按性别年龄分组微调模型，提升睡眠分期准确率。
Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography
- 先全局预训练，再按性别/年龄/鼾症严重度分组微调。
- 37种配置中35组优于基线，κ值最高提升12.9%。
- 适合需要个性化睡眠评估的临床场景。
自动化睡眠分期通常采用单一无人群差异的模型，忽略了睡眠结构在性别、年龄及阻塞性睡眠呼吸暂停（OSA）严重程度上的显著差异。本文提出基于人口统计分层与迁移学习的两阶段训练策略：首先在全人群上预训练卷积循环模型，然后根据美国睡眠医学学会（AASM）标准，针对性别、年龄和呼吸暂停低通气指数（AHI）严重度定义的人群子组分别进行微调。基于包含100名受试者和7个生理信号通道的DREAMT数据集，评估了37种单轴及双轴组合的微调配置。结果表明，37个微调模型中有35个优于基线，Cohen's kappa值提升达0.9%至12.9%。研究证明，针对特定患者群体定制的分层微调可显著提高睡眠分期准确性，为个性化睡眠评估提供了实用且临床可行的新范式。
原文摘要 · Abstract (English)
Automated sleep stage classification typically employs a single population-agnostic model, disregarding established demographic variations in sleep architecture. Sleep patterns, however, differ substantially across gender, age, and obstructive sleep apnea (OSA) severity, indicating that a onesize-fits all approach may be suboptimal for diverse clinical populations. In this paper, we propose a two stage training strategy based on demographic stratification and transfer learning framework. We first pretrains a convolutional recurrent model on the full population and then fine tunes it independently for demographic subgroups defined by gender, age, and Apnea-Hypopnea Index (AHI) severity according to the AASM clinical standard. Using the DREAMT dataset comprising 100 clinical subjects and 7 PSG channels, we evaluate 37 fine-tuned configurations across single-axis and two-way demographic combinations. Results demonstrate that 35 of the 37 fine-tuned models outperform the baseline, with Cohen's kappa improvements ranging from 0.9 to 12.9%. These findings indicate that stratified fine tuning tailored to specific patient demographics yields substantially more accurate sleep staging than a single generalized model, offering a practical and clinically grounded paradigm for personalized sleep assessment.
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