Autism spectrum disorders (ASD) is a complex developmental disorder characterized by impairments in social communication and stereotyped behaviors. Electroencephalograph (EEG), which can measure neurological changes associated with cortical synaptic activity, has been proven to be a powerful tool for detecting neurological disorders. The main goal of this study is to explore the effects of repetitive transcranial magnetic stimulation (rTMS) on behavioral response and EEG. We enrolled 32 autistic children, rTMS group (n = 16) and control group (n = 16) and calculated the relative power of the δ, θ, α, β rhythms in each brain area by fast Fourier transform and Welch’s method. We also compared Autism Behavior Checklist (ABC) scores of the patients before and after rTMS. The results showed a significant decrease in the relative power of the δ band on right temporal region and parietal region and also a decreased coherence on frontal region after rTMS intervention. The study proves that rTMS could have positive effects on behavior of attention, execution ability, and language ability of children and could reduce their stereotyped behavior and radical behavior.
The early diagnosis of children with autism spectrum disorders (ASD) is essential. Electroencephalography (EEG) is one of most commonly used neuroimaging techniques as the most accessible and informative method. In this study, approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn) and wavelet entropy (WaEn) were extracted from EEGs of ASD child and a control group, and Student's t-test was used to analyze between-group differences. Support vector machine (SVM) algorithm was utilized to build classification models for each entropy measure derived from different regions. Permutation test was applied in search for optimize subset of features, with which the SVM model achieved best performance. The results showed that the complexity of EEGs in children with autism was lower than that of the normal control group. Among all four entropies, WaEn got a better classification performance than others. Classification results vary in different regions, and the frontal lobe showed the best performance. After feature selection, six features were filtered out and the accuracy rate was increased to 84.55%, which can be convincing for assisting early diagnosis of autism.