فهرست مطالب f. moghaddam

The diagnosis of neurodevelopmental diseases, such as attention deficit/hyperactivity disorder (ADHD), has gained great attention in clinical studies due to its effect on the quality of human life. This disorder is caused by genetic factors, and anatomical and functional brain abnormalities, which can lead to timing deficits, working memory impairments, and inattention. Since the investigation of symmetry between activities of different brain regions may play an important role in the early diagnosis of this disorder, similarity quantification between brain signals is one of the existing challenges in the field of ADHD detection. The goal of this study is to compute symmetry between certain cortical areas from inter-hemispheric or intra-hemispheric channel pairs. For this purpose, a new algorithm based on dynamic time warping as a bivariate feature extraction step and support vector machine (SVM) classifier has been proposed. The proposed method's ability in distinct brain regions has also been explored.The proposed methods have been evaluated on electroencephalogram (EEG) recordings of 14 ADHD children and 19 age-matched healthy controls performing a time-reproduction task. It has achieved high average accuracy rates of 94.38±0.007 in discriminating between healthy controls and patients with ADHD. The experimental results have also demonstrated the superior performance of the proposed method in comparison with previous ADHD detection methods using EEG signals.