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

• Autorzy: Patro Kiran Kumar , Prakash Allam Jaya , Samantray Saunak , Pławiak Joanna , Tadeusiewicz Ryszard , Pławiak Paweł

Identyfikatory

DOI

10.34768/amcs-2022-0033

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

cardiac abnormalities; CAD; convolutional neural network; CNN; deep learning; ECG; electrocardiogram; supra ventricular ectopic beats; SVE

PL

nieprawidłowości kardiologiczne; CAD; sieć neuronowa konwolucyjna; CNN; uczenie głębokie; EKG; elektrokardiogram; SVE

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2022
• Tom: Vol. 32, no. 3
• Strony: 455--465
• Opis fizyczny: Bibliogr. 54 poz., rys., tab., wykr.

Twórcy

autor

Patro Kiran Kumar

• Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management, K Kotturu, Tekkali, Andhra Pradesh-532201, India

autor

Prakash Allam Jaya

• Department of Electronics and Communication Engineering, National Institute of Technology Rourkela, Odisha-769008, India

autor

Samantray Saunak

• Department of Electronics and Telecommunications, IIIT Bhubaneswar, Bhubaneswar-751003, India

autor

Pławiak Joanna

• Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland

autor

Tadeusiewicz Ryszard

• Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza 30, 30-059 Cracow, Poland

autor

Pławiak Paweł

• Department of Computer Science, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland
• Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, Poland

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)