Classification of Attention Deficit/Hyperactivity Disorder (ADHD) by Extracting Non-linear Features of Children's EEG

Ahmadreza Heidarpour; Mousa Shamsi; Faramarz Alsharif; Bruno Senzio-Savino; Mohammad Reza Alsharif

Summary

International Technical Conference on Circuits/Systems, Computers and Communications

2016

Session Number:P3

Session:

Number:P3-22

Classification of Attention Deficit/Hyperactivity Disorder (ADHD) by Extracting Non-linear Features of Children's EEG

Ahmadreza Heidarpour, Mousa Shamsi, Faramarz Alsharif, Bruno Senzio-Savino, Mohammad Reza Alsharif ,

pp.1073-1075

Publication Date:2016/7/10

Online ISSN:2188-5079

DOI:10.34385/proc.61.P3-22

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Summary:

ADHD is the most frequent disorder in children. According to researchers, it is the most common disorder of childhood and detection of that is very important. In this study, database includes totally 30 subjects, of which 12 are the ADHD and the other 18 are healthy subjects. Using non-linear features, that selected by Wilcoxon test, we address the classification of both groups (healthy and hyperactive). In this paper, the support victor machine (SVM) classifier with 4 kernel function (Polynomial kernel_3, Multilayer perceptron(mlp), Radial basis function(rbf), and quadratic) is used.