Title:Identification of Attention Deficit/Hyperactivity Disorder in Children Using Multiple ERP Features
VOLUME: 13 ISSUE: 5
Author(s):Wenjie Li, Tiantong Zhou, Ling Zou*, Jieru Lu, Hui Liu and Suhong Wang*
Affiliation:School of Information Science and Engineering, Changzhou University, Changzhou, School of Information Science and Engineering, Changzhou University, Changzhou, School of Information Science and Engineering, Changzhou University, Changzhou, School of Information Science and Engineering, Changzhou University, Changzhou, School of Business, Changzhou University, Changzhou, Department of Neuroscience, The Third Affiliated Hospital of Soochow University, Changzhou
Keywords:Attention deficit/hyperactivity disorder, executive function, electroencephalogram, feature extraction, classification, event related potential.
Abstract:Background and Objective: Attention deficit hyperactivity disorder (ADHD) is a typical
neurodevelopmental disorder occurs in children’s early school-age, which often results in serious
executive dysfunction. Recent ADHD studies highlight the great potential of non-invasive event-related
potential (ERP) technique. It is thus worth combining multiple features to form sensitive and robust
biomarkers to distinguish ADHD from normal children.
Methods: In this paper, we collected the EEG signals of sixty-eight ADHD children and seventy-three
age-match typically developing children during a classic Simon-spatial Stroop task. A channel
optimization method was used to select the feature channel. Time-domain features and frequencydomain
features were extracted from EEG data. Three classifiers were used to classify ADHD children
from typically developing children by using multiple features as well as each single feature.
Results: ADHD children showed weaker N2 and P2 signals than typically developing children.
Behavior response results showed that, children with ADHD exhibited lower correct response rates,
longer average response time and higher data variance. In classification experiment, performance of
three classifiers trained on multiple features was much better than that on single feature. Multiple
features classification achieved the highest accuracy of 96.6%, while single time-domain and frequencydomain
feature only achieved the highest accuracy of 88.10% and 92.85% respectively. All the highest
accuracies were achieved on feature channel in inferior parietal cortex.
Conclusion: Feature channel generally performed better than empirical channel. The multiple ERP
features classification method has a good recognition accuracy, being worth researching in ADHD’s
auxiliary diagnosis.
