Producción CyT
Congreso
Autoría
María Eugenia Torres
;
Marcelo Alejandro Colominas
;
SCHLOTTHAUER, GASTON
;
Patrick Flandrin
Fecha
2011
Editorial y Lugar de Edición
IEEE
ISSN
978-1-4577-0539-7
Resumen
Información suministrada por el agente en
SIGEVA
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. T...
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.
Ver más
Ver menos
Palabras Clave
Biomedical Signal ProcessingAdaptive Signal ProcessingEmpirical Mode DecompositionHeart Rate Variability