Learning Cardiac Features: ECG Biometrics Across Time and~Exercise
A Siamese ResNet identifies people from ECG under exercise stress and across sessions at low error rates.
The paper tests ECG biometrics beyond single-session resting recordings, measuring robustness to exercise stress and change across sessions. A Siamese ResNet with late multi-lead fusion, trained on cardiopulmonary exercise-test ECGs, achieves a 1.7% intra-session rest-to-peak equal error rate and 3.9% EER on the public CYBHi dataset. The authors conclude that a subject-specific cardiac signature can persist despite physiological and temporal drift, with possible use for authentication and self-supervised pretraining.
- Siamese ResNet with late multi-lead fusion identifies people from ECG.
- Intra-session rest-to-peak equal error rate is 1.7%.
- Reports 3.9% equal error rate on the public CYBHi dataset.
- Evaluation covers exercise stress and cross-session variability.
From the original article
1 min read at arxiv.orgElectrocardiograms (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.
By Luca Thiebaud · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.