Detection of cortical arousals in sleep using multimodal wearable sensors and machine learning

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) undergoing assessment for restless leg syndrome-related sleep disruption. The Random Forest model achieved the best overall performance, with a ROC-AUC of 0.94 and an AUPRC of 0.55, substantially higher than the baseline prevalence of arousals (~ 0.07). 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.