基于ICEEMDAN与小波包分解的脉搏信号联合去噪

Joint denoising of pulse signals based on ICEEMDAN and wavelet packet decomposition

针对脉搏信号非线性、非平稳,且难以去噪的问题,提出了一种基于改进的自适应噪声集合经验模态分解(ICEEMDAN)与小波包分解(WPD)相结合的联合去噪方法,对采集的脉搏信号进行去噪处理。首先对噪声信号进行ICEEMDAN模态分解,产生一系列的固有模态函数(IMF),再将这些IMF分量分别与原信号进行相关系数的计算,比较相关系数的值,然后进行信号的重组,最后对重组后的信号进行小波包分解,提取得到降噪后的脉搏信号。利用仿真数据、实际采集的脉搏信号进行实验分析,将该方法与集合经验模态分解(EEMD)进行了对比,并比较了这两种方法的信噪比(SNR)和均方根误差(RMSE)。实验结果表明:基于ICEEMDAN-WPD的联合去噪方法能更有效地去除噪声,并更好地保留脉搏信号的特征。

Aiming at the problem that the pulse signal is nonlinear,non-stationary and difficult to denoise,a joint denoising method based on improved adaptive noise set empirical mode decomposition(ICEEMDAN)and wavelet packet decomposition(WPD)is proposed to denoise the collected pulse signal.Firstly,ICEEMDAN mode decomposition is performed on the noise signal to generate a series of intrinsic mode functions(IMF).Then these IMF components are calculated with the correlation coefficient of the original signal respectively,the value of the correlation coefficient is compared,and then the signal is reconstructed.Finally,the reconstructed signal is decomposed by wavelet packet to extract the denoised pulse signal.The simulation data and the actual pulse signals are used for experimental analysis.The method is compared with the ensemble empirical mode decomposition(EEMD),and the signal-to-noise ratio(SNR)and root mean square error(RMSE)of the two methods are compared.The experimental results show that the joint denoising method based on ICEEMDAN-WPD can remove the noise more effectively and preserve the characteristics of pulse signal better.