An EEG Screening Method for Severe Obstructive Sleep Apnea Based on Limited Penetrable Difference Visibility Graph and Graph Convolutional Network

Obstructive Sleep Apnea (OSA) is a common respiratory disease characterized by recurrent airway block during sleep, which does great harm to the human body. Utilizing the electroencephalogram (EEG) has been proven instrumental in OSA detection, as sleep apnea occurrences induce discernible alterations in EEG patterns. In this study, we propose a Limited Penetrable Difference Visibility Graph (LPDVG) method to screen severe OSA. This method exhibits strong anti-noise performance, effective information extraction, and a certain degree of generalization ability. First of all, this study constructed LPDVG complex network and calculated the information entropy of the degree sequence in six leads. Subsequently, this study weighted the information entropy of each lead using the mutual information between leads to fuse information from the whole brain. Eventually, a classification model for the Graph Convolutional Network (GCN) was trained to detect patients with severe OSA. Using a dataset of 88 participants, we tested and evaluated this approach. The results showed a strong correlation between the extracted feature and AHI, with a Pearson correlation of 0.792. The accuracy, specificity, sensitivity, and area under the curve (AUC) of the GCN classification were 82.95%, 83.87%, 80.77%, and 0.905. Moreover, there are significant differences in LPDVG_wSEN between patients in severe and non-severe OSA groups. Compared to those with non-severe OSA, brain activity in patients with severe OSA is more disorganized, especially in the theta frequency band. EEG data indicating this elevated activity is associated with disturbed sleep patterns in individuals with OSA.