SYSTEMATIC REVIEW article
Front. Digit. Health
Sec. Health Informatics
Advances in Machine and Deep Learning for ECG Beat Classification: A Systematic Review
Provisionally accepted
Allam Jaya Prakash1
Abdelkader Nasreddine Belkacem1
Ibrahim Elfadel2
Herbert F Jelinek2
Mohamed Atef1,3*- 1United arab emirate university, Al Ain, United Arab Emirates
- 2Khalifa University, Abu Dhabi, United Arab Emirates
- 3United Arab Emirates University, Al Ain, United Arab Emirates
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The electrocardiogram (ECG) serves as a significant tool for exploring the structure and function of the heart due to its low cost, ease of use, efficiency, and non-invasive nature. With the rapid development of artificial intelligence (AI) in the medical field, ECG beat classification has emerged as a key area of research for performing accurate, automated, and interpretable cardiac analysis. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, we examined a total of 106 relevant articles published between 2014 and 2024. This study investigates ECG signal analysis to identify and categorise various beats with better accuracy and efficiency, by emphasising and applying vital pre-processing techniques for denoising the raw data. Particular attention is given to the evolution from traditional feature-engineering methods toward advanced architectures with automated feature extraction and classification, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid frameworks with attention mechanisms. In addition, this review paper investigates common challenges observed in the existing studies, including data imbalance, inter-patient variability, and the absence of unified evaluation metrics, which restrict fair comparison and clinical translation. To address these gaps, future research directions are proposed, focusing on the development of standardised multicentre datasets, cross-modal fusion of physiological signals, and interpretable AI models to facilitate real-world deployment in healthcare systems. This systematic review provides a structured overview of the current state and emerging trends in ECG beat classification, offering clear insights for researchers and clinicians to guide future advancements in intelligent cardiac diagnostics.
Keywords: arrhythmia, Classification, deep learning, electrocardiogram, feature extraction, machine learning
Received: 19 Jun 2025; Accepted: 31 Oct 2025.
Copyright: © 2025 Prakash, Belkacem, Elfadel, Jelinek and Atef. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mohamed Atef, moh_atef@uaeu.ac.ae
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