omni-sleep-foundation

name	omni-sleep-foundation
description	Omni-Sleep sleep foundation model methodology using CNS/ANS hierarchical contrastive learning for topology-constrained multimodal PSG representation learning. Use when working with sleep staging, affective BCI, polysomnography analysis, CNS-ANS dynamics, multimodal biosignal foundation models, or physiological hierarchy in representation learning.
metadata	{"arxiv_id":"2607.07720","published":"2026-07-04","authors":"Zhoujie Hou, Song Wang, Kexin Lou, Mo Wang, Chen Wei, Quanying Liu","tags":["sleep-foundation-model","CNS-ANS","polysomnography","contrastive-learning","EEG","multimodal"]}

Omni-Sleep: Sleep Foundation Model via Hierarchical Contrastive Learning

Overview

Omni-Sleep is a sleep foundation model that uses the CNS/ANS (Central/Autonomic Nervous System) partition as a physiological prior for topology-constrained representation learning. Pre-trained on 100,000+ hours of multi-center multimodal PSG data, it addresses the gap where existing sleep models fuse heterogeneous biosignals in a topology-agnostic manner.

Core Architecture

Three Learning Objectives

1. Intra-System Consistency: Captures shared subsystem-level factors within neural (EEG, EOG, EMG) and cardio-respiratory (ECG, respiration) signals separately
2. Inter-System Synchronization: Aligns CNS and ANS subsystem trajectories to model brain-body dynamics cross-correlations
3. Latent-Space Masked Temporal Modeling: Captures long-horizon sleep dynamics through masked token prediction in the latent space

Physiological Prior Structure

CNS Branch: EEG + EOG + EMG → Neural subsystem encoder
ANS Branch: ECG + Respiration → Cardio-respiratory encoder
                ↓
    Cross-system synchronization module
                ↓
    Unified sleep representation → Sleep staging / Disease classification

Key Innovations

• Topology-constrained fusion: Uses known physiological organization (CNS vs ANS) rather than treating all modalities equally
• 100K+ hours pre-training: Multi-center multimodal PSG dataset, significantly larger than prior work
• Modality robustness: Graceful degradation when modalities are missing during inference
• Cross-dataset generalization: Strong transfer across different sleep labs and recording protocols

Application Domains

• Sleep stage classification (AASM standard)
• Multi-disease sleep disorder classification (apnea, insomnia, narcolepsy, etc.)
• Brain-computer interface for sleep-state monitoring
• Multimodal biosignal representation learning
• Physiological hierarchy in AI models

Activation Keywords

omni-sleep, sleep foundation model, CNS-ANS dynamics, polysomnography, PSG, sleep staging, brain-body dynamics, physiological hierarchy, topology-constrained learning, multimodal biosignal, affective computing, sleep physiology

Pitfalls

• Modality availability: Requires at least one CNS and one ANS modality for full inter-system synchronization objective
• Data requirements: 100K+ hours pre-training is a significant compute investment; fine-tuning on smaller datasets is more practical
• Code availability: Repository at https://github.com/AutoBrain-sleep/OmniSleep — check for latest release before implementation
• Generalization limits: While showing cross-dataset generalization, the model may still require domain adaptation for very different recording setups
• Not a replacement for clinical judgment: Foundation model outputs should be validated against clinical ground truth