ArticleSleep2025
A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages.
Article in Sleep, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Sleep EEG foundation models reveal within-stage microstructure that improves health screening beyond traditional stages.Research square · 2026Article
- SleepJEPA: Learning the latent world of sleep with at-home sleep data to estimate disease risk.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence in sleep diagnostics for children under 2 years of age: state of the evidence and future directions.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Toward integrated sleep health: multimodal AI in Hang Hao Meng agent.NPJ digital medicine · 2026Review
- Application of a time series foundation model to noninvasively estimate intracranial pressure.Journal of clinical monitoring and computing · 2025Article
- PhysioJEPA: Joint Embedding Representations of Physiological Signals for Real Time Risk Estimation in the Intensive Care Unit.Proceedings of machine learning research · 2025Article
- Article
- Automated Sleep Stage Classification Using PSO-Optimized LSTM on CAP EEG Sequences.Brain sciences · 2025Article
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8 authors.
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Abstract
STUDY
objectivesTo evaluate whether a foundational transformer using 8-hour, multichannel polysomnogram (PSG) data can effectively encode signals and classify sleep stages with state-of-the-art performance.
methodsThe Sleep Heart Health Study, Wisconsin Sleep Cohort, and Osteoporotic Fractures in Men (MrOS) Study visit 1 were used for training, and the Multi-Ethnic Study of Atherosclerosis (MESA), Apnea Positive Pressure Long-term Efficacy Study (APPLES), and MrOS visit 2 served as independent test sets. We developed PFTSleep, a self-supervised foundational transformer that encodes full-night sleep studies with brain, movement, cardiac, oxygen, and respiratory channels. These representations were used to train another model to classify sleep stages. We compared our results to existing methods, examined differences in performance by varying channel input data and training dataset size, and investigated an AI explainability tool to analyze decision processes.
resultsPFTSleep was trained with 13 888 sleep studies and tested on 4169 independent studies. Cohen's Kappa scores were 0.81 for our held-out set, 0.59 for APPLES, 0.60 for MESA, and 0.75 for MrOS Visit 2. Performance increases to 0.76 on a held-out MESA set when MESA is included in the training of the classifier head but not the transformer. Compared to other state-of-the-art AI models, our model shows high performance across diverse datasets while only using task-agnostic PSG representations from a foundational transformer as input for sleep stage classification.
conclusionsFull night, multichannel PSG representations from a foundational transformer enable accurate sleep stage classification comparable to state-of-the-art AI methods across diverse datasets.
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