Askari Elham, Setarehdan Seyed Kamaledin, Sheikhani Ali, Mohammadi Mohammad Reza, Teshnehlab Mohammad
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. E-mail:
Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran. E-mail:
J Integr Neurosci. 2018;17(3-4):391-411. doi: 10.3233/JIN-180075.
In neuropsychological disorders, the significant abnormalities in the brain connections in some regions are observed. This paper presents a novel model to demonstrate the connections between different regions in children with autism. The proposed model first conducts the wavelet decomposition of electroencephalography signals by wavelet transform then the features are extracted, such as relative energy and entropy. These features are fed to the 3D-cellular neural network model as inputs to indicate the brain connections. The results showed that there are significant differences and abnormalities in the left hemisphere, (p<0.05) at the electrodes AF3, F3, P7, T7 and O1 in alpha band, AF3, F7, T7 and O1 in beta band, T7 and P7 in gamma band for children with autism compared with the control children. Also, the evaluation of the obtained connections values between brain regions indicated that there are more abnormalities in the connectivity of frontal and parietal lobes and the relations of the neighboring regions in all three bands especially in gamma band for autistic children. Evaluation of the analysis demonstrated that alpha frequency band had the best distinction level of 96.6% based on the obtained values of the cellular neural network using support vector machine method.
在神经心理障碍中，观察到某些区域大脑连接存在显著异常。本文提出了一种新颖的模型来展示自闭症儿童不同区域之间的连接。所提出的模型首先通过小波变换对脑电图信号进行小波分解，然后提取诸如相对能量和熵等特征。这些特征作为输入被馈送到三维细胞神经网络模型中以指示大脑连接。结果表明，与对照儿童相比，自闭症儿童在α波段的电极AF3、F3、P7、T7和O1、β波段的AF3、F7、T7和O1、γ波段的T7和P7处，左半球存在显著差异和异常（p<0.05）。此外，对大脑区域之间获得的连接值的评估表明，自闭症儿童在所有三个波段中，尤其是γ波段，额叶和顶叶的连接以及相邻区域的关系存在更多异常。基于使用支持向量机方法从细胞神经网络获得的值，分析评估表明α频段具有96.6%的最佳区分水平。
