20/8/2015 https://www.clear.rice.edu/elec301/Projects02/empiricalMode/process.html

https://www.clear.rice.edu/elec301/Projects02/empiricalMode/process.html 1/1

Empirical Mode DecompositionGroup Members: Max Lambert, Andrew Engroff, Matt Dyer, Ben Byer

Navigation: Introduction/Overview Process Application Synthetic vs. Natural Comparison Conclusions MATLAB Code Extras

ProcessThe EMD will break down a signal into its component IMFs.An IMF is a function that:

1. has only one extreme between zero crossings, and2. has a mean value of zero.

In order to describe the process, we borrow from our poster thefollowing section:

The Sifting Process

The sifting process is what EMD uses to decomposes thesignal into IMFs.The sifting process is as follows:For a signal X(t), let m1 be the mean of its upper and lowerenvelopes as determined from a cubic­spline interpolationof local maxima and minima. The locality is determined byan arbitrary parameter; the calculation time and theeffectiveness of the EMD depends greatly on such aparameter.The first component h1 is computed:h1=X(t)­m1In the second sifting process, h1 is treated as the data, andm11 is the mean of h1s upper and lower envelopes:h11=h1­m11This sifting procedure is repeated k times, until h1k is anIMF, that is:h1(k­1)­m1k=h1kThen it is designated as c1=h1k, the first IMF componentfrom the data, which contains the shortest periodcomponent of the signal. We separate it from the rest of thedata: X(t)­c1 = r1 The procedure is repeated on rj: r1­c2 =r2,....,rn­1 ­ cn = rn

The result is a set of functions; the number of functions in the setdepends on the original signal.For a visualization of the sifting process, check out this mpeg we foundwhile looking for information on our topic.