标题:The Feature Extraction Method of EEG Signals Based on Degree Distribution of Complex Networks from Nonlinear Time Series
作者:Wang, Fenglin; Meng, Qingfang; Zhou, Weidong; Chen, Shanshan
通讯作者:Meng, Q
作者机构:[Wang, Fenglin; Meng, Qingfang; Chen, Shanshan] Univ Jinan, Sch Informat Sci & Engn, Jinan 250022, Peoples R China.; [Wang, Fenglin; Meng, Qingfang; 更多
会议名称:9th International Conference on Intelligent Computing (ICIC)
会议日期:JUL 28-31, 2013
来源:INTELLIGENT COMPUTING THEORIES
出版年:2013
卷:7995
页码:354-361
DOI:10.1007/978-3-642-39479-9_42
关键词:EEG; Epilepsy; Seizure detection; Complex networks; Degree distribution;; Shannon entropy
摘要:The nonlinear time series analysis method based on complex networks theory gives a novel perspective to understand the dynamics of the nonlinear time series. Considering the electroencephalogram (EEG) signals showing different nonlinear dynamics under different brain states, this study proposes an epileptic EEG analysis approach based on statistical properties of complex networks and applies the approach to epileptic EEGs automatic detection. Firstly, the complex network is constructed from the epileptic EEG signals and the degree distribution (DDF) of the resulting networks is calculated. Then the entropy of the degree distribution (NDDE) is used as a feature to classify the ictal EEGs and the interictal EEGs. The experiment results show that the NDDE of the ictal EEG is lower than interictal EEG's and the classification accuracy, taking the NDDE as a classification feature, is up to 96.25%.
收录类别:CPCI-S;EI;SCOPUS
WOS核心被引频次:1
Scopus被引频次:3
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84882794567&doi=10.1007%2f978-3-642-39479-9_42&partnerID=40&md5=93c4adba60531472f01d1025aac9823d
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