Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal

• Published in: IET systems biology 2019-10, Vol.13 (5), p.260-266
• Main Authors: Boroujeni, Yasaman Kiani, Rastegari, Ali Asghar, Khodadadi, Hamed
• Format: Article
• Language: English
• Subjects: ADHD; Adolescent; approximate entropies; Attention Deficit Disorder with Hyperactivity - diagnosis; Attention Deficit Disorder with Hyperactivity - physiopathology; attention deficit hyperactivity disorder; Brain - physiopathology; brain neuronal activity; chaotic behaviour; chaotic indices; chaotic time series; Child; Child, Preschool; common behavioural disorder; early diagnosis; EEG signals; electroencephalogram signals; Electroencephalography; entropy; feature extraction; Female; fractals; Humans; identifying classifying children; life quality; Lyapunov methods; Male; medical disorders; medical signal processing; neurophysiology; nonlinear analysis methods; Nonlinear Dynamics; nonlinear extracted features; nonlinear features; Research Article; signal classification; Signal Processing, Computer-Assisted; time series
• ISSN: 1751-8849; 1751-8857
• DOI: 10.1049/iet-syb.2018.5130

Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may be found in 5%–8% of the children. Early diagnosis of ADHD is crucial for treating the disease and reducing its harmful effects on education, employment, relationships, and life quality. On the other hand, non-linear analysis methods are widely applied in processing the electroencephalogram (EEG) signals. It has been proved that the brain neuronal activity and its related EEG signals have chaotic behaviour. Hence, chaotic indices can be employed to classify the EEG signals. In this study, a new approach is proposed based on the combination of some non-linear features to distinguish ADHD from normal children. Lyapunov exponent, fractal dimension, correlation dimension and sample, fuzzy and approximate entropies are the non-linear extracted features. For computing, the chaotic time series of obtained EEG in the brain frontal lobe (FP1, FP2, F3, F4, and Fz) need to be analysed. Experiments on a set of EEG signal obtained from 50 ADHD and 26 normal cases yielded a sensitivity, specificity, and accuracy of 98, 92.31, and 96.05%, respectively. The obtained accuracy provides a significant improvement in comparison to the other similar studies in identifying and classifying children with ADHD.