ELM-based classification of ADHD patients using a novel local feature extraction method
Yang Li, Zhichao Lian, Min Li, Zhonggeng Liu, Liang Xiao, Zhihui Wei · 2016
Recently, it has been an increasing interest in modeling abnormal temporal dynamics of functional interactions in psychiatric disorders. However, the accuracy of differentiating attention-deficit/hyperactivity disorder (ADHD) children form normal children has still much space for improvement. To further improve the accuracy, the key issue is to extract more effective features from original fMRI data. In this paper, we propose a novel local feature extraction method named Local Binary Encoding Method (LBEM) that can effectively characterize functional interaction patterns (FIPs). In particular, we show that the proposed method can well discriminate the functional interaction abnormalities, which is composed of a Bayesian connectivity change point model, a local feature extraction method and a kernel Extreme Learning Machine (ELM)-based classifier. The experiment on a real dataset of 23 ADHD children and 45 normal control (NC) children has shown that our method achieved better classification performance compared to the existing methods.