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ABSTRACT: Introduction
Sleep disturbances are common in Alzheimer's disease (AD), with estimates of prevalence as high as 65%. Recent work suggests that specific sleep stages, such as slow-wave sleep (SWS) and rapid eye movement (REM), may directly impact AD pathophysiology. A major limitation to sleep staging is the requirement for clinical polysomnography (PSG), which is often not well tolerated in patients with dementia. We have recently developed a deep learning model to reliably analyze lower quality electroencephalogram (EEG) data obtained from a simple, two-lead EEG headband. Here we assessed whether this methodology would allow for home EEG sleep staging in patients with mild-moderate AD.Methods
A total of 26 mild-moderate AD patients and 24 age-matched, healthy control parti
SUBMITTER: Kent BA
PROVIDER: S-EPMC9381912 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature