Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning.

Abstract

Early diagnosis of attention deficit and hyperactivity disorder (ADHD) by experts is difficult. Some solutions using electroencephalography (EEG) signals have been presented in the literature to solve this problem. However, few studies have aimed to determine which recording statuses and which channels are effective for the diagnosis of ADHD. In this study, the effects of photic stimuli at different frequencies and on different channels on ADHD diagnosis were analysed. The main purpose of this study is to reveal the most effective channel and the most effective recording status for ADHD diagnosis. In this way, EEG data can be obtained from effective channels and recording statuses, and ADHD classification can be performed with fewer channels and higher accuracy. This can reduce the amount of data to be processed and the numbers of recording procedures. The dataset used in the experiments of this study was obtained using power spectral densities and spectral entropy values. These values were obtained from individuals with and without ADHD. When these data were applied to long short-term memory (LSTM), support vector machine (SVM), and artificial neural network classifiers, the highest accuracy was obtained with LSTM. The accuracy of LSTM was calculated as 88.88% on the Fp1,F7 channel and 92.15% in the eyes-closed resting state. Spectral entropy was found to contribute positively to the accuracy. As a result, the potential difference between Fp1,F7 electrodes in the eyes-closed resting state proved to be effective in diagnosing ADHD.