Diagnosis of Adult ADHD Using EEG Signals Based on the Spectrogram and Convolutional Neural Networks

Abstract

Attention deficit hyperactivity disorder (ADHD) is one of the most common mental disorders. This disease includes a combination of disorders in maintaining attention, hyperactivity, and impulsive behaviors. Diagnosis of ADHD is primarily clinical and based on history and examination. This study aims to provide a method for a more accurate diagnosis of adult ADHD using electroencephalography (EEG) signals. EEG signals recorded from 37 ADHD and 42 healthy adults were used as a control group in the age range of 20–68 years. We designed a convolutional neural network with three convolutional layers, three max-pooling layers, and one fully connected layer and trained it using spectrogram images obtained from EEG signals. The Cz channel was used for the diagnosis ADHD in four different states. To evaluate the performance of the proposed method, metrics such as accuracy, sensitivity, specificity, and precision were calculated. The results showed that using only one Cz channel has a good performance in diagnosing of ADHD. The highest accuracy of classification was related to the classification of two groups in the state when their eyes-open and eyes-closed spectrogram images were subtracted from each other. The results showed that proposed method based on deep learning can be a suitable method for diagnosing ADHD.