Automated ECG Classification Using Dual Heartbeat Coupling Based on Convolutional Neural Network
Xiaolong Zhai,Chung Tin +1 more
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TLDR
The proposed CNN classifier with an automatic training beats selection process has shown to outperform the previous methods and provides a reliable and fully automatic tool for detection of arrhythmia heartbeat without the need for manual feature extraction or expert assistant.Abstract:
A high performance electrocardiogram (ECG)-based arrhythmic beats classification system is presented in this paper. The classifier was designed based on convolutional neural network (CNN). Single channel ECG signal was segmented into heartbeats in accordance with the changing heartbeat rate. The beats were transformed into dual beat coupling matrix as 2-D inputs to the CNN classifier, which captured both beat morphology and beat-to-beat correlation in ECG. A systematic training beat selection procedure was also proposed which automatically include the most representative beats into the training set to improve classification performance. The classification system was evaluated for the detection of supraventricular ectopic beats (SVEB or S beats) and VEB using the MIT-BIH arrhythmia database. Our proposed method has demonstrated superior performance than several state-of-the-art detectors. In particular, our proposed CNN system has improved sensitivity and positive predictive rate for S beats by more than 12.2% and 11.9%, respectively, over these top performing algorithms. Our proposed CNN classifier with an automatic training beats selection process has shown to outperform the previous methods. The classifier is also a personalized one by combining training set from a common pool and a subject-specific set of ECG data. Our proposed system provides a reliable and fully automatic tool for detection of arrhythmia heartbeat without the need for manual feature extraction or expert assistant. It can potentially be implemented on portable device for the long-term monitoring of cardiac arrhythmia.read more
Citations
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Journal ArticleDOI
A Review on Deep Learning Methods for ECG Arrhythmia Classification
TL;DR: A comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes, which showed high accuracy in correct classification of Atrial Fibrillation, Supraventricular ECTopic Beats, and Ventricular Ectopic Beats using the GRU, CNN, and LSTM, respectively.
Journal ArticleDOI
Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review.
TL;DR: A systematic review of deep learning methods for ECG data from both modeling and application perspectives found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results.
Journal ArticleDOI
Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review
Fatma Murat,Ozal Yildirim,Muhammed Talo,Ulas Baran Baloglu,Yakup Demir,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +7 more
TL;DR: A detailed examination of deep learning methods for ECG arrhythmia detection is provided, and suggestions for further research in this area are presented.
Posted Content
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
TL;DR: A systematic review of deep learning methods for electrocardiogram (ECG) data from both modeling and application perspectives is presented in this paper, which highlights existing challenges and problems to identify potential future research directions.
Journal ArticleDOI
Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM
TL;DR: A new method for automatic classification of arrhythmias based on deep neural networks (DNNs) based on two DNN models constitutive of residual convolutional modules and bidirectional long short-term memory layers are trained to extract features from raw ECG signals.
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