<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 <italic>F</italic>1score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.</p>
</abstract>

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 F1score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.

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

Department of Electronics and Communication Engineering

Department of Electronics and Telecommunications

Department of Biocybernetics and Biomedical Engineering

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bioelectrical...

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

Publicado en línea: 08 oct 2022

Páginas: 455 - 465

Recibido: 27 ene 2022

Aceptado: 10 jun 2022

DOI: https://doi.org/10.34768/amcs-2022-0033

Palabras clave
cardiac abnormalities, CAD, convolutional neural network (CNN), deep learning, ECG, supra-ventricular ec-topic beats (SVE)

© 2022 Kiran Kumar Patro et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Publicado en línea: 08 oct 2022

Páginas: 455 - 465

Recibido: 27 ene 2022

Aceptado: 10 jun 2022

DOI: https://doi.org/10.34768/amcs-2022-0033

Palabras clavecardiac abnormalities, CAD, convolutional neural network (CNN), deep learning, ECG, supra-ventricular ec-topic beats (SVE)

© 2022 Kiran Kumar Patro et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
cardiac abnormalities, CAD, convolutional neural network (CNN), deep learning, ECG, supra-ventricular ec-topic beats (SVE)