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

• URL: http://arxiv.org/abs/2607.07720v2
• Date: Fri, 10 Jul 2026 08:50:36 GMT
• Title: Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-ANS Dynamics
• Abstract summary: We introduce Omni-Sleep, a sleep foundation model that uses the CNS/ANS partition as a physiological prior for representation learning.<n>Pre-trained on over 100,000 hours of multi-center multimodal PSG data, Omni-Sleep is evaluated on sleep staging and multi-disease classification.
• Score: 13.009463605294604
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• NanoSleep: A Parameter-Efficient Hybrid Temporal Convolutional Network for Single-Channel Sleep Stage Classification [0.0]
  We present NanoSleep, a compact hybrid temporal convolutional network for automatic sleep stage classification.<n>NanoSleep provides an effective balance between accuracy and efficiency, making it well suited for wearable devices, home-based sleep monitoring, and resource-constrained clinical applications.
  arXiv  Detail & Related papers  (2026-08-19T06:08:24Z)
• Topological Neural Dynamics: A Neuron-wise Framework for Sequence Modeling [55.07411490538404]
  Topological Neural Dynamics (TND) is a sequence modeling framework that shifts from layer-wise to neuron-wise dynamics.<n>TND represents a neural system as a directed neuron graph, an interaction operator, and a local dynamics function.<n>We evaluate TND as a case study on a behavior cloning task in single-player Pong.
  arXiv  Detail & Related papers  (2026-06-19T10:18:24Z)
• Next-Token Prediction Learns Generalisable Representations of Sleep Physiology [3.80147466733132]
  We develop Hypnos, a sleep foundation model trained using eight different sensing modalities.<n>We tokenize each modality into streams of discrete tokens using residual vector quantization.<n>We train a large auto-regressive RQ-Transformer to jointly predict the next token across all modalities in parallel.
  arXiv  Detail & Related papers  (2026-06-08T15:13:57Z)
• sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals [15.989293188183069]
  We present textttsleep2vec, a foundation model for diverse and incomplete nocturnal biosignals.<n>textttsleep2vec is contrastively pre-trained on 42,249 overnight recordings spanning nine modalities.<n>On downstream sleep staging and clinical outcome assessment, ttexttsleep2vec consistently outperforms strong baselines.
  arXiv  Detail & Related papers  (2026-02-14T19:40:04Z)
• Moving Beyond Diffusion: Hierarchy-to-Hierarchy Autoregression for fMRI-to-Image Reconstruction [65.67001243986981]
  We propose MindHier, a coarse-to-fine fMRI-to-image reconstruction framework built on scale-wise autoregressive modeling.<n>MindHier achieves superior semantic fidelity, 4.67x faster inference, and more deterministic results than the diffusion-based baselines.
  arXiv  Detail & Related papers  (2025-10-25T15:40:07Z)
• CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding [57.90382885533593]
  We propose a Cross-scale Spatiotemporal Brain foundation model for generalized decoding EEG signals.<n>We show that CSBrain consistently outperforms task-specific and foundation model baselines.<n>These results establish cross-scale modeling as a key inductive bias and position CSBrain as a robust backbone for future brain-AI research.
  arXiv  Detail & Related papers  (2025-06-29T03:29:34Z)
• CodeBrain: Towards Decoupled Interpretability and Multi-Scale Architecture for EEG Foundation Model [52.466542039411515]
  EEG foundation models (EFMs) have emerged to address the scalability issues of task-specific models.<n>We present CodeBrain, a two-stage EFM designed to fill this gap.<n>In the first stage, we introduce the TFDual-Tokenizer, which decouples heterogeneous temporal and frequency EEG signals into discrete tokens.<n>In the second stage, we propose the multi-scale EEGSSM architecture, which combines structured global convolution with sliding window attention.
  arXiv  Detail & Related papers  (2025-06-10T17:20:39Z)
• Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework [3.135883872525168]
  This study introduces SynthSleepNet, a multimodal hybrid self-supervised learning framework for analyzing polysomnography (PSG) data.<n> SynthSleepNet effectively integrates masked prediction and contrastive learning to leverage complementary features across multiple modalities.<n>It achieved superior performance compared to state-of-the-art methods across three downstream tasks.
  arXiv  Detail & Related papers  (2025-02-18T10:11:50Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
  We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
  arXiv  Detail & Related papers  (2024-02-26T15:01:54Z)
• Wake-Sleep Consolidated Learning [9.596781985154927]
  We propose Wake-Sleep Consolidated Learning to improve the performance of deep neural networks for visual classification tasks. Our method learns continually via the synchronization between distinct wake and sleep phases. We evaluate the effectiveness of our approach on three benchmark datasets.
  arXiv  Detail & Related papers  (2023-12-06T18:15:08Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification [12.488891852531422]
  We propose a multi-view spatial-temporal graph convolutional networks (MSTGCN) with domain generalization for sleep stage classification. MSTGCN consists of graph convolutions for extracting spatial features and temporal convolutions for capturing the transition rules among sleep stages. Experiments on two public datasets demonstrate that the proposed model outperforms the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-09-04T09:19:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.