# Detection of Cortical Arousals in Sleep Using Multimodal Wearable Sensors and Machine Learning

## TL;DR

This study introduces a wearable device and machine learning framework to detect sleep disruptions in children with ADHD.

## Contribution

A noninvasive framework using wearable sensors and machine learning to detect cortical arousals in sleep.

## Key findings

- Movement intensity features were most effective for arousal detection.
- Random Forest model achieved a ROC AUC of 0.94 in detecting cortical arousals.
- The framework was tested in a pediatric ADHD cohort with sleep concerns.

## 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 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.