Commenced in January 2007
Paper Count: 32009
Optimizing Approach for Sifting Process to Solve a Common Type of Empirical Mode Decomposition Mode Mixing
Abstract:Empirical mode decomposition (EMD), a new data-driven of time-series decomposition, has the advantage of supposing that a time series is non-linear or non-stationary, as is implicitly achieved in Fourier decomposition. However, the EMD suffers of mode mixing problem in some cases. The aim of this paper is to present a solution for a common type of signals causing of EMD mode mixing problem, in case a signal suffers of an intermittency. By an artificial example, the solution shows superior performance in terms of cope EMD mode mixing problem comparing with the conventional EMD and Ensemble Empirical Mode decomposition (EEMD). Furthermore, the over-sifting problem is also completely avoided; and computation load is reduced roughly six times compared with EEMD, an ensemble number of 50.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1131231Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 731
 N. E. Huang, Z. Shen, S. R. Long, M. L.Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis,” Proc. Roy. Soc. London A, vol. 454, pp. 903–995, 1998.
 V. D. Calhoun, T. Adali, L. K. Hansen, J. Larsen, and J. J. Pekar, “ICA of functional MRI data: An overview,” in in Proceedings of the International Workshop on Independent Component Analysis and Blind Signal Separation, 2003, pp. 281–288.
 A. Cichocki, S. Amari, K. Siwek, T. Tanaka, and A. H. P. et al., “ICALAB Toolboxes,” 2007. (Online). Available: http: //www.bsp.brain.riken.jp/ICALAB.
 P. Common and C. Jutten, Handbook of Blind Source Separation: Independent Component Analysis and its Applications. Academic Press, 2010.
 S. Al-Baddai, K. Al-Subari, A. Tomé, J. J. Solé-Casals, and E. Lang, “A green’s function-based bi-dimensional empirical mode decomposition,” Information Sciences, vol. 348, pp. 305–321, 2016.
 K. Al-Subari, S. Al-Baddai, A. Tomé, G. Volberg, R. Hammwöhner, and E. Lang, “Ensemble empirical mode decomposition analysis of EEG data collected during a contour integration task,” PLoS ONE, vol. 10, no. 4, p. e0119489, 04 2015.
 K. Al-Subari, S. Al-Baddai, A. Tomé, M. Goldhacker, R. Faltermeier, and E. Lang, “Emdlab:a toolbox for analysis of single-trial eeg dynamics using empirical mode decomposition,” Journal of Neuroscience Methods, vol. 253C, pp. 193–205, 07 2015.
 E. W. Lang, R. Schachtner, D. Lutter, D. Herold, A. Kodewitz, F. Blöchl, F. J. Theis, I. R. Keck, J. M. G. Sáez, P. G. Vilda, and A. M. Tomé, Exploratory Matrix Factorization Techniques for Large Scale Biomedical Data Sets. Bentham Science Publishers, 2010.
 N. Attoh-Okine, K. Barner, D. Bentil, and R. Zhang, “The Empirical Mode Decomposition and the Hilbert-Huang Transform,” EURASIP J. Advances in Signal Processing, 2008.
 Z. Wu and N. E. Huang, “Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method,” Adv. Adaptive Data Analysis, vol. 1(1), pp. 1–41, 2009.
 Z. Wu, N. E. Huang, and X. Chen, “The Multidimensional Ensemble Empirical Mode Decomposition Method,” Adv. Adaptive Data Analysis, vol. 1, pp. 339–372, 2009.
 P. Flandrin, G. Rilling, and P. Goncalves, “Empirical mode decomposition as afilter bank,” Signal Processing Letters, IEEE, vol. 11(2), pp. 112–114, 2004.
 Z. Wu1 and N. E. Huang, “A study of the characteristics of white noise using the empirical mode decomposition method,” in Proceedings of the Royal Society, vol. 460, 2004, pp. 1597–1611.