Ectopic Heartbeat Detection from ECG Signals using Deep Convolutional Neural Networks

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

doi:10.1109/bibm55620.2022.9995477

Ectopic Heartbeat Detection from ECG Signals using Deep Convolutional Neural Networks

A Hasitha Kuruwita, Brent Richards + Show 7 more

https://doi.org/10.1109/bibm55620.2022.9995477

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Publication Date: Dec 6, 2022

Affiliation: Griffith University, Gold Coast Hospital, Curtin University, Bond University

#Supraventricular Ectopic Beats #Ectopic Beats + Show 8 more

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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).

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