SleepXA: Composing Pretrained Neural and Cardiac Foundation Models for Sleep Representation Learning

Physiological signals recorded during sleep provide synchronized views of neural, cardiac, respiratory, and other bodily systems, while preserving modality-specific information. Existing EEG and ECG foundation models already capture rich but distinct physiological structure, motivating an alternative to training a multimodal sleep model from scratch: composing existing pretrained models. We introduce SleepXA, a framework that uses synchronized polysomnography (PSG) to compose independently pretrained neural and cardiac foundation models. SleepXA combines pretrained EEG and ECG encoders with lightweight encoders for additional PSG modalities, and learns shared and modality-specific representations through sleep-guided alignment. We pretrain SleepXA on 9,230 overnight PSG recordings from three large cohorts and evaluate it on 27 downstream tasks spanning sleep representation, clinical and disease prediction, and daytime physiological transfer. SleepXA ranks among the top two methods on 22 of 27 tasks. Controlled experiments further show consistent benefits across sensor configurations and EEG–ECG backbone choices, while pretrained initialization outperforms architecture-matched training from scratch and SleepXA surpasses alternative multimodal composition strategies. These results suggest that synchronized multimodal physiology can serve as a natural interface for composing existing physiological foundation models, yielding reusable representations that extend beyond sleep to clinical and daytime physiological settings.