Implementation of ICEEMDAN Decomposition Code in MATLAB

0, Preface

This article explains the ICEEMDAN decomposition method and shares the code.

1. Implementation of ICEEMDAN

The following is the main function part:

ecg=data;%data is a signal data to be decomposed, please replace it with your own data%% parameter settingsNstd = 0.2;nr = 1;MaxIter = 5000;%%ICEEMDAN[modes]=iceemdan(ecg,Nstd,NR,MaxIter,1);%iceemdanmodes=modes';t=1:length(ecg);[a b]=size(modes);figure;subplot(a+1,1,1);plot(t,ecg);% the ECG signal is in the first row of the subplotylabel('original')set(gca,'xtick',[])title('ICEEMDAN')axis tight;for i=2:asubplot(a+1,1,i);plot(t,modes(i-1,:));ylabel (['IMF ' num2str(i-1)]);set(gca,'xtick',[])xlim([1 length(ecg)])endsubplot(a+1,1,a+1)plot(t,modes(a,:))ylabel(['IMF ' num2str(a)])xlim([1 length(ecg)])xlabel('sample point')

The code of the sub-function iceemdan:

function [modes]=iceemdan(x,Nstd,NR,MaxIter,SNRFlag)% The current is an improved version, introduced in:%[1] Colominas MA, Schlotthauer G, Torres ME. "Improve complete ensemble EMD: A suitable tool for biomedical signal processing"% Biomedical Signal Processing and Control vol. 14 pp. 19-29 (2014)%The CEEMDAN algorithm was first introduced at ICASSP 2011, Prague, Czech Republic%The authors will be thankful if the users of this code reference the work%where the algorithm was first presented:%[2] Torres ME, Colominas MA, Schlotthauer G, Flandrin P. "A Complete Ensemble Empirical Mode Decomposition with Adaptive Noise"% Proc. 36th Int. Conf. on Acoustics, Speech and Signa Processing ICASSP 2011 (May 22-27, Prague, Czech Republic)%Author: Marcelo A. Colominas%contact: [email protected]%Last version: 25 feb 2015desvio_x=std(x);x=x/desvio_x;[a,b]=size(x);temp=zeros(b,1);modes=zeros(b,1);aux=zeros(a,b);for i=1:NRwhite_noise{i}=randn(size(x));%creates the noise realizationsend;for i=1:NRmodes_white_noise{i}=emd(white_noise{i},'display',0);%calculates the modes of white gaussian noiseend;% save interval modes_white_noisefor i=1:NR %calculates the first modexi=x+Nstd*modes_white_noise{i}(:,1)'/std(modes_white_noise{i}(:,1));[temp, o, it]=emd(xi,'MaxNumIMF',1,'SiftMaxIterations',MaxIter,'display',0);aux=aux+(xi-temp')/NR;% nnnnnnnnnnnnnnnnJub local envelopeend;modes= (x-aux)'; %saves the first modemedias = aux; %r1k=1;aux=zeros(a,b);es_imf = min(size(emd(medias(1,:),'SiftMaxIterations',MaxIter,'display',0)));while es_imf>1 %calculates the rest of the modesfor i=1:NRtamanio=size(modes_white_noise{i});if tamanio(2)>=k+1noise=modes_white_noise{i}(:,k+1);if SNRFlag == 2noise=noise/std(noise); %adjust the std of the noiseend;noise=Nstd*noise;try[temp,o,it]=emd(medias(1,:)+std(medias(1,:))*noise','MaxNumIMF',1,'SiftMaxIterations',MaxIter,'display',0);catchtemp=emd(medias(1,:)+std(medias(1,:))*noise','MaxNumIMF',1,'SiftMaxIterations',MaxIter,'display',0);end;elsetry[temp, o, it]=emd(medias(1,:),'MaxNumIMF',1,'SiftMaxIterations',MaxIter,'display',0);catchtemp=emd(medias(1,:),'MaxNumIMF',1,'SiftMaxIterations',MaxIter,'display',0);end;end;aux=aux+(medias(1,:)+std(medias(1,:))*noise'-temp')/NR;% r2 r3 r...end;modes=[modes (medias(1,:)-aux)'];medias = aux;aux=zeros(size(x));k=k+1;es_imf = min(size(emd(medias(1,:),'SiftMaxIterations',MaxIter,'display',0)));end;modes = [modes (medias(1,:))'];modes=modes*desvio_x;

The above code can be run directly to achieve signal decomposition.