arXiv AI September 21, 2026 By Luca Thiebaud (AMU, AMU SCI, DIAPRO, LIS), Paul Chauchat (AMU SCI, AMU, LIS, DIAPRO), Mustapha Ouladsine (AMU SCI, AMU, LIS, DIAPRO), St\'ephane Delliaux (AMU, APHM, C2VN)

Learning Cardiac Features: ECG Biometrics Across Time and~Exercise

Read the original on arXiv AI →

The study investigates ECG biometrics by training a Siamese ResNet with late multi-lead fusion on a large dataset from cardiopulmonary exercise tests. It evaluates the model under realistic conditions, including exercise-induced stress and cross-session variability, achieving an intra-session rest-to-peak EER of 1.7% and a state‑of‑the‑art 3.9% on the CYBHi dataset. The results demonstrate that an intrinsic cardiac signature remains robust to physiological and temporal drift.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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