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 ( = 16) and control group ( = 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.

Extraction and analysis of electroencephalogram (EEG) signal characteristics of patients with autism spectrum disorder (ASD) is of great significance for the diagnosis and treatment of the disease. Based on recurrence quantitative analysis (RQA)method, this study explored the differences in the nonlinear characteristics of EEG signals between ASD children and children with typical development (TD). In the experiment, RQA method was used to extract nonlinear features such as recurrence rate (RR), determinism (DET) and length of average diagonal line (LADL) of EEG signals in different brain regions of subjects, and support vector machine was combined to classify children with ASD and TD. The research results show that for the whole brain area (including parietal lobe, frontal lobe, occipital lobe and temporal lobe), when the three feature combinations of RR, DET and LADL are selected, the maximum classification accuracy rate is 84%, the sensitivity is 76%, the specificity is 92%, and the corresponding area under the curve (AUC) value is 0.875. For parietal lobe and frontal lobe (including parietal lobe, frontal lobe), when the three features of RR, DET and LADL are combined, the maximum classification accuracy rate is 82%, the sensitivity is 72%, and the specificity is 92%, which corresponds to an AUC value of 0.781. The research in this paper shows that the nonlinear characteristics of EEG signals extracted based on RQA method can become an objective indicator to distinguish children with ASD and TD, and combined with machine learning methods, the method can provide auxiliary evaluation indicators for clinical diagnosis. At the same time, the difference in the nonlinear characteristics of EEG signals between ASD children and TD children is statistically significant in the parietal-frontal lobe. This study analyzes the clinical characteristics of children with ASD based on the functions of the brain regions, and provides help for future diagnosis and treatment.
Autism Spectrum Disorder/diagnosis* ; Autistic Disorder ; Brain ; Child ; Electroencephalography ; Humans ; Recurrence

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 -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.
Algorithms ; Autism Spectrum Disorder ; classification ; diagnosis ; Child ; Electroencephalography ; Entropy ; Humans ; Support Vector Machine

In this paper, a feature extraction algorithm of weighted multiple multiscale entropy is proposed to solve the problem of information loss which is caused in the multiscale process of traditional multiscale entropy. Algorithm constructs the multiple data sequences from large to small on each scale. Then, considering the different contribution degrees of multiple data sequences to the entropy of the scale, the proportion of each sequence in the scale sequence is calculated by combining the correlation between the data sequences, so as to reconstruct the sample entropy of each scale. Compared with the traditional multiscale entropy the feature extraction algorithm based on weighted multiple multiscale entropy not only overcomes the problem of information loss, but also fully considers the correlation of sequences and the contribution to total entropy. It reduces the fluctuation between scales, and digs out the details of electroencephalography (EEG). Based on this algorithm, the EEG characteristics of autism spectrum disorder (ASD) children are analyzed, and the classification accuracy of the algorithm is increased by 23.0%, 10.4% and 6.4% as compared with the EEG extraction algorithm of sample entropy, traditional multiscale entropy and multiple multiscale entropy based on the delay value method, respectively. Based on this algorithm, the 19 channel EEG signals of ASD children and healthy children were analyzed. The results showed that the entropy of healthy children was slightly higher than that of the ASD children except the FP2 channel, and the numerical differences of F3, F7, F8, C3 and P3 channels were statistically significant ( <0.05). By classifying the weighted multiple multiscale entropy of each brain region, we found that the accuracy of the anterior temporal lobe (F7, F8) was the highest. It indicated that the anterior temporal lobe can be used as a sensitive brain area for assessing the brain function of ASD children.

Early detection and timely intervention are very essential for autism. This paper used the wavelet transform and empirical mode decomposition (EMD) to extract the features of electroencephalogram (EEG), to compare the feature differences of EEG between the autistic children and healthy children. The experimental subjects included 25 healthy children (aged 5-10 years old) and 25 children with autism (20 boys and 5 girls aged 5-10 years old) respectively. The alpha, beta, theta and delta rhythm wave spectra of the C3, C4, F3, F4, F7, F8, FP1, FP2, O1, O2, P3, P4, T3, T4, T5 and T6 channels were extracted and decomposed by EMD decomposition to obtain the intrinsic modal functions. Finally the support vector machine (SVM) classifier was used to implement assessment of autism and normal classification. The results showed that the accuracy could reach 87% and which was nearly 20% higher than that of the model combining the wavelet transform and sample entropy in the paper. Moreover, the accuracy of delta (1-4 Hz) rhythm wave was the highest among the four kinds of rhythms. And the classification accuracy of the forehead F7 channel, left FP1 channel and T6 channel in the temporal region were all up to 90%, which expressed the characteristics of EEG signals in autistic children better.

Focused on the world-wide issue of improving the accuracy of emotion recognition, this paper proposes an electroencephalogram (EEG) signal feature extraction algorithm based on wavelet packet energy entropy and auto-regressive (AR) model. The auto-regressive process can be approached to EEG signal as much as possible, and provide a wealth of spectral information with few parameters. The wavelet packet entropy reflects the spectral energy distribution of the signal in each frequency band. Combination of them gives a better reflect of the energy characteristics of EEG signals. Feature extraction and fusion are implemented based on kernel principal component analysis. Six emotional states from a public multimodal database for emotion analysis using physiological signals (DEAP) are recognized. The results show that the recognition accuracy of the proposed algorithm is more than 90%, and the highest recognition accuracy is 99.33%. It indicates that this algorithm can extract the feature of EEG emotion well, and it is a kind of effective emotion feature extraction algorithm, providing support to emotion recognition.