Li Xin, Cai Erjuan, Qin Luyun, Kang Jiannan
Institute of Biomedical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China;Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P.R.China;College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124,
Institute of Biomedical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China;Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018 Aug 25;35(4):524-529. doi: 10.7507/1001-5515.201705067.
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.
早期发现和及时干预对自闭症非常重要。本文采用小波变换和经验模态分解（EMD）提取脑电图（EEG）特征，比较自闭症儿童与健康儿童EEG的特征差异。实验对象分别包括25名健康儿童（5 - 10岁）和25名自闭症儿童（20名男孩和5名女孩，5 - 10岁）。提取C3、C4、F3、F4、F7、F8、FP1、FP2、O1、O2、P3、P4、T3、T4、T5和T6通道的α、β、θ和δ节律波谱，并通过EMD分解进行分解以获得本征模态函数。最后使用支持向量机（SVM）分类器实现自闭症评估和正常分类。结果表明，准确率可达87%，比文中结合小波变换和样本熵的模型高出近20%。此外，δ（1 - 4Hz）节律波的准确率在四种节律中最高。颞区的前额F7通道、左FP1通道和T6通道的分类准确率均高达90%，能更好地体现自闭症儿童EEG信号的特征。
