Applicable Features of Electroencephalogram for Adhd Diagnosis Publisher



Khaleghi A¹ ; Birgani PM¹ ; Fooladi MF² ; Mohammadi MR¹ Show Affiliations
Source: Research on Biomedical Engineering Published:2020

Abstract

Purpose: Attention-deficit/hyperactivity disorder (ADHD) is a neuro-developmental and psychiatric disorder, which affects 11% of children around the world. Several linear and nonlinear biomarkers from electroencephalogram (EEG) signals have been proposed for diagnosis of ADHD to date. However, the determination of which type of analysis gives us the best feature and biomarker to diagnose ADHD is still controversial. In this study, we aimed to evaluate and compare several categories of features, extracted from EEG signals, for diagnosis of ADHD. Methods: Thirty 7–12-year-old children fulfilling the DSM5 criteria for ADHD and thirty healthy children underwent a noninvasive EEG evaluation at resting-state. After preprocessing, five categories of features including morphological, time, frequency, time-frequency, and nonlinear features were extracted from EEGs. The efficacy of each feature category in ADHD diagnosis was determined using statistical analysis, receiver operating characteristic (ROC) curves, and evidential K-nearest neighbor (EKNN) classifier. Results: Statistical analyses showed that 13.15, 13.68, 14.47, 14.03, and 34.73% of extracted features were significant (p < 0.05) in morphological, time, frequency, time-frequency, and nonlinear domains, respectively. The largest AUC values for the five morphological, time, frequency, time-frequency, and nonlinear feature categories, were 0.870, 0.796, 0.824, 0.806, and 0.899, respectively. We obtained the accuracies of 77.43% using morphological features, 74.09% using time features, 80.44% using frequency features, 78.50% using time-frequency features, and 86.40% using nonlinear features. Conclusion: Our results showed that EEG nonlinear analysis is a good quantitative tool to detect the abnormalities of the electrical activity of the brain in ADHD. This result was expected due to the complexity of the brain and the nonlinear nature of the EEG signal. Therefore, better outcomes may be expected in early diagnosis of diseases, especially psychiatric disorders, by increasing the use of nonlinear methods. © 2020, Sociedade Brasileira de Engenharia Biomedica.