Exploring Effective Features in ADHD Diagnosis among Children through EEG/Evoked Potentials using Machine Learning Techniques

With the aid of intelligent system approaches, the present study aimed at extracting and investigating effective features for detecting Attention-Deficit/Hyperactivity Disorder (ADHD) in children. With this end in view, 103 children, aged from 6 to 10, were recruited for this study, among which 49 cases were assigned to the treatment group (ADHD children) and the remaining 54 cases to the control group (healthy children). The disorder diagnosis was performed using the well-known, relevant psychological questionnaires and clinical interviews with expert psychologists. Data collection consisted of EEG signals in eyes open and eyes closed states, as well as GO/NOGO task for about 3 hours for every participant. The extracted features consisted of the amplitudes and latency in Event-Related Potential (ERP) and the power spectrum in the sleep mode signals. Approximately 826 features of 19 channels were extracted in the standard 10-20 system and different task conditions. A set of features were selected with the aid of the feature selection methods, and then the selected features were analyzed by neuroscientists, and the irrelevant ones were removed. Next, the classification methods and their performance evaluation were applied. Finally, the best results in terms of the corresponding feature vector and classification method were presented. The healthy and ADHD groups were classified with 75.8% accuracy using the Support Vector Machine (SVM) method. The results showed that the use of selection of effective features with the aid of intelligent system techniques under the supervision of experts leads us to reach robust biomarkers in the detection of disorders.