Department of Clinical Psychology and Psychophysiology, Faculty of Medicine, Medical Centre - University of Freiburg, University of Freiburg, Freiburg, Germany.
Movisens GmbH, Karlsruhe, Germany.
J Sleep Res. 2019 Apr;28(2):e12694. doi: 10.1111/jsr.12694. Epub 2018 May 2.
As the prevalence of sleep disorders is increasing, new methods for ambulatory sleep measurement are required. This paper presents electrodermal activity in different sleep stages and a sleep detection algorithm based on electrodermal activity. We analysed electrodermal activity and polysomnographic data of 43 healthy subjects and 48 patients with sleep disorders. Electrodermal activity was measured using an ambulatory device worn at the wrist. Two parameters to describe electrodermal activity were defined based on previous literature: EDASEF (electrodermal activity-smoothed feature) as parameter for skin conductance level; and EDAcounts (number of electrodermal activity-peaks) as skin conductance responses. Analysis of variance indicated significant EDASEF differences between the sleep stages wake versus N1, wake versus N2, wake versus slow-wave sleep, and wake versus rapid eye movement. The analysis of EDAcounts also showed significant differences, especially in the stages slow-wave sleep versus rapid eye movement. Between healthy subjects and patients, a significant disparity of EDAcounts was revealed in stage N1. Furthermore, the variances of EDASEF and EDAcounts in N1, N2 slow-wave sleep and rapid eye movement were higher in the patient group (p [F test] < .05). Next, an electrodermal activity-based sleep/wake discriminating algorithm was constructed. The optimized algorithm achieved an average sensitivity and specificity for sleep detection of 97% and 75%. The epoch agreement rate (average accuracy) was 86%. These outcomes are comparative to sleep detection algorithms based on actigraphy or heart rate variability. The results of this study indicate that electrodermal activity is not only a robust parameter for describing sleep, but also a potential suitable method for ambulatory sleep monitoring.
随着睡眠障碍的患病率不断增加，需要新的方法来进行非卧床睡眠测量。本文介绍了不同睡眠阶段的皮肤电活动以及基于皮肤电活动的睡眠检测算法。我们分析了 43 名健康受试者和 48 名睡眠障碍患者的皮肤电活动和多导睡眠图数据。皮肤电活动使用佩戴在手腕上的非卧床设备进行测量。根据先前的文献，定义了两个描述皮肤电活动的参数：EDASEF（皮肤电活动平滑特征）作为皮肤电导率水平的参数；和 EDAcounts（皮肤电活动峰值数）作为皮肤电反应。方差分析表明，在清醒与 N1、清醒与 N2、清醒与慢波睡眠、清醒与快速眼动之间，EDASEF 存在显著差异。对 EDAcounts 的分析也显示出显著差异，尤其是在慢波睡眠与快速眼动之间。在健康受试者和患者之间，在 N1 阶段，EDAcounts 存在显著差异。此外，在 N1、N2 慢波睡眠和快速眼动中，EDASEF 和 EDAcounts 的方差在患者组中更高（p [F 检验]<.05）。接下来，构建了基于皮肤电活动的睡眠/觉醒判别算法。优化后的算法在睡眠检测中的平均灵敏度和特异性分别为 97%和 75%。睡眠阶段的epoch 一致性率（平均准确率）为 86%。这些结果与基于活动记录仪或心率变异性的睡眠检测算法相当。本研究的结果表明，皮肤电活动不仅是描述睡眠的一种稳健参数，也是一种潜在的适合非卧床睡眠监测的方法。
