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Learning Cardiac Features: ECG Biometrics Across Time and~Exercise

Paper recorded by Signals 4 on 2026-09-18 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.AI · 人工智能 · first seen 2026-09-21

Abstract

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 p

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#9 most recent of 320 cs.AI papers we have recorded · ↑ newer: NemotronLabs VoiceChat: An Open Full-duplex Speech-to-Speech Model wit · ↓ older: When Should a Failing Robot Ask? Initiating Corrective Human-Robot Dia
Cite this page: Learning Cardiac Features: ECG Biometrics Across Time and~Exercise: the #9 most recent of 320 cs.AI papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/learning-cardiac-features-ecg-biometrics-across-time-and-exercise.html
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