Home EEG sleep assessment shows reduced slow‐wave sleep in mild–moderate Alzheimer's disease

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.

The potential and prospects of sleep biomarkers in early Alzheimer's disease diagnosis

“…Longterm biomarker monitoring can capture sleep change trends more accurately and distinguish normal aging from pathological changes [25]. Utilizing machine learning and artificial intelligence (AI) techniques to analyze a large amount of sleep data can uncover potential patterns and features, thereby enhancing diagnostic accuracy [26]. The method of utilizing interpretable artificial intelligence (AI) in conjunction with high-density electroencephalography (HD-EEG) achieved an accuracy rate of 98.97% in testing patients with cognitive impairment.…”

A protocol for evaluating digital technology for monitoring sleep and circadian rhythms in older people and people living with dementia in the community

“…These macro changes include changes in sleep timing, shortened or extended nocturnal sleep duration/time in bed, increased number of nocturnal awakenings and time spent awake during the night, decreased slow wave and REM sleep, and increased frequency of daytime naps (reviewed in [38]). At the microarchitecture level, with age and in dementia there are reductions in slow wave activity (SWA) in NREM sleep, particularly in the prefrontal cortex and during the first NREM cycle of the sleep episode, as well as a decreasing number of sleep spindles and reductions in REM sleep (reviewed in [39][40][41]). This alteration in both the sleep signal and sleep continuity with age and in dementia implies dellaMonica C_et_al_Manuscript.docx 11 that sleep monitoring devices that perform well in young people may not do so well in older people or in people living with dementia.…”