Diagnosis for autism spectrum disorder based on electroencephalogram dynamic local graph theory indices

Abstract

Abstract Autism spectrum disorder (ASD) is a complex brain disorder that damages patients’ cognitive and social skills. Previous studies using static functional connectivity analysis from electroencephalogram (EEG) neglected dynamic traits of EEG signal. This study thus combined the dynamic functional connectivity with local graph theory indices to seek for biomarkers to diagnose the ASD. Traditionally, static local graph theory index was calculated using the entire EEG signal, and afterward 6 derivative dynamic graph theory indices were calculated by sliding windows with different width and moving step. For each index above, 95 features could be extracted. Further, they were selected and compared for its classification performance by support vector machine-recurrence feature elimination method. Our results indicated that dynamic graph theory indices in the 3s window width and 50% moving step achieved the best classification performance with average accuracy 0.952, which was better than its static counterpart. The distribution for selected features showed a preference in the frontal lobe and Beta band. Our studies indicated the window width of 3s with 50% moving step could be the appropriate settings for dynamic graph theory analysis, and the distribution tendency for the frontal lobe and Beta band could render researchers’ fresh perspectives into mechanism studies.