ADHD Children Identification With Multiview Feature Fusion of fNIRS Signals

Attention-deficit/hyperactivity disorder (ADHD) is one of highly prevalent neurodevelopmental disorders in children. It is of great value to find an objective, simple and non-invasive diagnosis method to compliment current diagnostic criteria. A discriminant correlation analysis (DCA) based multi-view feature fusion method combined with support vector machine (SVM) was proposed to identify ADHD children. The functional near-infrared spectroscopy signals from twenty-five ADHD children and twenty-five typically developing children were recorded during an n-back task. The DCA method was used to find a joint feature by fusing the features extracted in 1-back condition and 0-back condition. Then a SVM model was trained with the joint feature to identify the ADHD children. The results showed that the DCA-SVM method could identify the ADHD children with a classification accuracy of 88.0%. Meanwhile, part of left dorsolateral prefrontal cortex and left medial prefrontal cortex, posterior superior frontal cortex and bilateral temporal cortex were more discriminative in 1-back condition, left dorsolateral prefrontal cortex and part of left medial prefrontal cortex were more discriminative in 0-back condition. Our findings indicated that the DCA-SVM method is promising in clinical diagnosis of ADHD children.