A complete ensemble empirical mode decomposition with adaptive noise

TLDR

EEMD averages EMD modes over multiple Gaussian white noise realizations to mitigate mode mixing. The paper proposes a complete ensemble empirical mode decomposition algorithm based on EEMD. The algorithm adds a tailored noise at each decomposition stage and computes a unique residue to extract modes, illustrated on a Dirac delta and an ECG signal. The decomposition is complete with negligible error, offers superior spectral separation compared to EEMD, and requires fewer sifting iterations, lowering computational cost.

Abstract

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.