A complete ensemble empirical mode decomposition with adaptive noise
A complete ensemble empirical mode decomposition with adaptive noise
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DOI：
10.1109/icassp.2011.5947265
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发表时间：
2011-05
期刊：
影响因子：
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通讯作者：
M. E. Torres;M. A. Colominas;G. Schlotthauer;P. Flandrin
中科院分区：
文献类型：
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作者：
M. E. Torres;M. A. Colominas;G. Schlotthauer;P. Flandrin
本文提出了一种基于集成经验模式分解（EEMD）的信号处理算法。EEMD的关键思想依赖于平均的模式所获得的EMD应用到高斯白色噪声添加到原始信号的几个实现。所得到的分解解决了EMD模式混合问题，但是它引入了新的问题。在这里提出的方法中，在分解的每个阶段添加特定的噪声，并计算唯一的残差以获得每个模式。由此产生的分解是完整的，具有数值上可忽略的误差。两个例子：离散狄拉克δ函数和心电图信号。结果表明，与EEMD方法相比，该方法能更好地分离模态谱，且迭代次数少，计算量小。
In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is presented. The key idea on the EEMD relies on averaging the modes obtained by EMD applied to several realizations of Gaussian white noise added to the original signal. The resulting decomposition solves the EMD mode mixing problem, however it introduces new ones. In the method here proposed, a particular noise is added at each stage of the decomposition and a unique residue is computed to obtain each mode. The resulting decomposition is complete, with a numerically negligible error. Two examples are presented: a discrete Dirac delta function and an electrocardiogram signal. The results show that, compared with EEMD, the new method here presented also provides a better spectral separation of the modes and a lesser number of sifting iterations is needed, reducing the computational cost.