发表机构
Department of Biomedical Engineering, Southern University of Science and Technology; Omni-Intelligence; Shenzhen Loop Area Institute（南方科技大学生物医学工程系; 全知智能; 深圳河套学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
介绍全睡眠模型，利用CNS/ANS划分，通过分层对比学习的三个目标进行拓扑约束表示学习，在多中心多模态PSG数据预训练后用于睡眠分期和多疾病分类，性能优于基线，凸显生理层次对睡眠表示学习的价值。
AI 中文摘要
睡眠生理源于中枢神经系统（CNS）和自主神经系统（ANS）的协调动态，多模态多导睡眠图信号可反映。现有睡眠基础模型常以拓扑无关方式融合异质生物信号，忽视生理组织。我们引入全睡眠模型，利用CNS/ANS划分作为拓扑约束表示学习的生理先验。通过三个目标学习结构化表示：系统内一致性、系统间同步和潜在空间掩码时间建模。在超10万小时多中心多模态PSG数据上预训练，在睡眠分期和多疾病分类中评估。结果表明其优于强基础模型基线，突出了生理层次对可泛化睡眠表示学习的价值。
英文摘要
Sleep physiology arises from the coordinated dynamics of the central nervous system (CNS) and autonomic nervous system (ANS), as reflected by multimodal polysomnography signals including EEG, EOG, EMG, ECG, and respiration. However, existing sleep foundation models often fuse heterogeneous biosignals in a topology-agnostic manner, overlooking their physiological organization. We introduce Omni-Sleep, a sleep foundation model that uses the CNS/ANS partition as a physiological prior for topology-constrained representation learning. Omni-Sleep learns structured representations through three objectives: intra-system consistency, which captures shared subsystem-level factors within neural and cardio-respiratory signals; inter-system synchronization, which aligns subsystem trajectories to model brain--body dynamics; and latent-space masked temporal modeling, which captures long-horizon sleep dynamics. Pre-trained on over 100,000 hours of multi-center multimodal PSG data, Omni-Sleep is evaluated on sleep staging and multi-disease classification. Across datasets and modality-ablation settings, Omni-Sleep outperforms strong foundation-model baselines, showing improved label efficiency, cross-dataset generalization, and robustness to missing modalities. These results highlight the value of physiological hierarchy for generalizable sleep representation learning. Code is available at https://github.com/AutoBrain-sleep/OmniSleep.
