LPSGM: A Unified Flexible Large PSG Model for Sleep Staging and Mental Disorder Diagnosis

Sleep disorders affect billions worldwide, yet clinical polysomnography (PSG) analysis remains hindered by labor-intensive manual scoring and limited generalizability of automated sleep staging tools across heterogeneous protocols. We present LPSGM, a large-scale PSG model designed to address two critical challenges in sleep medicine: cross-center generalization and adaptable diagnosis of neuropsychiatric disorders. Trained on 220,500 hours of multi-center PSG data (24,000 full-night recordings from 16 public datasets), LPSGM integrates domain-adaptive pre-training, flexible channel configurations, and a unified architecture to mitigate variability in equipment, montages, and populations during sleep staging while enabling downstream fine-tuning for brain disorder detection. In prospective validation, LPSGM achieves expert-level consensus in sleep staging ({kappa} = 0.845 {+/-} 0.066 vs. inter-expert {kappa} = 0.850 {+/-} 0.102) and matches the performance of fully supervised models on two independent private cohorts. When fine-tuned for sleep disorder diagnosis, LPSGM achieved 80.47% accuracy on the large-scale MNC dataset (773 subjects) for a three-class classification (Healthy Control vs. T1 Narcolepsy vs. Other Hypersomnia). The model also demonstrated strong cross-institutional generalizability, with an AUC of 0.8791 on independent cohorts for a binary (Normal vs. Abnormal) classification. While depression screening on smaller datasets showed perfect accuracy in controlled settings, larger-scale validation is necessary. By bridging automated sleep staging with real-world clinical deployment, LPSGM establishes a scalable framework for integrated sleep and brain disorder diagnostics. The code and pre-trained model are publicly available at https://github.com/Deng-GuiFeng/LPSGM to advance reproducibility and translational research in sleep medicine.