Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)
Matlab code -
rar file (download)
Colominas, G. Schlotthauer, P. Flandrin, "A complete Ensemble
Empirical Mode decomposition with adaptive noise," IEEE Int. Conf. on
Acoust., Speech and Signal Proc. ICASSP-11, pp. 4144-4147, Prague (CZ).
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.