SleepGPT: Learning Transferable Sleep Stage Dynamics from Hypnograms

Sleep stage sequences (hypnograms) encode temporal structure that is useful for both resolving ambiguous stage predictions and characterizing whole-night sleep architecture. However, existing sleep models typically learn temporal dependencies jointly with physiological signal representations, leaving open whether stage dynamics can be learned independently and transferred across models and tasks. We introduce SleepGPT, a compact autoregressive transformer pretrained exclusively on 5.86 million expert-scored stage annotations from 5,793 overnight recordings. SleepGPT learns stage dynamics through next-stage prediction and supports two complementary forms of transfer. For sleep staging, the frozen model provides a sequence prior that is combined with signal-based predictions from independently trained PSG classifiers; for sleep disorder classification, its pretrained encoder represents local hypnogram segments within a hierarchical whole-night model. Across seven external staging comparisons involving three classifiers and three datasets, SleepGPT improves accuracy by 1.6-4.2 percentage points without target-cohort retraining. Improvements persist for classifiers that already model temporal EEG context and for simultaneous PSG and wearable EEG recordings. For hypnogram-based disorder classification, SleepGPT initialization improves balanced accuracy over the same hierarchical architecture trained from scratch, with the gains extending to independent external cohorts. These results show that transferable temporal representations can be learned directly from expert-scored hypnograms, independently of physiological signal modeling, and reused for both epoch-level inference and recording-level prediction.