Self-Supervised Learning Frameworks for Real-Time Electrocardiogram Anomaly Detection

Abstract

Early detection of cardiac arrhythmias via wearable ECG sensors requires robust unsupervised representation learning. We present a contrastive self-supervised model pre-trained on over 100,000 unannotated clinical ECG recordings.