联合改进CEEMD与近似熵的脑电去噪方法

Electroencephalogram Denoising Method Combining Improved CEEMD and Approximate Entropy

针对现有完备总体经验模态分解方法在脑电去噪中的模态筛选偏差问题,结合改进的完备总体经验模态分解(ICEEMD)与近似熵,提出一种新的脑电(EEG)信号去噪方法。对EEG信号进行ICEEMD分解,得到一系列本征模态函数(IMF),再对IMF分别计算近似熵,比较并选择近似熵值最大的IMF作为去噪后的信号。基于模拟信号和真实脑电信号的实验结果表明,与添加自适应噪声的完备总体经验模态分解方法相比,该方法能得到更清晰稳定的去噪结果,并且解决了IMF盲目选取导致的去噪失准及虚假模态等问题。

Aiming at the problem of modal selection bias in Complete Ensemble Empirical Mode t）ecomposluon （CEEMD） ,this paper proposes a new Electroencephalogram （EEG） signal denoising method by combining improved CEEMD （ICEEMD）. First, the EEG signal is decomposed to several Intrinsic Mode Functions （IMF） by ICEEMD. Then, the approximate entropy of each IMF is calculated respectively. Finally,the IMF with the maximum approximate entropy is chosen as the denoised result. The experiments result based on analog signals and real EEG signals shows that, compared with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise（ECCMDAN）, the new method can give more clear and stable denoising results, and it also solves the problems such as inaccurate denoising and false mode caused by the blind selection of IMF.