Title: A precise deep learning-based ECG arrhythmia classification scheme using deep bidirectional capsule network classifier

Authors: Soumen Ghosh; Satish Chander

Addresses: Birla Institute of Technology, Mesra, Jharkhand, 835215, India ' Birla Institute of Technology, Mesra, Jharkhand, 835215, India

Abstract: This paper presents a novel approach for accurate ECG signal classification using a deep bidirectional capsule network classifier. ECGs are vital for diagnosing cardiac arrhythmias, and precise classification is crucial for automated heart disease prediction. Initially, ECG signal data is acquired and artifacts are removed using a quantised wavelet threshold method, followed by spectrogram analysis. Features are extracted using the VGG spectral net algorithm, and arrhythmia classification is performed with the deep bidirectional capsule network approach. The study evaluates this technique using the MIT-BIH arrhythmia database, identifying five types of arrhythmias: NOR, RBBB, PVC, LBBB, and APB. The results demonstrate improved accuracy compared to traditional methods, suggesting this deep learning approach could enhance diagnostic capabilities, streamline healthcare workflows, and improve patient outcomes.

Keywords: electrocardiogram; ECG; deep bidirectional capsule network; DBCN; VGG spectral net; quantised wavelet threshold method; ECG arrhythmias; NOR; APC; left bundle branch block; LBBB; right bundle branch block; RBBB; premature ventricular contraction; PVC.

DOI: 10.1504/IJCSE.2025.144809

International Journal of Computational Science and Engineering, 2025 Vol.28 No.2, pp.127 - 141

Received: 31 Aug 2023
Accepted: 23 May 2024
Published online: 03 Mar 2025 *

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