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Last Updated
07-Jun-2011

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.

 

 

 

 
     
 
 
     
 

Applied math Education