Ectopic Heartbeat Detection from ECG Signals using Deep Convolutional Neural Networks

Hasitha Kuruwita, Ng Shu Kay, Alan Liew, Brent Richards, Kelvin Ross, Kuldeep Kumar, Luke Haseler, Meghan McConnell, Ping Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Electrocardiogram (ECG) signal analysis is widely used to diagnose various cardiac and non-cardiac diseases. Detecting abnormalities on ECG is critical for preventing the onset of life-threatening cardiac arrhythmias. This paper proposed a method based on deep convolutional neural network (DCNN) to detect abnormal heartbeats such as ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB). The proposed model was trained and validated on two large-sample PhysioNet's MIT-BIH datasets. A separate test result showed overall accuracy of 96% on distinguishing three types of heartbeats VEB, SVEB, and other heartbeats which are not ectopic beat (NOTEB).

Original languageEnglish
Title of host publication2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
EditorsDonald Adjeroh, Qi Long, Xinghua Shi, Fei Guo, Xiaohua Hu, Srinivas Aluru, Giri Narasimhan, Jianxin Wang, Mingon Kang, Ananda M. Mondal, Jin Liu
PublisherIEEE
Pages3535-3540
Number of pages6
ISBN (Electronic)978-1-6654-6819-0
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Bioinformatics and Biomedicine - Las Vegas, USA & Changsha, China
Duration: 6 Dec 20228 Dec 2022

Publication series

NameProceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

Conference

Conference2022 IEEE International Conference on Bioinformatics and Biomedicine
Abbreviated titleBIBM
Period6/12/228/12/22

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