mlsanityData to 14 Oct 2026

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

arXiv 2509.02982 ↗First posted 3 Sep 2025cs.LG cs.AI cs.NEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 3 Sep 2025.

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References · 14

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  2. 2017On Calibration of Modern Neural Networkscited by 4,960
  3. 2017Squeeze-and-Excitation Networkscited by 4,789
  4. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  5. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 950
  6. 2020Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptationcited by 848
  7. 2020Long-tail learning via logit adjustmentcited by 618
  8. 2022Continual Test-Time Domain Adaptationcited by 525
  9. 2022Efficient Test-Time Model Adaptation without Forgettingcited by 469
  10. 2016Revisiting Batch Normalization For Practical Domain Adaptationcited by 288
  11. 2017DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGcited by 157
  12. 2018SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Stagingcited by 91
2 more
  1. 2019U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Stagingcited by 88
  2. 2020Inter-database validation of a deep learning approach for automatic sleep scoringcited by 38

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