Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder

A method is presented for investigation of EEG of children with autistic spectrum disorder using complexity and chaos theory with the goal of discovering a nonlinear feature space. Fractal Dimension is proposed for investigation of complexity and dynamical changes in autistic spectrum disorder in brain. Two methods are investigated for computation of fractal dimension: Higuchi's Fractal Dimension and Katz's Fractal Dimension. A wavelet-chaos-neural network methodology is presented for automated EEG-based diagnosis of autistic spectrum disorder. The model is tested on a database of eyes-closed EEG data obtained from two groups: nine autistic spectrum disorder children, 6 to 13 years old, and eight non-autistic spectrum disorder children, 7 to 13 years old. Using a radial basis function classifier, an accuracy of 90% was achieved based on the most significant features discovered via analysis of variation statistical test, which are three Katz's Fractal Dimensions in delta (of loci Fp2 and C3) and gamma (of locus T6) EEG sub-bands with P < 0.001.