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

ABSTRACT: 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 var

SUBMITTER: Kucukosmanoglu M 

PROVIDER: S-EPMC12715250 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature