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Ripplet Ⅱ Transform and Higher Order Cumulants from R-fMRI data for Diagnosis of Autism
Diagnose of autism spectral disorder (ASD) as a mental disorder by machine learning algorithms has attracted many attentions. Finding biomarkers from rest state functional magnetic resonance imaging (R-fMRI) data is one of the common methods used for classifying ASDs from normal healthy person (HP). This paper presents Eickhoff-Zilles (EZ) atlas to evaluate time courses for 20 ASDs and 16 HPs in 116 regions of interest (ROIs). To extract the effective features for classification, Ripplet Ⅱ transform and higher order cumulants are proposed. Then, two sample t-test (Ttest2) is employed to select the discriminative features for classification. After normalizing the selected feature vector, the data are classified by support vector machine (SVM). The results show that the proposed method achieves 91.67% accuracy which outperforms previous works