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 is one of the common neurodevelopmental disorders in children. This paper presents an automated approach for ADHD detection using the proposed entropy difference (EnD)- based encephalogram (EEG) channel selection approach. In the proposed approach%2C we selected the most significant EEG channels for the accurate identification of ADHD using an EnD-based channel selection approach. Secondly%2C a set of features is extracted from the selected channels and fed to a classifier. To verify the effectiveness of the channels selected%2C we explored three sets of features and classifiers. More specifically%2C we explored discrete wavelet transform (DWT)%2C empirical mode decomposition (EMD) and symmetrically-weighted local binary pattern (SLBP)-based features. To perform automated classification%2C we have used k-nearest neighbor (k-NN)%2C Ensemble classifier%2C and support vectors machine (SVM) classifiers. Our proposed approach yielded the highest accuracy of 99.29%25 using the public database. In addition%2C the proposed EnD-based channel selection has consistently provided better classification accuracies than the entropy-based channel selection approach. Also%2C the developed method?quality=80&w=800)
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