EEG Classification of Epilepsy Based on Edge-center Network Feature Extraction
Epilepsy is one of the most common neurological diseases,and accurate seizure detection is crucial for treatment.In order to improve the accuracy of automatic identification and diagnosis of epileptic EEG signals,we design an edge-centered method to construct complex networks.Firstly,the Z-score value of the series was calculated,and the edge time series was con-structed by dot product operation.Secondly,the Pearson correlation coefficient was calculated to construct the edge matrix.Fi-nally,the feature parameters are obtained through network analysis,and three classifiers including SVM,K-NN and LR are se-lected for comparative classification research.The experimental results show that the classification method based on edge center network feature extraction has achieved good results.Among them,LR has the best classification effect for non-ictal and ictal epilepsy,with an accuracy of 99.30%.The results show that the proposed method can effectively extract feature information and provide new ideas for clinical early warning of epilepsy.
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