Predicting future risk of developing cognitive impairment using ambulatory sleep EEG: Integrating univariate analysis and multivariate information theory approach.

BackgroundEarly identification of individuals at risk for cognitive impairment is crucial, as the preclinical phase offers an opportunity for interventions to slow disease progression and improve outcomes.ObjectiveWhile sleep electroencephalography (EEG) has shown significant promise in detecting cognitive impairment, this study aims to 1) develop and validate overnight EEG biomarkers for the prediction of future cognitive impairment risk, 2) assess their predictive performance within 5 years, and 3) explore the feasibility of using wearable, low-density EEG devices for convenient at-home monitoring.MethodsOvernight polysomnography was performed on 281 cognitively normal women in the Study of Osteoporotic Fractures (SOF). Cognitive reassessments were conducted approximately five years later. Features such as relative EEG power across different frequency bands and channel interactions, quantified using generalized mutual information measures, were extracted and used as inputs for machine learning models. Binary classification models distinguished participants who developed cognitive impairment from those who remained cognitively normal. Optimal feature subsets and frequency bands for classiffiation were identifed, with additional analyses testing the contribution of demographic data, sleep macrostructure, and