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中文摘要: 为了在消除信号中噪声的同时尽可能保留有效信息，提出了一种基于集合经验模态分解(Ensemble Empirical Mode Decomposition, EEMD)和降噪源分离(De-noising Source Separation, DSS)与近似熵(Approximate Entropy, ApEn)相结合的脑电信号消噪方法。利用EEMD分解算法将含噪脑电信号分解为若干个内蕴模态函数(Intrinsic Mode Functions, IMF)分量，滤除最高频分量后的IMF分量应用DSS分离出各独立源信号，再选择频谱近似熵最大的独立源信号作为去噪信号。仿真和真实脑电信号的消噪实验表明，与独立EEMD消噪方法以及基于EEMD与改进提升小波消噪方法相比，本文提出的方法消噪效果更好。
Abstract:To eliminate the noise mixed in the Electroencephalogram (EEG) and retain useful information as much as you can, this paper presents a de-noising method based on Ensemble Empirical Mode Decomposition(EEMD), De-noising Source Separation(DSS) and Approximate Entropy(ApEn). Firstly, the contaminated EEG signals were decomposed into several Intrinsic Mode Function (IMF) components using EEMD. Secondly, several independent source signals were separated using DSS from the IMF components removed the highest frequency component. Then, the independent source signal with maximum spectral approximate entropy was selected as noise signal. the result of simulation EEG and real EEG experiment shows that this method has better de-noising effect compared with the method only adopting EEMD and the method adopting EEMD and Improved Lifting Wavelet.
keywords: EEG signal De-noising Ensemble Empirical Mode Decomposition(EEMD) De-noising Source Separation(DSS) Approximate Entropy(ApEn)
文章编号：cg18000272 中图分类号： 文献标志码：
基金项目:国家自然科学基金项目
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