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基于非线性预测效果的癫痫脑电信号的特征提取方法
The feature extraction of epileptic EEG signals based on nonlinear prediction
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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.Abstract: 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.
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