| 基于小波系数特征融合的小鼠癫痫脑电分类 |
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| 引用本文： | 肖文卿,汪鸿浩,詹长安.基于小波系数特征融合的小鼠癫痫脑电分类[J].计算机工程与应用,2019,55(14):155-161. |
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| 作者姓名： | 肖文卿 汪鸿浩 詹长安 |
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| 作者单位： | 南方医科大学 生物医学工程学院,广州 510515;广州南方医大医疗设备综合检测有限责任公司,广州 510515;南方医科大学附属南方医院,广州,510515;南方医科大学 生物医学工程学院,广州,510515 |
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| 基金项目： | 国家自然科学基金;广州市高校创新创业教育项目;广州市科技计划 |
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| 摘 要： | 采集癫痫小鼠模型在常态与致癫状态下的脑电信号以研究其癫痫脑电的自动分类。对经过噪声和伪迹消除预处理的脑电信号进行小波变换，获得不同频率子带的小波系数，对脑电信号及与癫痫特征波相关的小波系数提取相应的线性特征（标准差）和非线性特征（样本熵）；基于这些特征及其组合使用支持向量机分类器实现分类。实验发现基于小鼠脑电本身的标准差和样本熵的分类正确率分别为59.10%和58.00%；而融合各相关小波系数的标准差或样本熵，分类正确率分别达到86.60%和88.60%；融合全部相关小波系数的线性和非线性特征后分类正确率为99.80%。这些结果说明基于小波系数特征融合的分类算法性能有显著提升，能有效实现小鼠癫痫脑电的自动分类。
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| 关 键 词： | 癫痫小鼠模型 小波变换 特征融合 支持向量机 |
Wavelet Coefficient Feature Fusion Based Classification of Mice Epileptic EEG |
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| XIAO Wenqing,WANG Honghao,ZHAN Chang’an.Wavelet Coefficient Feature Fusion Based Classification of Mice Epileptic EEG[J].Computer Engineering and Applications,2019,55(14):155-161. |
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| Authors: | XIAO Wenqing WANG Honghao ZHAN Chang’an |
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| Affiliation: | 1.School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
2.Nanfang Hospital of Southern Medical University, Guangzhou 510515, China
3.S.M.U. Medical Equipment Test Co., Ltd., Guangzhou 510515, China |
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| Abstract: | The Electroencephalogram（EEG） of mouse model of epilepsy in normal and epileptic status is collected to study the automatic classification of epileptic EEG. The noise- and artifact-attenuated EEG is wavelet-transformed, and the linear feature（standard deviation） and the nonlinear feature（sample entropy） are then extracted for the EEG signals and those wavelet coefficients related to the characteristic waveforms of epileptic EEG. Classification is implemented using support vector machine with above individual features and their combinations. The classification accuracy based on the standard deviation and sample entropy of EEG signals are 59.1% and 58.00%, respectively.The accuracy increases to 86.60% or 88.60%, when the standard deviations or sample entropies of relevant wavelet coefficients are used as input features. After combining both types of features, the classification accuracy is 99.80%. These results show that wavelet coefficient features fusion significantly improves the classification accuracy, achieving effective classification of mouse epileptic EEG. |
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| Keywords: | epileptic mice model wavelet transform feature fusion support vector machine |
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