Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS--ANS Dynamic

Researchers introduced Omni-Sleep, a foundation model for sleep analysis that leverages the physiological hierarchy of the central and autonomic nervous systems to learn structured representations from polysomnography data. Pre-trained on over 100,000 hours of multimodal sleep recordings, the model outperforms existing baselines in sleep staging and disease classification tasks, demonstrating improved label efficiency and robustness to missing modalities.

arXiv:2607.07720v1 Announce Type: new 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, 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.