标题:Seizure detection in clinical EEG based on multi-feature integration and SVM
作者:Chen, Shanshan ;Meng, Qingfang ;Zhou, Weidong ;Yang, Xinghai
通讯作者:Meng, Q
作者机构:[Chen, Shanshan ;Meng, Qingfang ;Yang, Xinghai ] School of Information Science and Engineering, University of Jinan, Jinan 250022, China;[Zhou, Weidon 更多
会议名称:9th International Conference on Intelligent Computing, ICIC 2013
会议日期:28 July 2013 through 31 July 2013
来源:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版年:2013
卷:7996 LNAI
页码:418-426
DOI:10.1007/978-3-642-39482-9_48
关键词:Epileptic EEG; Multi-feature integration; Recurrence quantification analysis (RQA); Support vector machine (SVM)
摘要:Recurrence Quantification Analysis (RQA) was a nonlinear analysis method and widely used to analyze EEG signals. In this work, a feature extraction method based on the RQA measures was proposed to detect the epileptic EEG from EEG recordings. To combine the time-frequency characteristic of epileptic EEG, variation coefficient and fluctuation index were used to analyze epileptic EEG. The multi-feature combination of RQA and linear parameters had better performance in analyzing the nonlinear dynamic characteristics and time-frequency characteristic of epileptic EEG. For features selection and improving the classification accuracy, a support vector machine (SVM) classifier was used. The experimental results showed that the proposed method could classify the ictal EEG and interictal EEG with accuracy of 97.98%. © 2013 Springer-Verlag.
收录类别:EI;SCOPUS
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-s2.0-84883192538&doi=10.1007%2f978-3-642-39482-9_48&partnerID=40&md5=9c19602d394eecf7af49f87ef8c2dbd3
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