计算机工程 ›› 2019, Vol. 45 ›› Issue (7): 291-295,302.doi: 10.19678/j.issn.1000-3428.0051201

• 开发研究与工程应用 • 上一篇    下一篇

基于相位同步的癫痫信号识别与分析

周梦妮a, 牛焱a, 曹锐b, 阎鹏飞a, 相洁a   

  1. 太原理工大学 a. 信息与计算机学院;b. 软件学院, 太原 030024
  • 收稿日期:2018-04-13 修回日期:2018-06-29 出版日期:2019-07-15 发布日期:2019-07-23
  • 作者简介:周梦妮(1992-),女,硕士研究生,主研方向为智能信息处理;牛焱,博士研究生;曹锐,博士;阎鹏飞,讲师、博士;相洁(通信作者),教授、博士、博士生导师。
  • 基金项目:
    国家自然科学基金(61503272,61305142,61373101,61741212);中国博士后科学基金(2016M601287);山西省自然科学基金青年项目(2015021090,201601D202042);山西省回国留学人员科研项目(2016-037)。

Recognition and Analysis of Epileptic Signal Based on Phase Synchronization

ZHOU Mengnia, NIU Yana, CAO Ruib, YAN Pengfeia, XIANG Jiea   

  1. a. College of Information and Computer;b. College of Software, Taiyuan University of Technology, Taiyuan 030024, China
  • Received:2018-04-13 Revised:2018-06-29 Online:2019-07-15 Published:2019-07-23

摘要: 针对临床人工诊断癫痫信号效率低下的问题,建立一种基于相位同步的癫痫信号自动诊断模型。使用相位锁定值衡量各脑区间不同状态下的同步化程度,构建对应的脑功能网络连接矩阵,提取聚类系数和特征路径长度2种全局属性作为输入支持向量机的训练特征,使用六折交叉验证的方式对发作间期及发作期的信号进行分类识别。实验结果表明,加权网络的分类效果优于二值网络,其平均准确率为83.4%,单一属性难以全面反映癫痫患者2种状态下的功能网络连接差异,多数患者在gamma和beta频段取得较好的分类效果。

关键词: 癫痫, 相位锁定值, 同步, 聚类系数, 特征路径长度, 支持向量机

Abstract: To address the low efficiency of clinical manual diagnosis of epilepsy,this paper establishes an automatic diagnosis model of epilepsy signal based on phase synchronization.First,the model use Phase Locking Value (PLV) to measure the degree of synchronization of brain regions in different states and constructs a corresponding brain function network connection matrix.Then,the two global attributes,clustering coefficient and characteristic path length,are extracted as training features to input onto Support Vector Machine (SVM).Finally,6-fold cross-validation method is used for the classification and identification of interictal and ictal signals.Experimental results show that the classification effect of the weighted network is better than that of the binary network.The average accuracy of the weighted network is 83.4%.Single attribute is not enough to fully reflect the difference in functional network connections in two states of epilepsy,and most patients achieve better classification results in gamma and beta bands.

Key words: epilepsy, Phase Locking Value(PLV), synchronization, clustering coefficient, characteristic path length, Support Vector Machine(SVM)

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