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StableSleep: Source-Free Test-Time Adaptation for Sleep Staging with Lightweight Safety Rails

Machine Learning 2025-09-04 v1 Artificial Intelligence Neural and Evolutionary Computing Neurons and Cognition

Abstract

Sleep staging models often degrade when deployed on patients with unseen physiology or recording conditions. We propose a streaming, source-free test-time adaptation (TTA) recipe that combines entropy minimization (Tent) with Batch-Norm statistic refresh and two safety rails: an entropy gate to pause adaptation on uncertain windows and an EMA-based reset to reel back drift. On Sleep-EDF Expanded, using single-lead EEG (Fpz-Cz, 100 Hz, 30s epochs; R&K to AASM mapping), we show consistent gains over a frozen baseline at seconds-level latency and minimal memory, reporting per-stage metrics and Cohen's k. The method is model-agnostic, requires no source data or patient calibration, and is practical for on-device or bedside use.

Keywords

Cite

@article{arxiv.2509.02982,
  title  = {StableSleep: Source-Free Test-Time Adaptation for Sleep Staging with Lightweight Safety Rails},
  author = {Hritik Arasu and Faisal R Jahangiri},
  journal= {arXiv preprint arXiv:2509.02982},
  year   = {2025}
}

Comments

5 page paper, 8 figures