  The human electroencephalogram (EEG) of sleep undergoes profound changes with
age. These changes can be conceptualized as "brain age", which can be compared
to an age norm to reflect the deviation from normal aging process. Here, we
develop an interpretable machine learning model to predict brain age based on
two large sleep EEG datasets: the Massachusetts General Hospital sleep lab
dataset (MGH, N = 2,621) covering age 18 to 80; and the Sleep Hearth Health
Study (SHHS, N = 3,520) covering age 40 to 80. The model obtains a mean
absolute deviation of 8.1 years between brain age and chronological age in the
healthy participants in the MGH dataset. As validation, we analyze a subset of
SHHS containing longitudinal EEGs 5 years apart, which shows a 5.5 years
difference in brain age. Participants with neurological and psychiatric
diseases, as well as diabetes and hypertension medications show an older brain
age compared to chronological age. The findings raise the prospect of using
sleep EEG as a biomarker for healthy brain aging.
