Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICASSP.2011.5947265 | ICASSP |
Keywords | Field | DocType |
Gaussian noise,adaptive signal processing,white noise,Gaussian white noise,adaptive noise,discrete Dirac delta function,electrocardiogram signal,ensemble empirical mode decomposition,mode averaging,mode mixing problem,sifting iterations,spectral separation,Biomedical Signal Processing,Empirical Mode Decomposition,Heart Rate Variability | Oscillation,White noise,Dirac delta function,Adaptive filter,Artificial intelligence,Spectral separation,Pattern recognition,Signal-to-noise ratio,Algorithm,Speech recognition,Gaussian noise,Mathematics,Hilbert–Huang transform | Conference |
ISSN | Citations | PageRank |
1520-6149 | 81 | 4.32 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| María Eugenia Torres | 1 | 183 | 12.23 |
| Marcelo A. Colominas | 2 | 161 | 13.50 |
| Gastón Schlotthauer | 3 | 180 | 15.59 |
| Patrick Flandrin | 4 | 2307 | 568.82 |