标题:Feature extraction and recognition of ictal EEG using EMD and SVM
作者:Li,S.;Zhou,W.;Yuan,Q.;Geng,S.;Cai,D.
作者机构:[Li, S] School of Information Science and Engineering, Shandong University, Jinan 250100, China;[ Zhou, W] School of Information Science and Engineeri 更多
通讯作者:Zhou, W
通讯作者地址:[Zhou, WD]27 Shanda Rd, Jinan 250100, Peoples R China.
来源:Computers in Biology and Medicine
出版年:2013
卷:43
期:7
页码:807-816
DOI:10.1016/j.compbiomed.2013.04.002
关键词:EEG;EMD;Feature extraction and recognition;Seizure detection;SVM
摘要:Automatic seizure detection is significant for long-term monitoring of epilepsy, as well as for diagnostics and rehabilitation, and can decrease the duration of work required when inspecting the EEG signals. In this study we propose a novel method for feature extraction and pattern recognition of ictal EEG, based upon empirical mode decomposition (EMD) and support vector machine (SVM). First the EEG signal is decomposed into Intrinsic Mode Functions (IMFs) using EMD, and then the coefficient of variation and fluctuation index of IMFs are extracted as features. SVM is then used as the classifier for recognition of ictal EEG. The experimental results show that this algorithm can achieve the sensitivity of 97.00% and specificity of 96.25% for interictal and ictal EEGs, and the sensitivity of 98.00% and specificity of 99.40% for normal and ictal EEGs on Bonn data sets. Besides, the experiment with interictal and ictal EEGs from Qilu Hospital dataset also yields a satisfactory sensitivity of 98.05% and specificity of 100%.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:80
Scopus被引频次:97
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878902261&doi=10.1016%2fj.compbiomed.2013.04.002&partnerID=40&md5=53955222e443785190a9e0c13e65aec8
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