Home EEG sleep assessment shows reduced slow-wave sleep in mild-moderate Alzheimer's disease.
Home EEG sleep assessment shows reduced slow-wave sleep in mild-moderate Alzheimer's disease.
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DOI：
10.1002/trc2.12347
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发表时间：
2022
影响因子：
4.8
通讯作者：
Nygaard, Haakon B
中科院分区：
文献类型：
--
作者：
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
睡眠障碍在阿尔茨海默病（AD）中很常见，估计患病率高达65%。最近的研究表明，特定的睡眠阶段，如慢波睡眠（SWS）和快速眼动（REM），可能直接影响AD的病理生理学。睡眠分期的一个主要限制是需要临床多导睡眠图（PSG），这在痴呆患者中通常不能很好地耐受。我们最近开发了一种深度学习模型，可以可靠地分析从简单的双导联EEG头带中获得的低质量EEG数据。在这里，我们评估了这种方法是否允许轻中度AD患者的家庭EEG睡眠分期。共有26名轻中度AD患者和24名年龄匹配的健康对照参与者接受了家庭EEG睡眠记录以及通过匹兹堡睡眠质量指数（PSQI）进行的活动记录和主观睡眠测量。每个参与者佩戴EEG头带长达三个晚上。使用我们小组以前开发的深度学习模型对睡眠进行分期，睡眠阶段与体动仪测量以及PSQI评分相关。我们表明，家庭脑电图与头带是可行的，并在AD患者的耐受性良好。与健康对照参与者相比，轻中度AD患者在SWS中花费的时间较少。其他睡眠阶段在两组之间没有差异。活动记录仪或PSQI没有发现预测家庭EEG睡眠阶段。我们的数据表明，家庭脑电图耐受性良好，可以确定轻中度AD患者的SWS减少。类似的发现以前也有报道，但使用临床PSG不适合家庭环境。家庭脑电图将是特别有用的，在未来的临床试验评估潜在的干预措施，可能针对特定的睡眠阶段，以改变AD的发病机制。家庭脑电图（EEG）睡眠评估对于测量痴呆患者的睡眠非常重要，因为多导睡眠图是一种有限的资源，在该患者人群中耐受性不佳。在轻中度阿尔茨海默病（AD）患者中，简化的家庭EEG用于睡眠评估是可行的。与健康对照参与者相比，轻度-中度AD患者在家庭环境中的慢波睡眠时间较少。与健康对照参与者相比，轻度-中度AD患者卧床时间更长，睡眠效率降低，通过活动记录仪测量的觉醒次数更多，但这些措施与EEG睡眠阶段无关。
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. 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. 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. 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. 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.