Multivariate empirical mode decomposition and application to multichannel filtering

• Published in: Signal processing Vol. 91; no. 12; pp. 2783 - 2792
• Main Authors: Fleureau, Julien, Kachenoura, Amar, Albera, Laurent, Nunes, Jean-Claude, Senhadji, Lotfi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2011; Elsevier
• Subjects: Algorithms; Bioengineering; Computer Science; Decomposition; Electroencephalography data analysis; Empirical analysis; Engineering Sciences; Filter bank structure; Filter banks; Filtering; Life Sciences; Mathematics; Mono- and multivariate empirical mode decomposition; Multichannel; Recording; Representation Theory; Signal and Image processing; Signal processing; Filter bank structure; Electroencephalography data analysis; Mono- and multivariate empirical mode decomposition
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2011.01.018

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.