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Attention deficit hyperactivity disorder is a neurodevelopmental condition associated with varying levels of hyperactivity, inattention, and impulsivity. This study investigates brain function in children with attention deficit hyperactivity disorder using measures of nonlinear dynamics in EEG signals during rest. During eyes-closed resting, 19 channel EEG signals were recorded from 12 ADHD and 12 normal age-matched children. We used the multifractal singularity spectrum, the largest Lyapunov exponent, and approximate entropy to quantify the chaotic nonlinear dynamics of these EEG signals. As confirmed by Wilcoxon rank sum test, largest Lyapunov exponent over left frontal-central cortex exhibited a significant difference between ADHD and the age-matched control groups. Further, mean approximate entropy was significantly lower in ADHD subjects in prefrontal cortex. The singularity spectrum was also considerably altered in ADHD compared to control children. Evaluation of these features was performed by two classifiers: a Support Vector Machine and a Radial Basis Function Neural Network. For better comparison, subject classification based on frequency band power was assessed using the same types of classifiers. Nonlinear features provided better discrimination between ADHD and control than band power features. Under four-fold cross validation testing, support vector machine gave 83.33% accurate classification results.

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Several neurocognitive studies have indicated that children with attention-deficit/hyperactivity disorder (ADHD) exhibit cognitive deficits in perceptual timing functions; however, only a few electroencephalographic studies have investigated their time reproduction abilities. In the present research, 15 children with ADHD were studied along with 19 age-matched control subjects (aged 7-11 years) as they attempted to reproduce shorter (1000 ms) and longer (2200 ms) time intervals. Trial-mean event-related potential (ERP) and event-related spectral perturbation measures were used to compare the electroencephalography (EEG) source-level activity patterns of the ADHD and control subjects during the time-encoding and reproduction phases. For both short and long intervals, the performance of subjects with ADHD was significantly less accurate and more variable than that of the age-matched controls. During the encoding phase, the ADHD and control ERPs differed significantly for the midfrontal source cluster. The midfrontal P300 amplitude evoked by the onset of the encoding phase was significantly higher for the ADHD group. Similarly, the amplitude of contingent negative variation for the ADHD group was lower for the midfrontal independent component (IC) cluster during long-interval encoding. Theta event-related synchronization in the right occipital cluster also differed between groups during both the encoding and reproduction phases. Moreover, children with ADHD failed to show a frontal selection positivity component in the reproduction phase. Significant differences were found in the mean alpha power for the prefrontal source cluster during the time reproduction phase. These results suggest electrophysiological evidence for time perception deficiencies, selective visual processing disturbances, and working memory impairment in children with ADHD.

Event-related potential (ERP) is one of the most informative and dynamic methods of monitoring cognitive processes, which is widely used in clinical research to deal with a variety of psychiatric and neurological disorders such as attention-deficit/hyperactivity disorder (ADHD). In this study, there were 60 participants including 30 patients with ADHD and 30 subjects as a control group. Their ERP signals were recorded by three electrodes in two modalities. After a preprocessing step, several features such as band power, fractal dimension, autoregressive (AR) model coefficients and wavelet coefficients were extracted from recorded signals. The aim of this study is to achieve a high classification rate. The results show that the fractal dimension–wavelet combination features provided a good discriminative capability; it should be noted that this improvement was achieved by combining all sets of features and applying a feature selection algorithm, which resulted in a maximum accuracy rate of 88.77 and 95.39% in support vector machine (SVM) and v_SVM classification algorithms using a 10-fold cross-validation approach, respectively. ERP has been widely used for clinical diagnosis and cognitive processing deficits in children with ADHD. To increase the accuracy of the diagnostic process of ADHD, ERP signals were recorded to extract some specific ERP features related to this disease for classifying the two groups. The results show that the Fra-wave characterization produced the best average accuracy with an efficiency of 99.43% for v_SVM classifier, compared with 97.65% efficiency for the wavelet features and the other features.