A Unified Flexible Large Polysomnography Model for Sleep Staging and Mental Disorder Diagnosis

Sleep quality is vital to human health, yet automated sleep staging faces challenges in cross-center generalization due to data scarcity and domain gaps. Traditional scoring is labor-intensive, while deep learning models often fail to generalize across datasets. Here, we present LPSGM, a unified and flexible large polysomnography (PSG) model designed to enhance cross-center generalization in sleep staging and enable fine-tuning for disease diagnosis. Trained on 220,500 hours of PSG data from 16 public datasets, LPSGM integrates domain-adaptive learning and supports variable-channel configurations, achieving performance comparable to models trained directly on target-center data. In a prospective clinical study, LPSGM matches expert-level accuracy with lower variability. When fine-tuned, it attains 88.01% accuracy in narcolepsy detection and 100% in depression detection. These results establish LPSGM as a scalable, plug-and-play solution for automated PSG analysis, bridging the gap between sleep staging and clinical deployment.