Key result
A customized hybrid deep convolutional neural network classified cardiac beat abnormalities with 99.28% accuracy on the MIT-BIH database and 99.12% accuracy on a real-time acquired database.
Why the study?
Early detection of cardiac abnormalities is critical to avoid stroke or sudden cardiac death by identifying crucial beats that can damage heart function.
A novel hybrid deep learning CNN approach demonstrated >99% accuracy in classifying normal and abnormal ECG beats (SVE, VE, IVCD) in both standard and real-time databases, offering a potential tool for automated arrhythmia detection.
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.
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Patro et al. (2022) studied Cardiac abnormalities (n=15). Hybrid deep convolutional neural network (CNN) was evaluated on Classification accuracy of ECG beats. A customized hybrid deep convolutional neural network classified cardiac beat abnormalities with 99.28% accuracy on the MIT-BIH database and 99.12% accuracy on a real-time acquired database.
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