A Hybrid Approach of a Deep Learning Technique for Real–Time ECG Beat Detection

• Published in: International journal of applied mathematics and computer science Vol. 32; no. 3; pp. 455 - 465
• Main Authors: Patro, Kiran Kumar, Prakash, Allam Jaya, Samantray, Saunak, Pławiak, Joanna, Tadeusiewicz, Ryszard, Pławiak, Paweł
• Format: Journal Article
• Language: English
• Published: Zielona Góra Sciendo 01.09.2022; De Gruyter Poland
• Subjects: Abnormalities; Algorithms; Artificial neural networks; Bioelectricity; CAD; cardiac abnormalities; convolutional neural network (CNN); Deep learning; ECG; Electrocardiography; Machine learning; Real time; supra-ventricular ec-topic beats (SVE)
• ISSN: 1641-876X; 2083-8492
• DOI: 10.34768/amcs-2022-0033

This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bioelectrical 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 1score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.