Diagnostic Support System for the Detection of Attention Deficit Hyperactivity Disorder using EOG signals

Although Attention Deficit Hyperactivity Disorder (ADHD) is a common childhood disease, objective diagnostic methods are insufficient still. Current diagnostic methods include the subjective influence of the evaluator. In this context, in our study, we aimed to minimize the subjective effect of the evaluator with the objective diagnosis support system for ADHD. In our study, a visual stimulus follow-up test developed by us was applied to the patient with ADHD and healthy individuals, and electrooculogram (EOG) signals were recorded simultaneously. With the features extracted from EOG signals, Artificial Neural Networks (ANN) were used for the classification study of patients and healthy individuals, and it was determined that the classification of ADHD and healthy group could be distinguished by 81.76% performance. Thus, the outcomes that will contribute to the objective diagnosis of ADHD have been presented. The results are remarkable and important findings have been obtained that will contribute to the literature.