Abstract
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: captures shared subsystem-level factors within neural and cardio-respiratory signals;
- Inter-system synchronization: aligns subsystem trajectories to model brain–body dynamics;
- Latent-space masked temporal modeling: 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 GitHub.
Blogger's Review: The Omni-Sleep model significantly enhances sleep data representation learning by introducing a physiological hierarchy. Its successful application in multimodal data processing offers new insights and methods for future sleep research and clinical applications. Particularly, its performance in multi-disease classification demonstrates the model's broad applicability and potential.