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SleepFM is a novel foundation model developed to analyze polysomnography (PSG) recordings, facilitating accurate predictions of various health conditions based on sleep data. Trained on over 585,000 hours of sleep recordings, it demonstrates strong performance in predicting diseases such as dementia and heart failure, while also supporting standard sleep analysis tasks.
- SleepFM was trained on over 585,000 hours of polysomnography recordings, an unusually massive dataset for this domain.
- The model predicts diverse downstream conditions like dementia and heart failure directly from raw sleep data, not just standard sleep-stage/apnea metrics.
- It performs well on conventional sleep analysis tasks while also generalizing to disease prediction, suggesting a single foundation model can replace many task-specific ones.