标题：Seizure detection in clinical EEG based on multi-feature integration and SVM
作者：Chen, Shanshan ;Meng, Qingfang ;Zhou, Weidong ;Yang, Xinghai
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)
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.