical examination, and ECG (interpreted with the 2010 ESC recommendations). Athletes with abnormal results underwent secondary investigations, the costs of which were based on U.K. National Health Service Tariffs. The impact on cost after applying the Seattle and refined criteria was evaluated retrospectively. Overall, 1,072 (21.8%) athletes had an abnormal ECG on the basis of 2010 ESC recommendations; 11.2% required echocardiography, 1.7% exercise stress test, 1.2% Holter, 1.2% cardiac magnetic resonance imaging, and 0.4% other tests. The Seattle and refined criteria reduced the number of positive ECGs to 6.0% and 4.3%, respectively. Fifteen (0.3%) athletes were diagnosed with potentially serious cardiac disease using all 3 criteria. The overall cost of de novo screening using 2010 ESC recommendations was $539,888 ($110 per athlete and $35,993 per serious diagnosis). The Seattle and refined criteria reduced the cost to $92 and $87 per athlete screened and $30,251 and $28,510 per serious diagnosis, respectively. Contemporary ECG interpretation criteria decrease costs for de novo screening of athletes, which may be cost permissive for some sporting organizations. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Economic analysis of the use of coronary calcium scoring as an alternative to stress ECG in the non-invasive diagnosis of coronary artery disease.

PubMed

Raman, Vivek; McWilliams, Eric T M; Holmberg, Stephen R M; Miles, Ken

2012-03-01

To conduct an economic analysis (EA) of coronary calcium scoring (CCS) using a 0 score, as alternative to stress electrocardiography (sECG) in diagnosing coronary artery disease (CAD). A decision tree was constructed to compare four strategies for investigation of suspected CAD previously assessed in the formulation of clinical guidelines for the United Kingdom (UK) to two new strategies incorporating CCS. Sensitivity (96%; 95% CI 95.4-96.4%) and specificity (40%; 95% CI 38.7-41.4%) values for CCS were derived from a meta-analysis of 10,760 patients. Other input variables were obtained from a previous EA and average prices for hospital procedures in the UK. A threshold of £30,000/Quality-adjusted Life Year (QALY) was considered cost-effective. Using net monetary benefit calculations, CCS-based strategies were found to be cost-effective compared to sECG equivalents at all assessed prevalence of CAD. Using CCS prior to myocardial perfusion scintigraphy (MPS) and catheter angiography (CA) was found to be cost-effective at pre-test probabilities (PTP) below 30%. Adoption of CCS as an alternative to sECG in investigating suspected stable angina in low PTP population (<30%) would be cost-effective. In patients with PTP of CAD >30%, proceeding to MPS or CA would be more cost-effective than performing either CCS or sECG. Coronary calcium scoring (CCS) is useful for assessing coronary artery atherosclerosis It can be performed with multi-detector CT, which is now widely available It plays a role in excluding disease in suspected stable angina Our study assesses its role in this setting as alternative to stress-ECG Adoption of CCS as an alternative to sECG could prove cost-effective.

Human Identification by Cross-Correlation and Pattern Matching of Personalized Heartbeat: Influence of ECG Leads and Reference Database Size.

PubMed

Jekova, Irena; Krasteva, Vessela; Schmid, Ramun

2018-01-27

Human identification (ID) is a biometric task, comparing single input sample to many stored templates to identify an individual in a reference database. This paper aims to present the perspectives of personalized heartbeat pattern for reliable ECG-based identification. The investigations are using a database with 460 pairs of 12-lead resting electrocardiograms (ECG) with 10-s durations recorded at time-instants T1 and T2 > T1 + 1 year. Intra-subject long-term ECG stability and inter-subject variability of personalized PQRST (500 ms) and QRS (100 ms) patterns is quantified via cross-correlation, amplitude ratio and pattern matching between T1 and T2 using 7 features × 12-leads. Single and multi-lead ID models are trained on the first 230 ECG pairs. Their validation on 10, 20, ... 230 reference subjects (RS) from the remaining 230 ECG pairs shows: (i) two best single-lead ID models using lead II for a small population RS = (10-140) with identification accuracy AccID = (89.4-67.2)% and aVF for a large population RS = (140-230) with AccID = (67.2-63.9)%; (ii) better performance of the 6-lead limb vs. the 6-lead chest ID model-(91.4-76.1)% vs. (90.9-70)% for RS = (10-230); (iii) best performance of the 12-lead ID model-(98.4-87.4)% for RS = (10-230). The tolerable reference database size, keeping AccID > 80%, is RS = 30 in the single-lead ID scenario (II); RS = 50 (6 chest leads); RS = 100 (6 limb leads), RS > 230-maximal population in this study (12-lead ECG).

Electrocardiogram abnormalities and coronary calcification in postmenopausal women.

PubMed

Sabour, Siamak; Grobbee, Diederick; Rutten, Annemarieke; Prokop, Mathias; Bartelink, Marie-Louise; van der Schouw, Yvonne; Bots, Michiel

2010-01-01

An electrocardiogram (ECG) can provide information on subclinical myocardial damage. The presence, and more importantly, the quantity of coronary artery calcification (CAC), relates well with the overall severity of the atherosclerotic process. A strong relation has been demonstrated between coronary calcium burden and the incidence of myocardial infarction, a relation independent of age. The aim of this study was to assess the relation of left ventricular hypertrophy (LVH) and ECG abnormalities with CAC. The study population comprised 566 postmenopausal women selected from a population-based cohort study. Information on LVH and repolarization abnormalities (T-axis and QRS-T angle) was obtained using electrocardiography. Modular ECG Analysis System (MEANS) was used to assess ECG abnormalities. The women underwent a multi detector-row computed tomography (MDCT) scan (Philips Mx 8000 IDT 16) to assess CAC. The Agatston score was used to quantify CAC; scores greater than zero were considered as the presence of coronary calcium. Logistic regression was used to assess the relation of ECG abnormality with coronary calcification. LVH was found in 2.7% (n = 15) of the women. The prevalence of T-axis abnormality was 6% (n = 34), whereas 8.5% (n = 48) had a QRS-T angle abnormality. CAC was found in 62% of the women. Compared to women with a normal T-axis, women with borderline or abnormal T-axes were 3.8 fold more likely to have CAC (95% CI: 1.4-10.2). Similarly, compared to women with a normal QRS-T angle, in women with borderline or abnormal QRS-T angle, CAC was 2.0 fold more likely to be present (95% CI: 1.0-4.1). Among women with ECG abnormalities reflecting subclinical ischemia, CAC is commonly found and may in part explain the increased coronary heart disease risk associated with these ECG abnormalities.

Wavelet-based unsupervised learning method for electrocardiogram suppression in surface electromyograms.

PubMed

Niegowski, Maciej; Zivanovic, Miroslav

2016-03-01

We present a novel approach aimed at removing electrocardiogram (ECG) perturbation from single-channel surface electromyogram (EMG) recordings by means of unsupervised learning of wavelet-based intensity images. The general idea is to combine the suitability of certain wavelet decomposition bases which provide sparse electrocardiogram time-frequency representations, with the capacity of non-negative matrix factorization (NMF) for extracting patterns from images. In order to overcome convergence problems which often arise in NMF-related applications, we design a novel robust initialization strategy which ensures proper signal decomposition in a wide range of ECG contamination levels. Moreover, the method can be readily used because no a priori knowledge or parameter adjustment is needed. The proposed method was evaluated on real surface EMG signals against two state-of-the-art unsupervised learning algorithms and a singular spectrum analysis based method. The results, expressed in terms of high-to-low energy ratio, normalized median frequency, spectral power difference and normalized average rectified value, suggest that the proposed method enables better ECG-EMG separation quality than the reference methods. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Prevalence of hypertrophic cardiomyopathy on an electrocardiogram-based pre-participation screening programme in a young male South-East Asian population: results from the Singapore Armed Forces Electrocardiogram and Echocardiogram screening protocol.

PubMed

Ng, Choon Ta; Chee, Tek Siong; Ling, Lee Fong; Lee, Yian Ping; Ching, Chi Keong; Chua, Terrance S J; Cheok, Christopher; Ong, Hean Yee

2011-06-01

Hypertrophic cardiomyopathy is a leading cause of sudden cardiac death (SCD) in young people in the USA. Pre-participation screening for athletes might reduce the incidence of SCD. In Singapore, military service is compulsory for all young able-bodied male citizens. The Singapore Armed Forces Electrocardiogram and Echocardiogram (SAFE) pre-participation screening protocol based on the Italian programme was introduced. This study evaluates the prevalence of hypertrophic cardiomyopathy (HCM) in a young male South-East Asian population. From October 2008 to May 2009, all male military conscripts underwent pre-participation screening. For all conscripts whose electrocardiogram (ECG) findings fulfilled any of these pre-specified criteria (Group A), direct referral for a transthoracic echocardiogram was mandatory. Conscripts with ECG findings other than pre-specified criteria (e.g. T-wave inversions, repolarization abnormalities) were referred for secondary screening by cardiologists (Group B), which could include echocardiography. Out of 18 476 subjects screened during the study period, 988 (5.3%) subjects were fast tracked for echocardiogram (Group A). Of them, there were three (0.3%) cases with severe abnormalities; there was one case each of HCM, bicuspid aortic valve with significant aortic valve regurgitation, and atrial septal defect with right ventricular systolic dysfunction. The patient with HCM had left axis deviation on ECG. None of the 215 patients who underwent echocardiography following cardiology consult (Group B) had HCM. The prevalence of HCM in our young male population (mean age 19.5, range 16-27) using an ECG-based screening protocol was 0.005%; this appeared lower than published data from other geographical cohorts. Possible explanations include a later age of phenotypic manifestation in our population, limitations of the ECG criteria for screening, or a truly lower prevalence of HCM. More population-based longitudinal studies would be needed to ascertain the true prevalence of HCM in our South-East Asian population.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices.

PubMed

He, Ziyang; Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

2018-04-17

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

PubMed Central

Zhang, Xiaoqing; Cao, Yangjie; Liu, Zhi; Zhang, Bo; Wang, Xiaoyan

2018-01-01

By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices. PMID:29673171

The availability of prior ECGs improves paramedic accuracy in recognizing ST-segment elevation myocardial infarction.

PubMed

O'Donnell, Daniel; Mancera, Mike; Savory, Eric; Christopher, Shawn; Schaffer, Jason; Roumpf, Steve

2015-01-01

Early and accurate identification of ST-elevation myocardial infarction (STEMI) by prehospital providers has been shown to significantly improve door to balloon times and improve patient outcomes. Previous studies have shown that paramedic accuracy in reading 12 lead ECGs can range from 86% to 94%. However, recent studies have demonstrated that accuracy diminishes for the more uncommon STEMI presentations (e.g. lateral). Unlike hospital physicians, paramedics rarely have the ability to review previous ECGs for comparison. Whether or not a prior ECG can improve paramedic accuracy is not known. The availability of prior ECGs improves paramedic accuracy in ECG interpretation. 130 paramedics were given a single clinical scenario. Then they were randomly assigned 12 computerized prehospital ECGs, 6 with and 6 without an accompanying prior ECG. All ECGs were obtained from a local STEMI registry. For each ECG paramedics were asked to determine whether or not there was a STEMI and to rate their confidence in their interpretation. To determine if the old ECGs improved accuracy we used a mixed effects logistic regression model to calculate p-values between the control and intervention. The addition of a previous ECG improved the accuracy of identifying STEMIs from 75.5% to 80.5% (p=0.015). A previous ECG also increased paramedic confidence in their interpretation (p=0.011). The availability of previous ECGs improves paramedic accuracy and enhances their confidence in interpreting STEMIs. Further studies are needed to evaluate this impact in a clinical setting. Copyright © 2015 Elsevier Inc. All rights reserved.

Predictors of electrocardiographic abnormalities in type 1 Diabetes: the Wisconsin Epidemiologic Study of Diabetic Retinopathy.

PubMed

O'Neal, Wesley T; Lee, Kristine E; Soliman, Elsayed Z; Klein, Ronald; Klein, Barbara E K

2017-03-01

To determine the incidence and determinants of developing abnormalities on the 12-lead electrocardiogram (ECG) in persons with type 1 diabetes. We evaluated the distribution of ECG abnormalities and risk factors for developing new abnormalities in 266 (mean age = 44 years ± 9.0; 50 % female) people with type 1 diabetes from the Wisconsin Epidemiologic Study of Diabetic Retinopathy. This analysis included participants with complete ECG data from study visit 5 (2000-2001) and follow-up ECGs from study visit 7 (2012-2014). ECG abnormalities were classified as major and minor according to Minnesota Code Classification. At baseline, 94 (35 %) participants had at least one ECG abnormality, including 13 major ECG abnormalities. At follow-up, 117 (44 %) participants developed at least one new ECG abnormality, including 35 new major ECG abnormalities. In a multivariable logistic regression model, older age (per 5-year increase: OR = 1.31, 95 % CI = 1.08, 1.60) was associated with the development of at least one new ECG abnormality, while serum HDL cholesterol (per 10-unit increase: OR = 0.98, 95 % CI = 0.96, 1.00) was protective against developing new ECG abnormalities. The development of new ECG abnormalities is common in type 1 diabetes. Older age and HDL cholesterol are independent risk factors for developing new ECG abnormalities. Further research is needed to determine whether routine ECG screening is indicated in people with type 1 diabetes to identify those with underlying subclinical coronary heart disease.

Cohort Study of ECG Left Ventricular Hypertrophy Trajectories: Ethnic Disparities, Associations With Cardiovascular Outcomes, and Clinical Utility.

PubMed

Iribarren, Carlos; Round, Alfred D; Lu, Meng; Okin, Peter M; McNulty, Edward J

2017-10-05

ECG left ventricular hypertrophy (LVH) is a well-known predictor of cardiovascular disease. However, no prior study has characterized patterns of presence/absence of ECG LVH ("ECG LVH trajectories") across the adult lifespan in both sexes and across ethnicities. We examined: (1) correlates of ECG LVH trajectories; (2) the association of ECG LVH trajectories with incident coronary heart disease, transient ischemic attack, ischemic stroke, hemorrhagic stroke, and heart failure; and (3) reclassification of cardiovascular disease risk using ECG LVH trajectories. We performed a cohort study among 75 412 men and 107 954 women in the Northern California Kaiser Permanente Medical Care Program who had available longitudinal exposures of ECG LVH and covariates, followed for a median of 4.8 (range <1-9.3) years. ECG LVH was measured by Cornell voltage-duration product. Adverse trajectories of ECG LVH (persistent, new development, or variable pattern) were more common among blacks and Native American men and were independently related to incident cardiovascular disease with hazard ratios ranging from 1.2 for ECG LVH variable pattern and transient ischemic attack in women to 2.8 for persistent ECG LVH and heart failure in men. ECG LVH trajectories reclassified 4% and 7% of men and women with intermediate coronary heart disease risk, respectively. ECG LVH trajectories were significant indicators of coronary heart disease, stroke, and heart failure risk, independently of level and change in cardiovascular disease risk factors, and may have clinical utility. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Reconfigurable wearable to monitor physiological variables and movement

NASA Astrophysics Data System (ADS)

Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnación; García, Antonio; Tahmassebi, Amirhessam; Meyer-Baese, Anke

2017-05-01

This article presents a preliminary prototype of a wearable instrument for oxygen saturation and ECG monitoring. The proposed measuring system is based on the light reflection variability of a LED emission on the subject temple. Besides, the system has the capacity to incorporate electrodes to obtain ECG measurements. All measurements are stored and transmitted to a mobile device (tablet or smartphone) through a Bluetooth link.

The use of equine chorionic gonadotropin in the treatment of anestrous dairy cows in gonadotropin-releasing hormone/progesterone protocols of 6 or 7 days.

PubMed

Bryan, M A; Bó, G; Mapletoft, R J; Emslie, F R

2013-01-01

In seasonally calving, pasture-based dairy farm systems, the interval from calving to first estrus is a critical factor affecting reproductive efficiency. This study evaluated the effects of equine chorionic gonadotropin (eCG) on the reproductive response of lactating, seasonally calving dairy cows diagnosed with anovulatory anestrus by rectal palpation. Cows on 15 commercial dairy farms were selected for initial inclusion based on nonobserved estrus by 7 d before the planned start of mating. All cows were palpated rectally and evaluated for body condition score and ovary score, and were included for treatment according to the trial protocol if diagnosed with anovulatory anestrus. All cows received a standard anestrous treatment protocol consisting of insertion of a progesterone device, injection of 100 µg of GnRH at the time of device insertion, and injection of PGF(2α) at device removal (GPG/P4). Cows were randomly assigned to 1 of 2 groups (6 d or 7 d) for duration of progesterone device insertion. Within each of these groups, cows were further randomly assigned to receive either 400 IU of eCG at device removal or to remain untreated as controls, resulting in a 2×2 arrangement of treatment groups: (1) 6-d device and no eCG (n=484); (2) 6-d device and eCG (n=462); (3) 7-d device and no eCG (n=546); and (4) 7-d device and eCG (n=499). Cows were detected for estrus from the time of progesterone device removal and were inseminated; those not detected in estrus within 60 h after progesterone device removal received 100 µg of GnRH and were inseminated at 72 h. The primary outcomes considered were proportion of cows conceiving within 7 d of the beginning of breeding (7-d conception rate; 7-d CR), proportion pregnant within 28 d (28-d in calf rate; 28-d ICR), and days to conception (DTC). We found no significant differences between the 6- and 7-d insertion periods and found no 6- or 7-d insertion period × eCG treatment interactions. Inclusion of eCG into either length of GPG/P4 protocol increased 7-d CR (36.0 vs. 30.6%) and 28-d ICR (58.6 vs. 52.3%) and decreased median days to conception. The use of eCG in GPG/P4 breeding protocols will improve reproductive efficiency in seasonally calving, anestrous dairy cattle. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A remote access ecg monitoring system - biomed 2009.

PubMed

Ogawa, Hidekuni; Yonezawa, Yoshiharu; Maki, Hiromichi; Iwamoto, Junichi; Hahn, Allen W; Caldwell, W Morton

2009-01-01

We have developed a remotely accessible telemedicine system for monitoring a patient's electrocardiogram (ECG). The system consists of an ECG recorder mounted on chest electrodes and a physician's laptop personal computer. This ECG recorder is designed with a variable gain instrumentation amplifier; a low power 8-bit single-chip microcomputer; two 128KB EEPROMs and 2.4 GHz low transmit power mobile telephone. When the physician wants to monitor the patient's ECG, he/she calls directly from the laptop PC to the ECG recorder's phone and the recorder sends the ECG to the computer. The electrode-mounted recorder continuously samples the ECG. Additionally, when the patient feels a heart discomfort, he/she pushes a data transmission switch on the recorder and the recorder sends the recorded ECG waveforms of the two prior minutes, and for two minutes after the switch is pressed. The physician can display and monitor the data on the computer's liquid crystal display.

Cancelable ECG biometrics using GLRT and performance improvement using guided filter with irreversible guide signal.

PubMed

Kim, Hanvit; Minh Phuong Nguyen; Se Young Chun

2017-07-01

Biometrics such as ECG provides a convenient and powerful security tool to verify or identify an individual. However, one important drawback of biometrics is that it is irrevocable. In other words, biometrics cannot be re-used practically once it is compromised. Cancelable biometrics has been investigated to overcome this drawback. In this paper, we propose a cancelable ECG biometrics by deriving a generalized likelihood ratio test (GLRT) detector from a composite hypothesis testing in randomly projected domain. Since it is common to observe performance degradation for cancelable biometrics, we also propose a guided filtering (GF) with irreversible guide signal that is a non-invertibly transformed signal of ECG authentication template. We evaluated our proposed method using ECG-ID database with 89 subjects. Conventional Euclidean detector with original ECG template yielded 93.9% PD1 (detection probability at 1% FAR) while Euclidean detector with 10% compressed ECG (1/10 of the original data size) yielded 90.8% PD1. Our proposed GLRT detector with 10% compressed ECG yielded 91.4%, which is better than Euclidean with the same compressed ECG. GF with our proposed irreversible ECG template further improved the performance of our GLRT with 10% compressed ECG up to 94.3%, which is higher than Euclidean detector with original ECG. Lastly, we showed that our proposed cancelable ECG biometrics practically met cancelable biometrics criteria such as efficiency, re-usability, diversity and non-invertibility.

A novel ECG detector performance metric and its relationship with missing and false heart rate limit alarms.

PubMed

Daluwatte, Chathuri; Vicente, Jose; Galeotti, Loriano; Johannesen, Lars; Strauss, David G; Scully, Christopher G

Performance of ECG beat detectors is traditionally assessed on long intervals (e.g.: 30min), but only incorrect detections within a short interval (e.g.: 10s) may cause incorrect (i.e., missed+false) heart rate limit alarms (tachycardia and bradycardia). We propose a novel performance metric based on distribution of incorrect beat detection over a short interval and assess its relationship with incorrect heart rate limit alarm rates. Six ECG beat detectors were assessed using performance metrics over long interval (sensitivity and positive predictive value over 30min) and short interval (Area Under empirical cumulative distribution function (AUecdf) for short interval (i.e., 10s) sensitivity and positive predictive value) on two ECG databases. False heart rate limit and asystole alarm rates calculated using a third ECG database were then correlated (Spearman's rank correlation) with each calculated performance metric. False alarm rates correlated with sensitivity calculated on long interval (i.e., 30min) (ρ=-0.8 and p<0.05) and AUecdf for sensitivity (ρ=0.9 and p<0.05) in all assessed ECG databases. Sensitivity over 30min grouped the two detectors with lowest false alarm rates while AUecdf for sensitivity provided further information to identify the two beat detectors with highest false alarm rates as well, which was inseparable with sensitivity over 30min. Short interval performance metrics can provide insights on the potential of a beat detector to generate incorrect heart rate limit alarms. Published by Elsevier Inc.

Microvascularization of corpus luteum of bovine treated with equine chorionic gonadotropin.

PubMed

Moura, Carlos Eduardo Bezerra; Rigoglio, Nathia Nathaly; Braz, Janine Karla França S; Machado, Marcello; Baruselli, Pietro S; Papa, Paula De Carvalho

2015-09-01

This study aimed to evaluate the morphological changes in microvascular density and corpus luteum (CL) vascularization in cows treated with eCG during stimulatory and superovulatory protocols. Sixteen cows were synchronized and divided into three groups: control (n = 6), stimulated (n = 4) and superovulated (n =6), one was submitted to estrous synchronization (ES) and received no eCG (control), and those that were submitted to ES and received eCG before or after follicular deviation (superovulation and stimulation of the dominant follicle, respectively). Ovulation was synchronized using a progesterone device-based protocol. After six days of ovulation, the cows were slaughtered and the ovaries and CL were collected. The CLs were processed and photomicrographs were taken under light microscopy to assess the vascular volume density (Vv) by stereology, and scanning electron microscopy (SEM) was used to perform ultrastructural analysis of the microvasculature. The Vv in stimulated and superovulated cows significantly increased (P ≤ 0.0001) when compared to control, indicating that the eCG is able to induce angiogenic activity in bovine CL. However, no significant differences were observed between stimulated and superovulated cows. The SEM demonstrated ratings indicative of angiogenesis, marked by several button-shaped projections in the capillaries, and the presence of more dilated capillaries in CL treated with eCG. These morphological findings are evidence of an angiogenic effect of the eCG treatment in CL of cows. © 2015 Wiley Periodicals, Inc.

Wearable technology and ECG processing for fall risk assessment, prevention and detection.

PubMed

Melillo, Paolo; Castaldo, Rossana; Sannino, Giovanna; Orrico, Ada; de Pietro, Giuseppe; Pecchia, Leandro

2015-01-01

Falls represent one of the most common causes of injury-related morbidity and mortality in later life. Subjects with cardiovascular disorders (e.g., related to autonomic dysfunctions and postural hypotension) are at higher risk of falling. Autonomic dysfunctions increasing the risk of falling in the short and mid-term could be assessed by Heart Rate Variability (HRV) extracted by electrocardiograph (ECG). We developed three trials for assessing the usefulness of ECG monitoring using wearable devices for: risk assessment of falling in the next few weeks; prevention of imminent falls due to standing hypotension; and fall detection. Statistical and data-mining methods are adopted to develop classification and regression models, validated with the cross-validation approach. The first classifier based on HRV features enabled to identify future fallers among hypertensive patients with an accuracy of 72% (sensitivity: 51.1%, specificity: 80.2%). The regression model to predict falls due to orthostatic dropdown from HRV recorded before standing achieved an overall accuracy of 80% (sensitivity: 92%, specificity: 90%). Finally, the classifier to detect simulated falls using ECG achieved an accuracy of 77.3% (sensitivity: 81.8%, specificity: 72.7%). The evidence from these three studies showed that ECG monitoring and processing could achieve satisfactory performances compared to other system for risk assessment, fall prevention and detection. This is interesting as differently from other technologies actually employed to prevent falls, ECG is recommended for many other pathologies of later life and is more accepted by senior citizens.

Myocardial complications of immunisations.

PubMed

Helle, E P; Koskenvuo, K; Heikkilä, J; Pikkarainen, J; Weckström, P

1978-10-01

Immunisation may induce myocardial complications. In this pilot study clinical, electrocardiographic, chemical and immunological findings have been studied during a six weeks' follow-up after routine immunisation (mumps, polio, tetanus, smallpox, diphtheria and type A meningococcal disease) among 234 Finnish conscripts at the beginning of their military service. Serial pattern of ECG changes suggestive of myocarditis was recorded in eight of the 234 conscripts one to two weeks after vaccination against smallpox and diphtheria. Changes were mainly minor ST segment elevations and T wave inversions and usually they disappeared in a few weeks. The ECG positives more often had a history of atopy, and their mean body temperatures and heart rates after the vaccinations were higher than among the other subjects (p less than 0.01). However, clinical myocarditis was never noted, nor were immunological or enzymological changes different among the ECG positives. Thus in 3% of the study population, evidence of postvaccinal myocarditis was noted, based on serial ECG patterns, but without any other evidence of cardiac disease.

[Research on automatic external defibrillator based on DSP].

PubMed

Jing, Jun; Ding, Jingyan; Zhang, Wei; Hong, Wenxue

2012-10-01

Electrical defibrillation is the most effective way to treat the ventricular tachycardia (VT) and ventricular fibrillation (VF). An automatic external defibrillator based on DSP is introduced in this paper. The whole design consists of the signal collection module, the microprocessor controlingl module, the display module, the defibrillation module and the automatic recognition algorithm for VF and non VF, etc. This automatic external defibrillator has achieved goals such as ECG signal real-time acquisition, ECG wave synchronous display, data delivering to U disk and automatic defibrillate when shockable rhythm appears, etc.