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

ABSTRACT:

Study objectives

To investigate whether a foundational transformer model using 8-hour, multichannel data from polysomnograms can outperform existing artificial intelligence (AI) methods for sleep stage classification.

Methods

We utilized the Sleep Heart Health Study (SHHS) visits 1 and 2 for training and validation and the Multi-Ethnic Study of Atherosclerosis (MESA) for testing of our model. We trained a self-supervised foundational transformer (called PFTSleep) that encodes 8-hour long sleep studies at 125 Hz with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels. These encodings are used as input for training of an additional model to classify sleep stages, without adjusting the weights of the foundational transformer. We compared our results

SUBMITTER: Fox B 

PROVIDER: S-EPMC11326349 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature