Migovich Miroslava, Ullal Akshith, Fu Cary, Peters Sarika U, Sarkar Nilanjan
Department of Mechanical Engineering, Vanderbilt University, Nashville, TN,USA.
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
Digit Health. 2023 Aug 1;9:20552076231191622. doi: 10.1177/20552076231191622. eCollection 2023 Jan-Dec.
Sleep is vital to many processes involved in the well-being and health of children; however, it is estimated that 80% of children with Rett syndrome suffer from sleep disorders. Caregiver reports and questionnaires, which are the current method of studying sleep, are prone to observer bias and missed information. Polysomnography is considered the gold standard for sleep analysis but is labor and cost-intensive and limits the frequency of data collection for sleep disorder studies. Wearable digital health technologies, such as actigraphy devices, have shown potential and feasibility as a method for sleep analysis in Rett syndrome, but have not been validated against polysomnography. Furthermore, the collected accelerometer data has limitations due to the rigidity, periodic limb movement, and involuntary muscle contractions prevalent in Rett syndrome. Heart rate and electrodermal activity, along with other physiological signals, have been linked to sleep stages and can be utilized with machine learning to provide better resistance to noise and false positives than actigraphy. This research aims to address the gap in Rett syndrome sleep analysis by comparing the performance of a machine learning model utilizing both accelerometer data and physiological data features to the gold-standard polysomnography for sleep analysis in Rett syndrome. Our analytical validation pilot study ( = 7) found that using physiological and accelerometer features, our machine learning models can differentiate between awake, non-rapid eye movement sleep, and rapid eye movement sleep in Rett syndrome children with an accuracy of 85.1% when using an individual model. Additionally, this work demonstrates that it is feasible to use digital health technologies in Rett syndrome, even at a young age, without data loss or interference from repetitive movements that are characteristic of Rett syndrome.
睡眠对于儿童的幸福和健康所涉及的许多过程至关重要；然而，据估计，80%的雷特综合征患儿患有睡眠障碍。目前用于研究睡眠的方法，即照顾者报告和问卷调查，容易出现观察者偏差和信息遗漏。多导睡眠图被认为是睡眠分析的金标准，但它 labor and cost-intensive（此处原文有误，应是labor-intensive and costly，即劳动强度大且成本高），并且限制了睡眠障碍研究的数据收集频率。可穿戴数字健康技术，如活动记录仪设备，已显示出作为雷特综合征睡眠分析方法的潜力和可行性，但尚未与多导睡眠图进行验证。此外，由于雷特综合征中普遍存在的僵硬、周期性肢体运动和非自愿肌肉收缩，所收集的加速度计数据存在局限性。心率和皮肤电活动，以及其他生理信号，已与睡眠阶段相关联，并且可以与机器学习一起使用，以提供比活动记录仪更好的抗噪声和抗误报能力。本研究旨在通过比较利用加速度计数据和生理数据特征的机器学习模型与用于雷特综合征睡眠分析的金标准多导睡眠图的性能，来填补雷特综合征睡眠分析方面的空白。我们的分析验证试点研究（n = 7）发现，使用生理和加速度计特征，我们的机器学习模型在使用单个模型时，可以区分雷特综合征患儿的清醒、非快速眼动睡眠和快速眼动睡眠，准确率为85.1%。此外，这项工作表明，即使在年幼时，在雷特综合征中使用数字健康技术也是可行的，不会出现数据丢失或受到雷特综合征特有的重复运动的干扰。
