Arrhythmia Classification Method Based on Transformer: Progress, Challenges and Prospects

Abstract

The rapid advancement of deep learning has revolutionized electrocardiogram (ECG) analysis, with Transformer architectures emerging as powerful tools for automated arrhythmia classification. This paper presents a comprehensive review of Transformer-based arrhythmia classification methods, examining their evolution, current capabilities, and future potential. We systematically analyze the architectural adaptations of Transformers for ECG signal processing, including Vision Transformers adapted for 1D medical signals, hybrid CNN-Transformer models, and lightweight implementations for edge computing. Our review encompasses recent studies demonstrating exceptional performance, with models like ECGformer achieving 98\% accuracy on MIT-BIH datasets and tiny Transformer variants reaching 98.97\% accuracy with only 6k parameters suitable for wearable devices. We discuss key advantages including the ability to capture long-range dependencies in ECG sequences, handle variable-length inputs, and integrate multi-lead spatial information through attention mechanisms. However, significant challenges remain, including high computational requirements, dependence on large labeled datasets, limited interpretability in clinical settings, and over-fitting risks with imbalanced data. The paper explores emerging solutions such as transfer learning, data augmentation techniques, and explainable AI methods to address these limitations. Future prospects include the development of more efficient architectures for real-time monitoring, integration with multi-modal physiological data, and enhanced clinical interpretability. This comprehensive analysis provides valuable insights for researchers and clinicians working toward more accurate, efficient, and clinically viable automated arrhythmia detection systems.