基于局部模式的癫痫脑电信号自动分类方法

Automatic Classification Method for Epilepsy EEG Signals Based on Local Pattern

为有效地检测脑电图(EEG)中的癫痫信号,设计一维局部三值模式(1D-LTP)算子提取信号特征,并结合主成分分析(PCA)和极限学习机(ELM)对特征进行分类。通过1D-LTP算子计算信号点的顶层模式和底层模式下的特征变换码以准确滤除干扰信号,并对变换码直方图PCA降维后采用ELM进行分类,以10折交叉验证评估分类性能。实验结果表明,该方法能有效识别在癫痫发作期的EEG信号,其准确率可达99.79%。

In order to effectively detect epileptic signals in Electroencephalogram(EEG),this paper proposes a one-dimensional Local Ternary Pattern(1D-LTP)operator to extract signal features,and the features are classified by combing Principal Component Analysis(PCA)and Extreme Learning Machine(ELM).The 1D-LTP operator is used to calculate the feature-transformation code in the top-level and bottom-level modes of the signal points,so as to accurately filter out the interference signals.Then the histogram of transformation code is dimensionally reduced by PCA and classified by ELM,and the classification performance is evaluated by 10-fold cross validation.Experimental results show that the proposed method can identify EEG signals during seizures,and the recognition accuracy can reach 99.79%.