A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages.

ABSTRACT:

Study objectives

To 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.

Methods

The 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

SUBMITTER: Fox B 

PROVIDER: S-EPMC12663716 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature