Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition
Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition
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DOI：
10.1007/s11265-009-0447-z
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发表时间：
2011-08-01
影响因子：
1.8
通讯作者：
Liu, Shing-Hong
中科院分区：
文献类型：
--
作者：
Chang, Kang-Ming;Liu, Shing-Hong
经验模态分解（EMD）是一种基于信号复杂度将信号分解为一组固有模态函数（IMF）的强大算法。在本研究中，部分重建的IMF作为滤波器用于心电信号的降噪。一种改进的算法，集成EMD (EEMD)，首次用于提高噪声滤波性能，基于近IMF尺度之间的模式混合减少。心电信号采用仿真器标准心电模板和心律失常心电数据库作为心电信号，噪声源采用高斯白噪声。以重构心电与原始心电的均方误差（MSE）作为滤波性能指标。采用FIR维纳滤波器与EEMD进行滤波性能比较。实验结果表明，EEMD比EMD和FIR维纳滤波器具有更好的噪声滤波性能。EEMD与EMD和FIR维纳滤波器的平均MSE比分别为0.71和0.61。因此，本研究探讨了一种基于EEMD的心电噪声滤波方法。并对EEMD的最优附加噪声功率和试验次数进行了分析。
Empirical mode decomposition (EMD) is a powerful algorithm that decomposes signals as a set of intrinsic mode function (IMF) based on the signal complexity. In this study, partial reconstruction of IMF acting as a filter was used for noise reduction in ECG. An improved algorithm, ensemble EMD (EEMD), was used for the first time to improve the noise-filtering performance, based on the mode-mixing reduction between near IMF scales. Both standard ECG templates derived from simulator and Arrhythmia ECG database were used as ECG signal, while Gaussian white noise was used as noise source. Mean square error (MSE) between the reconstructed ECG and original ECG was used as the filter performance indicator. FIR Wiener filter was also used to compare the filtering performance with EEMD. Experimental result showed that EEMD had better noise-filtering performance than EMD and FIR Wiener filter. The average MSE ratios of EEMD to EMD and FIR Wiener filter were 0.71 and 0.61, respectively. Thus, this study investigated an ECG noise-filtering procedure based on EEMD. Also, the optimal added noise power and trial number for EEMD was also examined.