Algorithm 2: EEMD

Input:  The original signal $x$, the amplitude of the added Gaussian noise $\sigma$, and the number of ensemble trials $m$.

Output:  Several IMF and a residue, i.e., $IM{F}_k,\left(k=1,2,\dots ,p\right)$ and $r$.

1:  function EEMD ($x$, $\sigma$, $m$)

2:   IMF$\leftarrow$0

3:   r $\leftarrow 0$

4:   for i = 1 $\to i$ = $m$ do

5:    $n_i\leftarrow N\left(\mathsf{\sigma }\ast \mathrm{std}\left(\mathrm{x}\right),1\right)$

6:     $x_i\leftarrow x+n_i$

7:    EMD ($x_i$)

8:     $r\leftarrow mean\left(r_m\right)$

9:   end for

10:   for k = 1 $\to k$ = p do

11:     $IM{F}_k\leftarrow$ $mean(IM{F}_{i,k}$)

12:   end for

13:    $residue\leftarrow r$

14:   return IMF and residue

15:  end function