Applications of empirical mode decomposition for processing nonstationary signals

Empirical mode decomposition (EMD) has recently been pioneered by Huang as a fully data-driven technique aimed at decomposing nonstationary signals in a set of “Intrinsic mode functions” (IMFs, Empirical modes). We will report on the main theoretical aspects of EMD, its extensive possibilities, and various contemporary applications. We will pay attention to detrending; denoising; Hilbert-Huang time-frequency analysis; and a very perspective and actual scientific direction known as Data Mining, which involves such problems as segmentation, cluster-analysis (clustering), classification, etc.