Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep.

ABSTRACT:

Study objectives

To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality.

Methods

Power spectra from PSGs of 8716 participants, including from the MrOS Sleep Study and the Sleep Heart Health Study, were analyzed in deep learning-based survival models. The best-performing model was further examined using SHapley Additive Explanation (SHAP) for data-driven sleep-stage specific definitions of power bands, which were evaluated in predicting mortality using Cox Proportional Hazards models.

Results

Survival analyses, adjusted for known covariates, identified multiple EEG frequency bands across all sleep stages predicting all-cause mortality. For EEG, we found

SUBMITTER: Kristjansson TO 

PROVIDER: S-EPMC12554981 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature