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Paper details
Number 3 - September 2022
Volume 32 - 2022
A hybrid approach of a deep learning technique for real-time ECG beat detection
Kiran Kumar Patro, Allam Jaya Prakash, Saunak Samantray, Joanna Pławiak, Ryszard Tadeusiewicz, Paweł Pławiak
Abstract
This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bio-electrical signal that helps monitor the heart’s electrical activity. It can provide health information about the normal and abnormal physiology of the heart. Early diagnosis of cardiac abnormalities is critical
for cardiac patients to avoid stroke or sudden cardiac death. The main aim of this paper is to detect crucial beats that can
damage the functioning of the heart. Initially, a modified Pan–Tompkins algorithm identifies the characteristic points, followed by heartbeat segmentation. Subsequently, a different hybrid deep convolutional neural network (CNN) is proposed to experiment on standard and real-time long-term ECG databases. This work successfully classifies several cardiac beat abnormalities such as supra-ventricular ectopic beats (SVE), ventricular beats (VE), intra-ventricular conduction disturbances
beats (IVCD), and normal beats (N). The obtained classification results show a better accuracy of 99.28% with an F1 score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.
Keywords
cardiac abnormalities, CAD, convolutional neural network (CNN), deep learning, ECG, supra-ventricular ectopic beats (SVE)