Resting-State EEG Functional Connectivity for Brain Function Analysis and Severity Classification in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), one of the most common sleep disorders globally, is closely linked to brain function. Resting-state electroencephalography (EEG), due to its convenience, cost-effectiveness, and high temporal resolution, serves as a valuable tool for exploring the human brain function. This study utilized a large cohort with 968 participants who joined in 15-minute daytime resting-state EEG acquisition and overnight polysomnography (PSG) monitoring. Participants were categorized into healthy controls and mild, moderate, and severe OSA groups based on apnea-hypopnea index (AHI) derived from PSG data. Resting-state EEG functional connectivity (FC) was estimated using correlation (Corr), coherence (Coh), phase-locking value (PLV), and phase lag index (PLI). Results showed that FC between most nodes increased with the OSA severity, which suggest the potential neural compensation. However, regional decreases emerged in the right central, right frontal, left central, and left parieto-occipital regions. Higher frequency bands exhibited fewer enhanced FC connections. Graph-theoretical analysis revealed reduced centrality, indicating weakened communication hubs and potential topological reorganization. Multivariate analysis with adjustment of age, sex, and BMI, was also used as a feature selection strategy, identified effective FC features of OSA severity (p value < adjusted significance threshold, 2.15e-5). These FC features were used in machine learning models for severity classification and enhanced interpretability. The Corr-based XGBoost model achieved the highest performance, with an accuracy of 0.79 and AUC of 0.90. These findings highlight OSA-related brain function alterations and demonstrate that resting-state EEG FC provides a non-invasive, task-free and interpretable tool for OSA severity classification without disrupting natural sleep.