基于FSWT和GBDT的癫痫脑电信号分类研究

为解决癫痫脑电信号分类类别以及分类精度不足的问题，使用频率切片小波变换对脑电数据进行信号重构，得到5 个频段的节律信号，再利用非线性指标近似熵和线性指标波动指数共同作为癫痫信号的特征值，充分提取信号的特征信息。随后使用梯度提升树算法对得到的特征数据集进行多分类。实验表明，该算法对癫痫脑电信号的三分类识别率为98． 4%。较传统Adaboost 算法，该方法采取了GBDT( Gradient Boosting Decision Tree) 作为分类算法，成功利用更多的数据集，并且使得分类精度更高。

Classification of Epileptic EEG Signals Based on Frequency Slice Wavelet Transform and Gradient Boosting Decision Tree#br#

In order to solve the problem of classification and accuracy of epilepsy EEG( Electroencephalogram)signals，frequency slice wavelet transform was used to reconstruct EEG data and get five frequency bands of rhythmic signals． We use approximate entropy of non-linear index and fluctuation index of linear index as the eigenvalues of epileptic signals to fully extract the characteristic information of signals． Gradient lifting tree algorithm was used to classify the feature data set． The classification recognition rate of epileptic EEG signals is 98． 4%． Compared to the traditional Adaboost algorithm，we adopt GBDT( Gradient Boosting Decision Tree) as a classification algorithm． This method can use more data sets successfully and has higher classification accuracy．