基于小波变换结合经验模态分解提取孤独症儿童脑电异常特征研究

Abnormal electroencephalogram features extraction of autistic children based on wavelet transform combined with empirical modal decomposition

孤独症的早期发现与及时干预至关重要。本文结合小波变换和经验模态分解(EMD)提取脑电信号(EEG)特征,比较分析孤独症儿童和正常儿童脑电信号的特征差异。试验共采集了25例(20例男孩,5例女孩)5~10岁孤独症儿童和25例5~10岁正常儿童的脑电信号,基于小波变换提取C3、C4、F3、F4、F7、F8、FP1、FP2、O1、O2、P3、P4、T3、T4、T5和T6的alpha、beta、theta和delta频段的节律波,再进行EMD分解得到固有模态函数(IMF)特征,以支持向量机(SVM)实现孤独症和正常儿童脑电的分类评估。试验结果表明,小波变换和EMD结合的方法可以有效地识别孤独症儿童和正常儿童的脑电信号特征,分类正确率达到87%,相比文中小波结合样本熵方法提取脑电特征分类评估的准确率高出将近20%。所提取的四种节律波中,delta节律(1~4 Hz)波的分类正确率最高,特别是在前额F7通道、左前额FP1通道和颞区T6通道其分类准确率均超过90%,能够较好地表达孤独症儿童脑电信号的特点。

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.