Kocanaogullari Deniz, Akcakaya Murat, Bendixen Roxanna, Soehner Adriane M, Hartman Amy G
Swanson School of Engineering at the University of Pittsburgh, Pittsburgh, PA, United States.
Department of Occupational Therapy, Pittsburgh, PA, United States.
Front Netw Physiol. 2025 May 30;5:1519407. doi: 10.3389/fnetp.2025.1519407. eCollection 2025.
Current approaches to objective measurement of sleep disturbances in children overlook the period prior to sleep, or the settling down time. Using machine learning techniques, we identified key features that characterize differences in activity during the settling down period that differentiate children with sensory sensitivities to tactile input (SS) and children without sensitivities (NSS).
Actigraphy data were collected from children with SS (n = 17) and children with NSS (n = 18) over 2 weeks (a total of 430 evenings). The settling down period, indicated using caregiver report and actigraphy indices, was isolated each evening and seven features (mean magnitude, maximum magnitude, kurtosis, skewness, Shannon entropy, standard deviation, and interquartile range) were extracted. 10-fold cross-validation with random forests were used to determine accuracy, sensitivity, and specificity of differentiating groups.
We could accurately differentiate groups (accuracy = 83%, specificity = 83%, sensitivity = 84%). Feature importance maps identify that children with SS have higher maximum bouts of activity (U = -2.23, p = 0.026) during the settling down time and a higher variance in activity for the children with SS (e.g., interquartile range, Shannon entropy) that sets them apart from their peers.
We present a novel use of machine learning techniques that successfully uncovered differentiating features within the settling down period for our groups. These differences have been difficult to capture using standard sleep and rest-activity metrics. Our data suggests that activity during the settling down period may be a unique target for future research for children with SS.
目前用于客观测量儿童睡眠障碍的方法忽略了睡前时段，即平静期。我们运用机器学习技术，识别出了一些关键特征，这些特征可表征平静期活动的差异，从而区分对触觉输入有感觉敏感的儿童（SS）和无感觉敏感的儿童（NSS）。
收集了17名SS儿童和18名NSS儿童连续2周（共430个晚上）的活动记录仪数据。每晚通过照顾者报告和活动记录仪指标确定平静期，并提取七个特征（平均幅度、最大幅度、峰度、偏度、香农熵、标准差和四分位间距）。使用随机森林进行10倍交叉验证，以确定区分两组的准确性、敏感性和特异性。
我们能够准确区分两组（准确性=83%，特异性=83%，敏感性=84%）。特征重要性图显示，SS儿童在平静期的最大活动次数更多（U=-2.23，p=0.026），且其活动的方差更大（如四分位间距、香农熵），这使其与同龄人有所不同。
我们展示了机器学习技术的一种新用途，该技术成功地揭示了两组在平静期的区分特征。使用标准的睡眠和休息-活动指标很难捕捉到这些差异。我们的数据表明，平静期的活动可能是未来针对SS儿童研究的一个独特目标。
