Title: A mobile-based deep learning technique for ECG beat classification

Authors: Geetamma Tummalapalli; Sanapala Umamaheswara Rao; Marpu Chaitanya Kumar; Potnuru Narayanarao

Addresses: ECE Department, GMR Institute of Technology, Rajam, Andhra Pradesh, India ' Department of EC, Aditya Institute of Technology and Management, Tekkali, Srikakulam, India ' Department of EC, Aditya Institute of Technology and Management, Tekkali, Srikakulam, India ' Department of EC, Aditya Institute of Technology and Management, Tekkali, Srikakulam, India

Abstract: The electrocardiogram (ECG) is a valuable tool for diagnosing cardiovascular issues. However, manual analysis can be time-consuming and prone to error. This work presents a novel ECG classification system utilizing a convolutional neural network (CNN) to automatically categorize ECG signals into five classes: normal, left/right bundle branch block, atrial premature contraction, and ventricular premature contraction. Our method extracts nonlinear features directly from the signal, outperforming approaches reliant on hand-crafted features. We achieved 99.25% accuracy on the MIT-BIH database, with rapid classification time (0.0738 seconds per beat). Crucially, we integrated this model into an Android application, enabling convenient ECG signal classification and result display for potential clinical use.

Keywords: Android application; convolutional neural network; CNN; electrocardiogram; ECG; MIT-BIH.

DOI: 10.1504/IJCVR.2026.153127

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.3, pp.360 - 384

Received: 24 Jun 2023
Accepted: 06 Nov 2023
Published online: 23 Apr 2026 *

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