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-  2018 

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

DOI: doi:10.7507/1001-5515.201705067

Keywords: 孤独症, 脑电信号, 小波变换, 经验模态分解

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Abstract:

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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