Research on Epilepsy EEG Classification Based on Time Series Complex Network
YANG Xiaoli
YANG Bin
LI Zhenwei
WU Xiaoqin
Abstract：The brain is a highly complex system,and the EEG signal has a strong noise background and weak signal.The tra-ditional EEG signal feature extraction method cannot fully reflect the feature information of the EEG signal.Therefore,an epilepsy EEG classification method combining with complex network theory and constructing complex networks based on time series is pro-posed.First,the time series of epilepsy EEG signals is processed in segments,and each segment is used as a node in the network.The relationship between nodes through Pearson correlation is calculated to construct the connection matrix of the network,and then the network feature parameters is calculated through the connection matrix,and statistical analysis on the feature parameters is per-formed to construct the feature vector.Finally,classifiers such as SVM,logistic regression and K-NN are used for classification re-search.The results show that the classification accuracy of this method for data sets A-E,AB-CDE and ABCD-E reached 96.67％,94.00％ and 94.33％,respectively.Experimental results show that,as an alternative to traditional time and frequency analysis,this method can be used for pattern recognition and classification of EEG signals,and can effectively classify and recognize epileptic EEG signals.
Keywords：complex networktime seriesepilepsyEEGclassification
Publication Date：2023-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 2814-2820 )