SleepFM-2：从两百万小时睡眠中学习可迁移的人体生理特征

中文摘要 AI 辅助

睡眠通过捕捉大脑、心脏、肌肉和呼吸系统的协调活动，为健康提供了一个夜间观察窗口。我们推出了SleepFM-2，这是一个睡眠基础模型，在来自26个队列的282,511例多导睡眠监测记录上进行了开发和评估，其中235,865例用于预训练。这些数据涵盖超过两百万小时的多模态生理信号。与SleepFM相比，SleepFM-2在疾病预测和睡眠分期方面有所改进，支持觉醒、肢体运动和呼吸事件检测，并能迁移到可穿戴传感和主观睡眠表型。将其PSG表示与年龄、性别和BMI相结合的模型，在两个保留队列中，对随后记录的215个电子健康记录表型达到了预设的判别力和显著性标准，其中一个队列是预训练期间未见过的医疗系统。对于155个表型，PSG表示在人口统计学信息之外增加了可复现的信息。SleepFM-2还优于基于相同记录提取的480特征基线。其疾病评分揭示了一个可复现的主成分，该成分与sigma频段空间耦合降低和高密度图熵增加相关。冻结编码器在睡眠事件方面表现处于专家评分者的观察范围内，并能迁移到清醒脑电图、头带和耳内脑电图、腕部光电容积脉搏波和腕部加速度计数据。它在六个加速度计队列中改善了睡眠分期，并在英国生物银行中实现了与直接在加速度计上预训练的模型相似的疾病预测性能。最后，SleepFM-2捕捉了传统PSG摘要未能恢复的主观睡眠方面，特别是对记录当晚的报告。这些结果表明，多模态睡眠生理学可以提供一种可迁移的人体健康表示，适用于疾病、临床任务、传感器和主观体验。

英文摘要

Sleep provides a nightly window into health by capturing coordinated activity across the brain, heart, muscles and respiratory system. We introduce SleepFM-2, a sleep foundation model developed and evaluated on 282,511 polysomnography recordings from 26 cohorts, including 235,865 used for pretraining. These data span more than two million hours of multimodal physiology. Compared with SleepFM, SleepFM-2 improves disease prediction and sleep scoring, supports arousal, limb movement and respiratory event detection, and transfers to wearable sensing and subjective sleep phenotypes. A model combining its PSG representation with age, sex and BMI met a prespecified discrimination and significance criterion for 188 subsequently recorded EHR phenotypes in two held-out cohorts, including one health system unseen during pretraining. SleepFM-2 also outperformed a 480-feature baseline derived from the same recordings. Its disease scores revealed a reproducible principal component associated with reduced sigma-band spatial coupling and increased hypnodensity entropy. The frozen encoder performed within the observed range of expert scorers for sleep events and transferred to wakeful EEG, headband and in-ear EEG, wrist PPG and wrist accelerometry. It improved sleep staging across six accelerometry cohorts and achieved disease-prediction performance in UK Biobank similar to models pretrained directly on accelerometry. Finally, SleepFM-2 captured aspects of subjective sleep not recovered by conventional PSG summaries, particularly reports of the recorded night. These results show that multimodal sleep physiology can provide a transferable representation of human health across diseases, clinical tasks, sensors and subjective experience.

发表机构

• Stanford University（斯坦福大学）
• BrainCapture
• Technical University of Denmark（丹麦技术大学）
• Danish Center for Sleep Medicine（丹麦睡眠医学中心）
• Hvidovre Hospital（希维多夫医院）
• Pioneer Center for SMARTbiomed, National Research Center for the Working Environment（SMARTbiomed先锋中心，国家工作环境研究中心）

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