Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography

Abstract--Objective: Electrocardiograms (ECGs) play a crucial role in diagnosing heart conditions; however, the effectiveness of artificial intelligence (AI)-based ECG analysis is often hindered by the limited availability of labeled data. Self-supervised learning (SSL) can address this by leveraging large-scale unlabeled data. We introduce PhysioCLR (Physiology-aware Contrastive Learning Representation for ECG), a physiology-aware contrastive learning framework that incorporates domain-specific priors to enhance the generalizability and clinical relevance of ECG-based arrhythmia classification. Methods: During pre-training, PhysioCLR learns to bring together embeddings of samples that share similar clinically relevant features while pushing apart those that are dissimilar . Additionally, we introduce ECGspecific augmentations that preserve the ECG category post-augmentation and propose a hybrid loss function to further refine the quality of learned representations. Results: We evaluate PhysioCLR on two public ECG datasets, Chapman and Georgia, for multilabel ECG diagnoses, as well as a private ICU dataset labeled for binary classification. Across the Chapman, Georgia, and private cohorts, PhysioCLR boosts the mean AUROC by 12% relative to the strongest baseline, underscoring its robust cross-dataset generalization. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC); the New Frontiers in Research Fund (NFRF) through the Social Sciences and Humanities Research Council (SSHRC); and the V ector Institute. Sophia Mannina is supported in part by the Social Sciences and Humanities Research Council. Stephanie Sibley is supported in part by the Canadian Institutes of Health Research (CIHR). David Maslove is supported in part by the Southeastern Ontario Academic Medical Association (SEAMO).