Cortical arousals are brief brain activations that disrupt sleep continuity and contribute to cardiovascular, cognitive, and behavioral impairments. Although polysomnography is the gold standard for arousal detection, its cost and complexity limit use in long-term or home-based monitoring. This study presents a noninvasive machine learning based framework for detecting cortical arousals using the RestEaze™ system, a leg-worn wearable that records multimodal physiological signals including accelerometry, gyroscope, photoplethysmography (PPG), and temperature. Across multiple methods tested, including logistic regression, XGBoost, and Random Forest classifiers, we found that features related to movement intensity were the most effective in identifying cortical arousals, while heart rate variability had a comparatively lower impact. The framework was evaluated in 14 children with attention-deficit/hyperactivity disorder (ADHD) who were being assessed for possible restless leg syndrome related sleep disruption. The Random Forest model achieved the best performance, with a ROC AUC of 0.94. For the arousal class specifically, it reached a precision of 0.57, recall of 0.78, and F1-score of 0.65. These findings support the feasibility of wearable-based machine learning for real-world arousal detection, demonstrated here in a pediatric ADHD cohort with sleep-related behavioral concerns.

皮层唤醒是短暂的大脑激活事件，会破坏睡眠的连续性，并导致心血管、认知和行为功能障碍。尽管多导睡眠图是检测唤醒的标准方法，但其成本和复杂性限制了它在长期或家庭监测中的应用。本研究提出了一种基于机器学习的非侵入性框架，使用RestEaze™系统（一种记录包括加速度计、陀螺仪、光电容积描记法(PPG)和温度在内的多模态生理信号的腿部穿戴设备）来检测皮层唤醒。在测试的方法中，包括逻辑回归、XGBoost和随机森林分类器，我们发现与运动强度相关的特征最有效地识别皮层唤醒，而心率变异的影响相对较低。该框架在一个由14名被评估是否有注意力缺陷多动障碍（ADHD）相关不安腿综合征睡眠中断的儿童组成的队列中进行了评价。随机森林模型取得了最佳性能，在ROC AUC上达到了0.94。在具体识别觉醒事件时，其精度为0.57，召回率为0.78，F1得分为0.65。这些发现支持了基于可穿戴设备的机器学习在现实世界中检测唤醒事件的可行性，并在此研究中展示了在具有睡眠相关行为问题的儿科ADHD队列中的应用。