Abstract: R. Yaghoobi, S. Azadi, P. Keshavarzi (2019)

Attention-deficit/hyperactivity disorder (ADHD) as an behavioral challenge, which affects the people's learning and experiences, is one of the disorders, which lead to reducing the complexity of brain processes and human behaviors. Nevertheless, most studies on the ADHD have currently focused on the frequency content of single and multichannel EEG segments and a few studies, which have tried to indicate this reduce, employed the approximate entropy, which is usually used for measuring the complexity of EEG signals in terms of their information. In this study, we tried to provided a different view of this reduce by focused on the tissue of patterns appeared on the auto-recurrence plots obtained from the trajectory of phase space reconstructed from the EEG signals recorded under the open-eyes and closed-eyes resting conditions. The outcomes of this analysis generally indicated a significant difference in the tissue of recurrence plots, which its reason was the increase of recurrence rate in the plots. Separating children with ADHD using the support vector machines with the radial basis function kernel developed by the features extracted from the recurrence plots also provided a remarkable accuracy (91.3% for the testing sets), which means the change in the tissue of recurrence plots relevant to the EEG signals of ADHD children. Comparing the classification results of this research and previous researches nonetheless represented that the statistical population usually affects this accuracy. Therefore, these findings generally proved that although the classifiers developed by an EEG segment are not applicable to clinical conditions, the ADHD averagely leads to reducing the complexity of EEG processes.

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Loss Detection of Recurrence Rate in the EEG Signals of Children with ADHD