Characterization of the abnormal cortical effective connectivity in patients with sleep apnea hypopnea syndrome during sleep. (June 2021) - Pan, Yu; Yang, Juan; Zhang, Tingting; Wen, Jinfeng; Pang, Feng; Luo, Yuxi
Highlights: Symbolic transfer entropy quantified cortical effective connectivity during sleep. Posterior-to-anterior pattern of information flow was compared between groups. Abnormal cortical effective connectivity in SAHS patients was characterized. Posterior-to-anterior features got a good result in SAHS severities classification. Cortical effective connectivity may perform as EEG biomarkers in SAHS diagnosis. Abstract: Background and objectives: Sleep apnea hypopnea syndrome (SAHS) is a prevalent sleep breathing disorder that can lead to brain damage and is also a risk factor for cognitive impairment and some common diseases. Studies on cortical effective connectivity (EC) during sleep may provide more direct and pathological information and shed new light on brain dysfunction due to SAHS. However, the EC is rarely explored in SAHS patients, especially during different sleep stages. Methods: To this end, six-channel EEG signals of 43 SAHS patients and 41 healthy participants were recorded by whole-night polysomnography (PSG). The symbolic transfer entropy (STE) was applied to measure the EC between cortical regions in different frequency bands. Posterior-anterior ratio (PA) was employed to evaluate the posterior-to-anterior pattern of information flow based on overall cortical EC. The statistical characteristics of the STE and PA and of the intra-individual normalized parameters (STE* and PA*) were served as different feature sets for classifying the severity of SAHS. Results: Although the patterns of STE across electrodes were similar, significant differences were found between the patient and the control groups. The variation trends across stages in the PA were also different in multiple frequency bands between groups. Important features extracted from the STE* and PA* were distributed in multiple rhythms, mainly in δ, α, and γ. The PA* feature set gave the best results, with accuracies of 98.8% and 83.3% for SAHS diagnosis (binary) and severity classification (four-way). Conclusions: These results suggest that modifications in cortical EC were existed in SAHS patients during sleep, which may help characterize cortical abnormality in patients.