Learning Cardiac Features: ECG Biometrics Across Time and~Exercise
Electrocardiograms (ECGs) carry subject-specific patterns enabling reliable individual discrimination, forming the basis of ECG biometrics. Beyond authentication, this paradigm holds significant potential to secure sensitive cardiac data and to serve as a pretext task in self-supervised learning. Yet, most studies remain confined to singlesession, resting data, leaving robustness to temporal and physiological variations largely untested. We address this gap by evaluating ECG biometrics under realistic conditions involving exercise-induced stress and cross-session variability. A Siamese ResNet with late multi-lead fusion strategy is trained on a large ECG dataset extracted from cardiopulmo...
| Authors | Luca Thiebaud, Paul Chauchat, Mustapha Ouladsine et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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