基于局部均值分解和迭代随机森林的脑电分类

为实现癫痫患者的脑电信号有效识别，进而提高患者的生活质量，针对脑电信号的非平稳、非线性特点， 提出一种基于局部均值分解和迭代随机森林相结合的脑电信号分类方法。首先利用局部均值分解将脑电信号 分解成若干个乘积函数分量和一个残余分量，然后对所有分量进行特征提取，并使用支持向量机、随机森林和 迭代随机森林方法进行分类。实验结果表明，迭代随机森林的分类准确率高于支持向量机和随机森林方法。 此方法为准确识别癫痫脑电信号提供了一个可行有效的途径，具有较好的推广和应用价值。

Classification of EEG Signals Using Local Mean Decomposition#br# and Iterative Ｒandom Forest#br#

In order to achieve effective identification of EEG( Electroencephalogram) signals in patients with epilepsy，improve the quality of life for patients，a method of EEG signal classification based on the combination of local mean decomposition and iterative random forest is proposed for the non-stationary and nonlinear characteristics of EEG signals． Firstly，the EEG signal is decomposed into several product function components and a residual component by using local mean decomposition． Then all components are extracted and classified using support vector machine，random forest and iterative random forest methods． The experimental results show that the classification accuracy of iterative random forest is higher than that of support vector machine and random forest method． This method provides a feasible and effective way to accurately identify epileptic EEG signals，and has good application value．