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Bivariate Empirical Mode Decomposition

2007; Institute of Electrical and Electronics Engineers; Volume: 14; Issue: 12 Linguagem: Inglês

10.1109/lsp.2007.904710

1558-2361

Gabriel Rilling, Patrick Flandrin, Paulo Gonçalves, Jonathan M. Lilly,

Blind Source Separation Techniques

The empirical mode decomposition (EMD) has been introduced quite recently to adaptively decompose nonstationary and/or nonlinear time series. The method being initially limited to real-valued time series, we propose here an extension to bivariate (or complex-valued) time series that generalizes the rationale underlying the EMD to the bivariate framework. Where the EMD extracts zero-mean oscillating components, the proposed bivariate extension is designed to extract zero-mean rotating components. The method is illustrated on a real-world signal, and properties of the output components are discussed. Free Matlab/C codes are available at http://perso.ens-lyon.fr/patrick.flandrin.