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  • Title: [Abnormal electroencephalogram features extraction of autistic children based on wavelet transform combined with empirical modal decomposition].
    Author: Li X, Cai E, Qin L, Kang J.
    Journal: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2018 Aug 25; 35(4):524-529. PubMed ID: 30124014.
    Abstract:
    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. 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。.
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