计算机与现代化Issue(5)：22-26,5.DOI:10.3969/j.issn.1006-2475.2024.05.005
基于边中心网络特征提取的癫痫脑电分类研究
EEG Classification of Epilepsy Based on Edge-center Network Feature Extraction
摘要
Abstract
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.关键词
癫痫/分类/复杂网络/特征提取/连边矩阵Key words
epilepsy/classification/complex network/feature extraction/connected edge matrix分类
信息技术与安全科学引用本文复制引用
刘力霈,杨晓利,李振伟..基于边中心网络特征提取的癫痫脑电分类研究[J].计算机与现代化,2024,(5):22-26,5.基金项目
河南省重点研发与推广专项(202102310534) （202102310534）
