Multivariate empirical mode decomposition and application to multichannel filtering

Abstract : Empirical Mode Decomposition (EMD) is an emerging topic in signal processing research, applied in various practical fields due in particular to its data-driven filter bank properties. In this paper, a novel EMD approach called X-EMD (eXtended-EMD) is proposed, which allows for a straightforward decomposition of mono- and multivariate signals without any change in the core of the algorithm. Qualitative results illustrate the good behavior of the proposed algorithm whatever the signal dimension is. Moreover, a comparative study of X-EMD with classical mono- and multivariate methods is presented and shows its competitiveness. Besides, we show that X-EMD extends the filter bank properties enjoyed by monovariate EMD to the case of multivariate EMD. Finally, a practical application on multichannel sleep recording is presented.
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Julien Fleureau, Amar Kachenoura, Laurent Albera, Jean-Claude Nunes, Lotfi Senhadji. Multivariate empirical mode decomposition and application to multichannel filtering. Signal Processing, Elsevier, 2011, 91 (12), pp.2783-2792. 〈10.1016/j.sigpro.2011.01.018〉. 〈inserm-00588301〉

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