Graph-Weighted High-Dimensional Information-Based Similarity for Detecting Altered Brain Functional Connectivity in Sleep Apnea Patients

Sleep apnea (SA) is a common respiratory disease associated with changes in the cortical connectivity pattern of the brain, leading to a growing social and medical burden. The electroencephalogram (EEG)-based analysis can help to understand the pathological mechanism of SA in the brain. Information-based similarity (IBS) is an effective method for measuring the nonlinear similarity of two physiological signals and has the potential for SA screening. However, the traditional IBS method can only extract local information, failing to fully capture pathological EEG patterns characterized by critical cross-frequency dynamics and regional variability. To address these issues, we propose graph-weighted high-dimensional information-based similarity (HGIBS), which integrates 2-D IBS into global information, thus improving the accuracy and reliability of SA detection. We use EEG energy sequences from multiple brain regions and frequency bands, divide their wave patterns into several modes, and calculate their similarity by comparing the distribution of the appearance frequency of the modes. Additionally, we construct the brain network graph and extend the calculation of similarity to high-dimensional space, thereby integrating local information into global information. We evaluated the performance of the HGIBS method on dataset of 88 participants and achieved a correlation between HGIBS and apnea-hypopnea index (AHI) of 0.730, with an accuracy of 89.4% in severe SA classification, demonstrating the superiority. This method has the potential to become a new SA screening indicator and provide a deeper understanding of the pathological mechanism of SA research.