EEG characteristics of children with attention-deficit/hyperactivity disorder. (15th May 2019)

• Record Type: Journal Article
• Authors: Chen, He; Chen, Wenqing; Song, Yan; Sun, Li; Li, Xiaoli
• Is Part Of: Neuroscience. Volume 406(2019)
• Page Start: 444
• Page End: 456
• Publication Date: 2019-05-15
• Subjects: EEG -- ADHD -- complexity -- bicoherence -- SVM; Neurochemistry -- Periodicals; Neurophysiology -- Periodicals; Neurology -- Periodicals; Neurochimie -- Périodiques; Neurophysiologie -- Périodiques; Neurochemistry; Neurophysiology; Electronic journals; Periodicals; Electronic journals; 612.8
• DOI: 10.1016/j.neuroscience.2019.03.048
• Languages: English
• ISSNs: 0306-4522

Abstract

Abstract: The electroencephalogram (EEG) is an informative neuroimaging tool for studying attention-deficit/hyperactivity disorder (ADHD); one main goal is to characterize the EEG of children with ADHD. In this study, we employed the power spectrum, complexity and bicoherence, biomarker candidates for identifying ADHD children in a machine learning approach, to characterize resting-state EEG (rsEEG). We built support vector machine classifiers using a single type of feature, all features from a method (relative spectral power, spectral power ratio, complexity or bicoherence), or all features from all four methods. We evaluated effectiveness and performance of the classifiers using the permutation test and the area under the receiver operating characteristic curve (AUC). We analyzed the rsEEG from 50 ADHD children and 58 age-matched controls. The results show that though spectral features can be used to build a convincing model, the prediction accuracy of the model was unfortunately unstable. Bicoherence features had significant between-group differences, but classifier performance was sensitive to brain region used. rsEEG complexity of ADHD children was significantly lower than controls and may be a suitable biomarker candidate. Through a machine learning approach, 14 features from various brain regions using different methods were selected; the classifier based on these features had an AUC of 0.9158 and an accuracy of 84.59%. These findings strongly suggest that the combination of rsEEG characteristics obtained by various methods may be a tool for identifying ADHD. Highlights: Various EEG characteristics reveal the underlying neuropsychological deviation of ADHD from different perspectives. We demonstrated the between-group difference of the features derived from power spectrum, complexity and bicoherence. We evaluated the discriminability of biomarker candidates and their combinations through a machine learning approach. The combination of EEG characteristics may be a tool for identifying ADHD.