Detection of Attention Deficit and Hyperactivity Disorder by Nonlinear Eeg Analysis

Abstract

This study proposes to experts a fast and highly successful algorithm for the diagnosis of ADHD disorder using EEG (Electroencephalogram) signals obtained during the Attention task, reducing their dependence on subjective evaluations. Accordingly, EEG signals obtained from 61 ADHD and 60 control participants were analyzed using nonlinear features (approximate entropy, Petrosian, and Lyapunov exponent). After feature extraction, the classification process was performed using support vector machine (SVM), and K-Nearest-Neighbor (KNN), and ensemble learning. In this study t-test based and location based feature selection methods were used. We used only features that were extracted from prefrontal and frontal regions. The highest accuracy that was reached in this study was 95.8%.