Attention Deficit Hyperactivity Disorder Diagnosis using non-linear univariate and multivariate EEG measurements: a preliminary study.

Attention Deficit Hyperactivity Disorder (ADHD) is a common neuro-developmental disorder of childhood. In this study we propose two classification algorithms for discriminating ADHD children from normal children using their resting state Electroencephalography (EEG) signals. One algorithm is based on the univariate features extracted from individual EEG recording channels and the other is based on the multivariate features extracted from brain lobes. We focused on entropy measures as non-linear univariate and multivariate features. Average power, Theta/Beta Ratio (TBR), Shannon Entropy (ShanEn), Sample Entropy (SampEn), Dispersion Entropy (DispEn) and Multiscale SampEn (MSE) were extracted as linear and non-linear univariate features. Besides, multivariate SampEn (mvSE) and multivariate MSE (mvMSE) were extracted as non-linear multivariate features. Classification was followed by three classifiers: Support Vector Machines (SVM) with different kernels, k-Nearest Neighbor (kNN) and Probabilistic Neural Network (PNN). Complexity analysis of multi-channel EEG data was performed using mvMSE approach. Entropy mapping as a useful tool was used to visually track changes of entropies in various brain regions. Based on achieved results, ADHD children have higher brain activity and TBR compared to normal children, while their neural system is more regular. Besides, ADHD children have reduced dynamical complexity of neural system. Finally, the accuracy of 99.58% was achieved in classification based on a combination of non-linear univariate features by Radial Basis Function (RBF) SVM. For classification based on brain regions using multivariate features, 90.63% accuracy was achieved by PNN.