Advances in machine and deep learning for ECG beat classification: a systematic review

The electrocardiogram (ECG) is an important 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 criteria, we examined a total of 106 relevant articles published between 2014 and 2024. This study investigates ECG signal analysis to identify and categorize various beats with better accuracy and efficiency, by emphasizing 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, recurrent neural networks, and hybrid frameworks with attention mechanisms. In addition, this review article investigates the 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 standardized multi-center 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.