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Página creada y mantenida por
M. E. Torres


My Research Interests


Biomedical Signal Processing


Electroencephalograms, Electroencephalogram and Speech signals


Voice:  Pathologies, emotions, singing


Hearing Aids

  Chaos and complexity  
  Fractals, self similarity, LRD  
  Time - scale/frequency analysis - Wavelet Analysis  
  Empirical Mode Decomposition (EMD)  
  Advanced signal analysis



Our Matlab Codes

  Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)

Matlab code - rar file (download)

Reference paper: (http://www.cmsworldwide.com/ICASSP2011/Papers/ViewPapers.asp?PaperNum=3385 )

M.E.Torres, M.A. 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). (pdf)

Bibref (download)

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.




  Improved Complete Ensemble EMD (CEEMDAN_v2014)

Matlab code - rar file (download)

Reference paper: (http://www.sciencedirect.com/science/article/pii/S1746809414000962 )

Marcelo A. Colominas, Gastón Schlotthauer, María E. Torres "Improved Complete Ensemble EMD: a suitable tool for biomedical signal processing" Biomedical Signal Processing and Control, Volume 14, November 2014, Pages 19–29- DOI:10.1016/j.bspc.2014.06.009 .

Bibref (bspc_2014_06_009.bib)

Abstract: The empirical mode decomposition (EMD) decomposes non-stationary signals that may stem from nonlinear systems, in a local and fully data-driven manner. Noise-assisted versions have been proposed to alleviate the so-called “mode mixing” phenomenon, which may appear when real signals are analyzed. Among them, the complete ensemble EMD with adaptive noise (CEEMDAN) recovered the completeness property of EMD. In this work we present improvements on this last technique, obtaining components with less noise and more physical meaning. Artificial signals are analyzed to illustrate the capabilities of the new method. Finally, several real biomedical signals are decomposed, obtaining components that represent physiological phenomenons..




Applied math Education