No Cover Image

Journal article 475 views 165 downloads

An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification

Ehab Essa, Xianghua Xie Orcid Logo

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

Swansea University Author: Xianghua Xie Orcid Logo

  • An_Ensemble_of_Deep_Learning-Based_Multi-Model_for_ECG_Heartbeats_Arrhythmia_Classification-2.pdf

    PDF | Version of Record

    This work is licensed under a Creative Commons Attribution 4.0 License

    Download (6.61MB)

Check full text

DOI (Published version): 10.1109/access.2021.3098986

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...

Full description

Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa57528
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2021-08-05T10:34:18Z
last_indexed 2021-09-10T03:20:15Z
id cronfa57528
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2021-09-09T15:34:14.1063865</datestamp><bib-version>v2</bib-version><id>57528</id><entry>2021-08-05</entry><title>An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification</title><swanseaauthors><author><sid>b334d40963c7a2f435f06d2c26c74e11</sid><ORCID>0000-0002-2701-8660</ORCID><firstname>Xianghua</firstname><surname>Xie</surname><name>Xianghua Xie</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-08-05</date><deptcode>SCS</deptcode><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 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 &#x2018;&#x2018;subject-oriented&#x2019;&#x2019; 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.</abstract><type>Journal Article</type><journal>IEEE Access</journal><volume>9</volume><journalNumber/><paginationStart>103452</paginationStart><paginationEnd>103464</paginationEnd><publisher>Institute of Electrical and Electronics Engineers (IEEE)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2169-3536</issnElectronic><keywords/><publishedDay>28</publishedDay><publishedMonth>7</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-07-28</publishedDate><doi>10.1109/access.2021.3098986</doi><url/><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm>SU College/Department paid the OA fee</apcterm><funders>Ser&#x2C6; Cymru COFUND Fellowship</funders><lastEdited>2021-09-09T15:34:14.1063865</lastEdited><Created>2021-08-05T11:27:07.4017402</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Ehab</firstname><surname>Essa</surname><order>1</order></author><author><firstname>Xianghua</firstname><surname>Xie</surname><orcid>0000-0002-2701-8660</orcid><order>2</order></author></authors><documents><document><filename>57528__20552__fdfe2314028147028e8d9acf971d9081.pdf</filename><originalFilename>An_Ensemble_of_Deep_Learning-Based_Multi-Model_for_ECG_Heartbeats_Arrhythmia_Classification-2.pdf</originalFilename><uploaded>2021-08-05T11:33:29.1620478</uploaded><type>Output</type><contentLength>6933295</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>This work is licensed under a Creative Commons Attribution 4.0 License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
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 Faculty of Science and Engineering School of Mathematics and Computer 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 https://creativecommons.org/licenses/by/4.0/
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
container_start_page 103452
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 Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
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 2021-07-28T04:13:20Z
_version_ 1763753906037325824
score 11.012678

Similar Items