摘要
在非线性时间序列预测研究的基础上,提出了基于非线性预测效果的癫痫脑电信号特征提取方法,从脑电信号中自动检测出癫痫脑电信号.采用基于可预测性的选取嵌入维数的方法确定脑电信号序列的嵌入维数,进行相空间重构.实验结果表明:基于非线性预测效果的特征提取方法提取的特征能明显地区分癫痫脑电信号与正常脑电信号,该非线性特征提取方法适合小数据量的情况且对噪声的稳定性好.
Based on the nonlinear time series prediction, a feature extraction method for epileptic EEG signals using nonlinear prediction is proposed to automatically detect the epileptic EEG from EEG recordings. To reconstruct the phase space, the approach of determining the embedding dimension based on nonlinear predictability is used to determine the embedding dimension of the EEG signals. The experimental results show that the feature extracted with the method based on nonlinear prediction could clearly distinguish the epileptic EEG from the normal EEG, and the proposed nonlinear feature extraction method is fit for the small set time series and is stable to noise.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2010年第1期123-130,共8页
Acta Physica Sinica
基金
国家自然科学基金(批准号:60573065)
山东省自然科学基金(批准号:Y2007G31
Y2007G33)
山东省优秀中青年科学家科研奖励基金(批准号:BS2009SW003)
山东省"十一五"重点学科研究方向团体建设(批准号:XTD0708)资助的课题~~
关键词
脑电信号
癫痫
非线性预测
特征提取
EEG signals, epilepsy, nonlinear prediction, feature extraction
