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 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.

Keywords: ADHD; RestEaze; cortical arousals; machine learning; sleep monitoring; wearables.

Figure 1. Top 30 Features for cortical arousal classification.

Top features ranked by importance using a Random Forest model. Feature importance was determined based on the mean decrease in impurity.

Figure 2. Arousal rate correlation.

Correlation between predicted and true arousal rates (n = 14). Strong positive correlations were observed (Spearman’s ρ = 0.89, p = 2.00 × 10⁻⁵; Kendall’s τ = 0.76, p = 3.95 × 10⁻⁵). The solid line represents the best-fit linear regression: y = 0.72x + 0.32.

Figure 3. Bland–Altman plot for arousal rates.

Bland–Altman plot comparing predicted and true (expert-labeled) arousal rates. The mean difference was +0.88 arousals per hour (Predicted − True), with 95% limits of agreement ranging from −1.40 to +3.17.

Figure 4. Temporal prediction of cortical arousals.

Predicted versus true cortical arousal events for three ADHD participants. Each subplot shows 1-minute window predictions across the sleep period (x-axis in hours). Blue crosses represent model-predicted arousals, and red circles indicate ground truth events.

Figure 5. Multimodal data preprocessing pipeline for arousal classification.

Raw data from the RestEaze^™ wearable system included PPG, 3-D accelerometer, 3-D gyroscope, and temperature sensors

Figure 6. PPG signal preprocessing and peak detection.

The top panel (a) shows the raw LED green PPG signal, which contains low-frequency drift and movement-related noise. The middle panel (b) displays the same signal after linear interpolation to 200 Hz and bandpass filtering (0.2–5 Hz). The bottom panel (c) shows the corresponding PPG signal quality over time, with values closer to 1 indicating cleaner, more reliable signal segments.

Figure 7. Accelerometer and gyroscope feature trends across sleep.

The top panel (a) shows the standard deviation of the X-axis accelerometer signal, reflecting variability in leg movement amplitude. The bottom panel (b) displays the spectral entropy of the Z-axis gyroscope signal, which quantifies the irregularity or complexity of rotational motion. Red markers indicate windows labeled as arousals, while blue markers denote non-arousal periods.