Multivariate empirical mode decomposition and application to multichannel filtering

Signal Processing

Volumn 91, Issue 12, 2011, Pages 2783-2792

  Fleureau, Julien ^a,b   Kachenoura, Amar ^a,b   Albera, Laurent ^a,b   Nunes, Jean Claude ^a,b   Senhadji, Lotfi ^a,b  

^a INSERM   (France)

^b UNIVERSITÉ DE RENNES 1   (France)

Author keywords

Electroencephalography data analysis; Filter bank structure; Mono and multivariate empirical mode decomposition

Indexed keywords

ELECTROENCEPHALOGRAPHY; ELECTROPHYSIOLOGY; FILTER BANKS;

COMPARATIVE STUDIES; EMPIRICAL MODE DECOMPOSITION; FILTER BANK STRUCTURES; MULTI-CHANNEL FILTERING; MULTIVARIATE EMPIRICAL MODE DECOMPOSITION (MEMD); MULTIVARIATE METHODS; MULTIVARIATE SIGNALS; SIGNAL PROCESSING RESEARCH;

BIOMEDICAL SIGNAL PROCESSING;

EID: 80051473062     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2011.01.018     Document Type: Article

References (21)

1. N. Huang, Z. Shen, S. Long, M. Wu, H. Shih, Q. Zheng, N.-C. Yen, C. Tung, H. Liu, The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. London, Ser. A, 1998.
2. L. Hualou, L. Qiu-Hua, and J. Chen Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease IEEE Trans. Biol. Eng. 52 10 2005 620 623
3. M.A. Arafat, J. Sieed, and Md. Kamrul Hasan Detection of ventricular fibrillation using empirical mode decomposition and Bayes decision theory Comput. Biol. Med. 39 11 2009 1051 1057
4. K.T. Coughlin, and K.K. Tung 11-Year solar cycle in the stratosphere extracted by the empirical mode decomposition method Adv. Space Res. 34 2 2004 323 329 (Pubitemid 38804549)
5. Y. Huang, F. Schmitt, Z. Lu, and Y. Liu Analysis of daily river flow fluctuations using Empirical Mode Decomposition and arbitrary order Hilbert spectral analysis J. Hydrol. 373 12 2009 103 111
6. F. Zhou, M. Xing, X. Bai, G. Sun, and Z. Bao Narrow-band interference suppression for SAR based on complex empirical mode decomposition IEEE Geosci. Remote Sens. Lett. 6 3 2009 423 427
7. N. Chatlani, J. Soraghan, EMD-based Noise Estimation and Tracking (ENET) with application to speech enhancement, in: 17th European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, 2009.
8. N. Bi, Q. Sun, D. Hunag, Z. Yang, and J. Huang Robust image watermarking based on multiband wavelets and empirical mode decomposition IEEE Trans. Image Process. 16 8 2007 1956 1966 (Pubitemid 47225448)
9. E. Delechelle, J. Lemoine, and O. Niang Empirical mode decomposition: an analytical approach for sifting process IEEE Signal Process. Lett. 12 11 2005 764 767 (Pubitemid 41638128)
10. Y. Kopsinis, and S. McLaughlin Investigation and performance enhancement of the empirical mode decomposition method based on a heuristic search optimization approach IEEE Trans. Signal Process. 56 1 2008 1 13
11. S.D. Hawley, L.E. Atlas, and H.J. Chizeck Some properties of an empirical mode type signal decomposition algorithm IEEE Signal Process. Lett. 17 1 2010 24 27
12. Z. Wu, and N.E. Huang A study of the characteristics of white noise using the empirical mode decomposition method Proc. R. Soc. London 460 2004 1597 1611 (Pubitemid 38703409)
13. P. Flandrin, G. Rilling, and P. Goncalves Empirical mode decomposition as a filter bank IEEE Signal Process. Lett. 11 2 2004 112 114
14. T. Tanaka, and D. Mandic Complex empirical mode decomposition IEEE Signal Process. Lett. 14 2 2007 101 104 (Pubitemid 46351330)
15. M. Bin Altaf, M. Gautama, T. Tanaka, D. Mandic, Rotation invariant complex empirical mode decomposition, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP07), Honolulu, Hawai, vol. 3, 2007.
16. G. Rilling, P. Flandrin, P. Goncalves, and J. Lilly Bivariate empirical mode decomposition IEEE Signal Process. Lett. 14 12 2007 1 10
17. N. Ur Rehman, and D.P. Mandic Empirical mode decomposition for trivariate signals IEEE Trans. Signal Process. 58 3 2010 1059 1068
18. N. Ur Rehman, and D.P. Mandic Multivariate empirical mode decomposition Proc. R. Soc. London Ser. A 466 2117 2010 1291 1302
19. A. Kachenoura, J.-C. Nunes, L. Albera, L. Senhadji, 3A-EMD: a generalized approach for monovariate and multivariate EMD, in: 10th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA 10), Kuala Lumpur, Malaysia, vol. 5, 2010, pp. 300303.
20. J. Fleureau, J.-C. Nunes, A. Kachenoura, L. Albera, L. Senhadji, Turning Tangent Empirical Mode Decomposition: a framework for mono and multivariate signals, IEEE Transactions on Signal Processing, doi: 10.1109/TSP.2010.2097254, 2010 (to appear).
21. F. Porée, A. Kachenoura, H. Gauvrit, C. Morvan, G. Carrault, and L. Senhadji Blind source separation for ambulatory sleep recording IEEE Trans. Inf. Technol. Biomed. 10 2 2006 293 301