Robust Remote ECG Monitoring under Energy Constraints: Anomaly Filtering versus Predictive Uncertainty
Paper recorded by Signals 4 on 2026-11-01 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-11-01 on arXiv · recorded by Signals 4 on 2026-11-03
Category: cs.LG · 机器学习 · first seen 2026-11-03
Abstract
Machine learning for remote electrocardiogram monitoring must handle noise and diseases absent from training under tight resource constraints. We investigate whether upstream unsupervised anomaly detection can improve monitoring decisions more efficiently than classifier-derived uncertainty. We evaluate five detectors on PTB-XL and BUT QDB and compare a two-stage filter-classifier system with Mont
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Cite this page: Robust Remote ECG Monitoring under Energy Constraints: Anomaly Filtering versus Predictive Uncertainty: the #37 most recent of 703 cs.LG papers we have recorded (as of 2026-11-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/robust-remote-ecg-monitoring-under-energy-constraints-anomaly-filtering-versus-p.html
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