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-  2015 

基于改进多元多尺度熵的癫痫脑电信号自动分类

DOI: doi:10.7507/1001-5515.20150047

Keywords: 多元多尺度熵, 癫痫, 嵌入理论, 小波包分解

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Abstract:

传统样本熵很难量化信号本身固有的远程相关性, 虽然多尺度熵能够检测数据内在相关性, 但其多用于单变量信号。多元多尺度熵作为多尺度熵在多元信号上的推广, 是非线性动态相关性的一种反映, 但是传统的多元多尺度熵计算量大, 对于通道数较多的系统需要耗费大量的时间和空间, 并且无法准确地反映变量间的相关性。本文提出的改进的多元多尺度熵, 将传统的多元多尺度熵针对单个变量的嵌入模式改为对所有变量同时嵌入, 不但解决了随着通道数增加内存溢出的问题, 也更适用于实际多变量信号分析。本文方法对仿真数据及波恩癫痫数据进行了试验, 仿真结果表明该方法对相关性数据具有良好的区分性能; 癫痫数据实验表明, 该方法对5个数据集均具有较好的分类精度, 其中对数据集Z、S的分类精度达100%

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