A Novel Feature Extraction Method for Epilepsy EEG Signals Based on Robust Generalized Synchrony Analysis

摘要：

　　A feature extraction method for Epilepsy diagnosis is proposed in this paper,which can be incorporated in automatic/semi-automatic epilepsy diagnosis systems to improve diagnosis efficiency from multi-channel Electroencephalogram signals.This method calculates the Robust Generalized Synchrony between pairs of Electroencephalogram channels in the first step.Then six character parameters are extracted from the Robust Generalized Synchrony values for the whole brain and the sub-brain regions.A set of Electroencephalogram data including 20 normal objects and 20 epileptic patients in interictal states were used to test the proposed method The results demonstrate that these features are effective to differentiate between epilepsy patients and the normal objects with the p-values smaller than 0.01.

关键词： Electroencephalogram (EEG) Epilepsy Feature Extraction Robust Generalized Synchrony (RGS)

作者: Li Shunan Li Donghui Deng Bin Wei Xile Wang Jiang Wai-Loc Chan

作者单位: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072

会议类型: 国际会议

会议名称: the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)

会议地点: 贵阳

会议语种:英文

页码: 5144-5147

在线出版日期: 2013-05-01（万方平台首次上网日期，不代表论文的发表时间）