A multimodal sleep foundation model for disease prediction | Nature Medicine
A deep learning-based model, developed using the rich, multimodal data available from polysomnography-derived sleep recordings, performs well on common sleep analysis tasks and predicts future disease risk across a range of diseases.
Main Sleep is a complex process characterized by intricate interactions across physiological systems, including brain, heart, respiratory and muscle activity1. PSG—the gold standard for sleep evaluation—captures these interactions through recordings of several modalities, including brain activity signals (BAS, including electroencephalogram (EEG) and electrooculogram (EOG)), electrocardiography (ECG), electromyography (EMG) and respiratory signals2. Sleep disorders affect millions of people and are increasingly recognized as indicators of, and contributors to, various health conditions3.…
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