Home EEG sleep assessment shows reduced slow‐wave sleep in mild–moderate Alzheimer's disease. Issue 1 (16th August 2022)

• Record Type: Journal Article
• Authors: Kent, Brianne A.; Casciola, Amelia A.; Carlucci, Sebastiano K.; Chen, Meghan; Stager, Sam; Mirian, Maryam S.; Slack, Penelope; Valerio, Jason; McKeown, Martin J.; Feldman, Howard H.; Nygaard, Haakon B.
• Is Part Of: Alzheimer's & dementia. Volume 8:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-16
• Subjects: actigraphy -- Alzheimer's disease -- deep learning -- home electroencephalogram -- N3 -- polysomnography -- Pittsburgh Sleep Quality Index -- rapid eye movement -- sleep staging -- slow‐wave sleep; Dementia -- Periodicals; Dementia -- Treatment -- Periodicals; Alzheimer's disease -- Treatment -- Periodicals; Alzheimer's disease -- Periodicals; 616.831
• DOI: 10.1002/trc2.12347
• Languages: English
• ISSNs: 2352-8737

Abstract

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 participants underwent home EEG sleep recordings as well as actigraphy and subjective sleep measures through the Pittsburgh Sleep Quality Index (PSQI). Each participant wore the EEG headband for up to three nights. Sleep was staged using a deep learning model previously developed by our group, and sleep stages were correlated with actigraphy measures as well as PSQI scores. Results: We show that home EEG with a headband is feasible and well tolerated in patients with AD. Patients with mild–moderate AD were found to spend less time in SWS compared to healthy control participants. Other sleep stages were not different between the two groups. Actigraphy or the PSQI were not found to predict home EEG sleep stages. Discussion: Our data show that home EEG is well tolerated, and can ascertain reduced SWS in patients with mild–moderate AD. Similar findings have previously been reported, but using clinical PSG not suitable for the home environment. Home EEG will be particularly useful in future clinical trials assessing potential interventions that may target specific sleep stages to alter the pathogenesis of AD. Highlights: Home electroencephalogram (EEG) sleep assessments are important for measuring sleep in patients with dementia because polysomnography is a limited resource not well tolerated in this patient population. Simplified at‐home EEG for sleep assessment is feasible in patients with mild–moderate Alzheimer's disease (AD). Patients with mild–moderate AD exhibit less time spent in slow‐wave sleep in the home environment, compared to healthy control participants. Compared to healthy control participants, patients with mild–moderate AD spend more time in bed, with decreased sleep efficiency, and more awakenings as measured by actigraphy, but these measures do not correlate with EEG sleep stages.