EEG Epileptic Seizures Separation with Multivariate Empirical Mode Decomposition for Diagnostic Purposes

被引：0
|
作者：
Rutkowski, Tomasz M. [1 ]
Struzik, Zbigniew R. [2 ]
Mandic, Danilo P. [3 ,4 ]
机构：
[1] Univ Tsukuba, Life Sci Ctr TARA, Tsukuba, Ibaraki 305, Japan
[2] Univ Tokyo, RIKEN, Bunkyo, Tokyo, Japan
[3] Imperial Coll London, Kensington, England
[4] RIKEN, Brain Sci Inst, Bunkyo, Tokyo, Japan
来源：
2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2013年
基金：
日本学术振兴会;
关键词：
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
We present a successful application of a soft computing approach based on the multivariate empirical mode decomposition (MEMD) method to EEG epileptic seizures separation. The results of the automatic multivatiate intrinsic mode functions (IMF) clustering allowed us to separate the seizure related spikes and sharp waves. The results of the proposed method have been compared with classical blind separation approach based on ICA, which failed to identify the non-linear and non-stationary signals related to the brain seizures. The proposed method supports epileptic seizure diagnostic methods.
引用
收藏
页码：7128 / 7131
页数：4
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