Object

Title: A hybrid approach of a deep learning technique for real-time ECG beat detection

Contributor:

Wiśniewski, Remigiusz - ed. ; Gomes, Luis - ed. ; Wan, Shaohua - ed.

Subtitle:

.

Group publication title:

AMCS, volume 32 (2022)

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:86457

DOI:

10.34768/amcs-2022-0033

Pages:

455-465

Source:

AMCS, volume 32, number 3 (2022) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

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