无需源数据和校准，实时适配睡眠分期模型，提升部署稳定性。
StableSleep: Source-Free Test-Time Adaptation for Sleep Staging with Lightweight Safety Rails
- 采用熵最小化与批归一化刷新的流式无源测试时自适应方法
- 在单导联脑电上实现秒级延迟、低内存消耗下的稳定性能提升
- 内置熵门与滑动平均重置机制，防止模型漂移，适合设备端使用
睡眠分期模型在面对未见过的生理特征或记录条件时性能常会下降。本文提出一种流式、无源测试时自适应（TTA）方案，结合熵最小化（Tent）与批归一化统计量刷新，并引入两个安全机制：基于熵的门控用于在不确定片段暂停适应，以及基于指数移动平均（EMA）的重置以回滚模型漂移。在 Sleep-EDF Expanded 数据集上，使用单导联脑电（Fpz-Cz，100 Hz，30秒窗口；R&K 到 AASM 映射），该方法在秒级延迟与极低内存开销下持续优于冻结基线模型，报告了各阶段指标与 Cohen's k 值。该方法不依赖源数据或患者校准，具有模型无关性，适用于设备端或床边部署。
原文摘要 · Abstract (English)
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.
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