ADHD classification by feature space separation with sparse representation

In recent years, increasing attention has been paid to Attention Deficit Hyperactivity Disorder (ADHD) as one of the most common neurobehavioral diseases in school-age children. In this paper, sparse representation is used as an effective tool to analyze the corresponding functional brain connectivities from functional MRI data. Two diagnosis models, i.e., ADHD and healthy control models, are employed to deal with the ADHD patients and control subjects. In these models, we learn the feature spaces of ADHD and control components adaptively via dictionary learning. Moreover, we also perform an energy operation as a penalty term in the models, which forces the feature energy of subjects minimized in the wrong feature spaces. Following these models, we carefully design a framework for ADHD classification, where more sophisticated methods are adopted including synthetic minority oversampling technique and SVM. In the experiments on ADHD-200 dataset, our method can achieve a remarkable accuracy compared with several state-of-the-art classification methods.