Self-Supervised Learning for Biomedical Signal Processing: A Systematic Review on ECG and PPG Signals

Self-supervised learning has emerged as a promising paradigm for enhancing the analysis of physiological signals, particularly Electrocardiogram (ECG) and Photoplethysmogram (PPG) data. This review paper surveys the application of self-supervised learning techniques in the domain of ECG and PPG signal analysis. Traditional supervised methods often rely on labeled data, which can be limited and costly to acquire in medical contexts. Self-supervised learning leverages the inherent structure and temporal dependencies within ECG and PPG signals to train models without explicit annotations. By exploiting pretext tasks such as predicting time intervals, missing samples, or temporal order, self-supervised approaches can learn meaningful representations that capture crucial information for subsequent downstream tasks. This paper provides an overview of key self-supervised methods applied to ECG and PPG data, highlighting their advantages and challenges. Furthermore, it discusses the transferability of learned representations to various clinical applications, including arrhythmia detection, anomaly detection, and heart rate variability analysis. Through this comprehensive review, we shed light on the potential of self-supervised learning to revolutionize ECG and PPG signal processing, ultimately contributing to improved healthcare diagnostics and monitoring.