A complete ensemble empirical mode decomposition with adaptive noise

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 m...

Full description

Saved in:
Bibliographic Details
Published in2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 4144 - 4147
Main Authors Torres, Maria E., Colominas, Marcelo A., Schlotthauer, Gaston, Flandrin, Patrick
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2011
Subjects
Biomedical Signal Processing
Computational efficiency
Electrocardiography
Empirical Mode Decomposition
Heart Rate Variability
Oscillators
Signal processing algorithms
Signal to noise ratio
White noise
Online AccessGet full text

Cover

More Information
Summary: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.
ISBN:9781457705380
1457705389
ISSN:1520-6149
DOI:10.1109/ICASSP.2011.5947265