用生理先验提升单源睡眠分期模型泛化能力
SleepBand: Single-Source Domain Generalization for Sleep Staging via Physiologically Structured Spectral Modeling
- 基于莫雷特滤波器和结构化整合重校准，引入生理节律先验
- 在5个公开数据集上达到当前最优单源域泛化性能
- 适合关注睡眠分期鲁棒性与生理可解释性的研究者
将睡眠分期模型泛化到未见数据集极具挑战性，传统领域泛化方法通常依赖多个源域或领域标签，而这些在实际中很少可用。本文解决更严格且实用的单源域泛化问题：仅使用单一带标注源数据集进行训练，无需领域标签或目标数据访问。提出SleepBand框架，通过可学习的莫雷特（Morlet）滤波器组和结构化集成-重校准流程，嵌入振荡生理先验，使表征锚定于域不变的睡眠节律（如慢波、纺锤波），降低对数据集特定伪影的依赖。在五个公开数据集上，SleepBand取得当前最优单源域泛化表现，并在留一域排除（多源）域泛化设置下仍具竞争力。分析表明，学习到的滤波器与经典神经生理学特征对齐，鲁棒性源于聚焦窄带、生理意义明确的线索。结果表明，基于原理的生理感知归纳偏置是实现稳健单域睡眠分期的可行路径。代码已开源。
原文摘要 · Abstract (English)
Generalizing sleep staging models to unseen datasets is challenging, and typical domain generalization (DG) methods often rely on multiple source domains or domain labels that are rarely available in practice. We tackle the stricter and more practical setting of single-source domain generalization: training on a single labeled source dataset, without domain labels or access to target data. We present SleepBand, a physiology-guided framework that embeds oscillatory priors via a learnable Morlet filter bank and a structured integration-and-recalibration pipeline. This anchors representations to domain-invariant sleep rhythms (e.g., slow waves, spindles), reducing reliance on dataset-specific artefacts. On five public datasets, SleepBand achieves state-of-the-art SDG performance and remains competitive under leave-one-domain-out (multi-source) DG. Analyses show that the learned filters align with canonical neurophysiology and that robustness stems from focusing on narrowband, physiologically meaningful cues. Our results suggest that principled, physiology-aware inductive biases are a promising path for robust single-domain sleep staging. Code is available at https://github.com/lzcn/sleep-band
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