Algorithm 1.

Ensemble Empirical Mode Decomposition

Step 1	Define a noise signal Yt
Step 2	Obtain Xt = Yt + ng; (with adding white Gaussian noise ng)
Step 3	Identify local extrema for the time series Yt
Step 4	To form the upper emax(t) and lower envelop emin(t), aggregate all local maxima and all local minima with cubic splines interpolation
Step 5	Compute mean envelop mi(t) = [emax(t)+ emin(t)]/2
Step 6	Calculate the difference hi(t) = Xt - mi(t)
Step 7	Check hi(t)satisfy the stopping criteria condition (IMF is symmetric with zero mean and have the same number of zero-crossing), if hi(t) satisfy then h1(t) = IMF1(t), otherwise hi(t) replicate step 1 to step 4 until stopping criteria met.
Step 8	Compute the residual rt = Xt – IMFi(t)
Step 9	Consider rt is a new time series yt until getting a constant residual rn[t].