Learning transferable human physiology from two million hours of sleep with SleepFM-2
Sleep provides a nightly window into health by capturing coordinated activity across the brain, heart, muscles and respiratory system. We introduce SleepFM-2, a sleep foundation model developed and evaluated on 282,511 polysomnography recordings from 26 cohorts, including 235,865 used for pretraining. These data span more than two million hours of multimodal physiology. Compared with SleepFM, SleepFM-2 improves disease prediction and sleep scoring, supports arousal, limb movement and respiratory event detection, and transfers to wearable sensing and subjective sleep phenotypes. A model combining its PSG representation with age, sex and BMI met a prespecified discrimination and significanc...
| Authors | Rahul Thapa, Christopher Sun, William Theodor Lehn-Schioler et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 September 2026 |
| Last updated at source | 2 October 2026 |
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