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

作　　者：刘力霈 杨晓利[1] 李振伟[1] LIU Lipei;YANG Xiaoli;LI Zhenwei(College of Medical Technology and Engineering,Henan University of Science and Technology,Luoyang 471023,China)

机构地区：[1]河南科技大学医学技术与工程学院,河南洛阳471023

出　　处：《计算机与现代化》2024年第5期22-26,共5页Computer and Modernization

基　　金：河南省重点研发与推广专项(202102310534)。

摘　　要：癫痫病是最常见的神经系统疾病之一,准确检测癫痫发作对治疗至关重要。为了提高对癫痫脑电信号的自动识别和诊断的准确性,本文设计一种以边为中心构建复杂网络的特征提取方法。该方法首先计算时间序列的Z-score并通过点积运算构造连边时间序列,然后计算Pearson相关系数构造连边矩阵,最后通过网络分析获取特征参数,并选取SVM、K-NN和LR这3种分类器进行对比分类研究。实验结果表明,基于边中心网络特征提取的分类方法取得了较好的效果。其中,LR对癫痫非发作期和发作期的分类效果最佳,准确率达到99.30%。研究结果表明,该方法可有效提取特征信息,为癫痫的临床预警提供新思路。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 constructed by dot product operation.Secondly,the Pearson correlation coefficient was calculated to construct the edge matrix.Finally,the feature parameters are obtained through network analysis,and three classifiers including SVM,K-NN and LR are selected 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 

分 类 号：TN305[电子电信—物理电子学]