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An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification / Ehab Essa, Xianghua Xie

IEEE Access, Volume: 9, Pages: 103452 - 103464

Swansea University Author: Xianghua Xie

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Abstract

An automatic system for heart arrhythmia classification can perform a substantial role inmanaging and treating cardiovascular diseases. In this paper, a deep learning-based multi-model system is proposed for the classification of electrocardiogram (ECG) signals. Two different deep learning bagging m...

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Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
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spelling 2021-09-09T15:34:14.1063865 v2 57528 2021-08-05 An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2021-08-05 SCS An automatic system for heart arrhythmia classification can perform a substantial role inmanaging and treating cardiovascular diseases. In this paper, a deep learning-based multi-model system is proposed for the classification of electrocardiogram (ECG) signals. Two different deep learning bagging models are introduced to classify heartbeats into different arrhythmias types. The first model (CNN-LSTM) is based on a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture local features and temporal dynamics in the ECG data. The second model (RRHOS-LSTM) integrates some classical features, i.e. RR intervals and higher-order statistics (HOS), with LSTM model to effectively highlight abnormality heartbeats classes. We create a bagging model from the CNN-LSTM and RRHOS-LSTM networks by training each model on a different sub-sampling dataset to handle the high imbalance distribution of arrhythmias classes in the ECG data. Each model is also trained using a weighted loss function to provide high weight for not sufficiently represented classes. These models are then combined using a meta-classifier to form a strong coherent model. The meta-classifier is a feedforward fully connected neural network that takes the different predictions of bagging models as an input and combines them into a final prediction. The result of the meta-classifier is then verified by another CNN-LSTM model to decrease the false positive of the overall system. The experimental results are acquired by evaluating the proposed method on ECG data from the MIT-BIH arrhythmia database. The proposedmethod achieves an overall accuracy of 95.81% in the ‘‘subject-oriented’’ patient independent evaluation scheme. The averages of F1 score and positive predictive value are higher than all other methods by more than 3% and 8% respectively. The experimental results show the superiority of the proposed method for ECG heartbeats classification compared to many state-of-the-art methods. Journal Article IEEE Access 9 103452 103464 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 28 7 2021 2021-07-28 10.1109/access.2021.3098986 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University SU College/Department paid the OA fee Serˆ Cymru COFUND Fellowship 2021-09-09T15:34:14.1063865 2021-08-05T11:27:07.4017402 College of Science Computer Science Ehab Essa 1 Xianghua Xie 0000-0002-2701-8660 2 57528__20552__fdfe2314028147028e8d9acf971d9081.pdf An_Ensemble_of_Deep_Learning-Based_Multi-Model_for_ECG_Heartbeats_Arrhythmia_Classification-2.pdf 2021-08-05T11:33:29.1620478 Output 6933295 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution 4.0 License true eng
title An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification
spellingShingle An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification
Xianghua, Xie
title_short An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification
title_full An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification
title_fullStr An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification
title_full_unstemmed An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification
title_sort An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua, Xie
author Xianghua, Xie
author2 Ehab Essa
Xianghua Xie
format Journal article
container_title IEEE Access
container_volume 9
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publishDate 2021
institution Swansea University
issn 2169-3536
doi_str_mv 10.1109/access.2021.3098986
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str College of Science
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description An automatic system for heart arrhythmia classification can perform a substantial role inmanaging and treating cardiovascular diseases. In this paper, a deep learning-based multi-model system is proposed for the classification of electrocardiogram (ECG) signals. Two different deep learning bagging models are introduced to classify heartbeats into different arrhythmias types. The first model (CNN-LSTM) is based on a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) network to capture local features and temporal dynamics in the ECG data. The second model (RRHOS-LSTM) integrates some classical features, i.e. RR intervals and higher-order statistics (HOS), with LSTM model to effectively highlight abnormality heartbeats classes. We create a bagging model from the CNN-LSTM and RRHOS-LSTM networks by training each model on a different sub-sampling dataset to handle the high imbalance distribution of arrhythmias classes in the ECG data. Each model is also trained using a weighted loss function to provide high weight for not sufficiently represented classes. These models are then combined using a meta-classifier to form a strong coherent model. The meta-classifier is a feedforward fully connected neural network that takes the different predictions of bagging models as an input and combines them into a final prediction. The result of the meta-classifier is then verified by another CNN-LSTM model to decrease the false positive of the overall system. The experimental results are acquired by evaluating the proposed method on ECG data from the MIT-BIH arrhythmia database. The proposedmethod achieves an overall accuracy of 95.81% in the ‘‘subject-oriented’’ patient independent evaluation scheme. The averages of F1 score and positive predictive value are higher than all other methods by more than 3% and 8% respectively. The experimental results show the superiority of the proposed method for ECG heartbeats classification compared to many state-of-the-art methods.
published_date 2021-07-28T04:24:18Z
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score 10.830003