Welcome Research - Interests - The Lab - Cluster Codes Teaching - Undergraduate - Graduate - Other Students Publications Collaboration Contact me... ____________ Last Updated 10-May-2015 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