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graph_plots_c2f.m
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graph_plots_c2f.m
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%% load data and generate segments
clc
clear
cd("C:\Users\vlsi\Desktop\harit\mutliscale\original_data")
fs=250;
segLen=25;
k=1;
for j=1:1
if(j<10)
subject=strcat('h0',num2str(j),'.edf');
else
subject=strcat('h',num2str(j),'.edf');
end
[hdr, full_eeg_cont] = edfread((subject));
SegNum=size(full_eeg_cont,2)/(fs*segLen);
for i=1:SegNum
Class_Helthy{k,1}=full_eeg_cont(:,segLen*fs*(i-1)+1:segLen*fs*i);
k=k+1;
end
end
%% plot power spectral density
% subplot(2,1,1)
% plot(full_eeg_cont(3,1:1*fs),LineWidth=1.5,Color='black',lineStyle='-', DisplayName="T4");
% hold on
% plot(full_eeg_cont(11,1:1*fs),LineWidth=1.5,Color='black',lineStyle='--',DisplayName="F4");
% plot(full_eeg_cont(12,1:1*fs),LineWidth=1.5,color='black',lineStyle=':',DisplayName="C4");
% xlabel("Time(sec)")
% ylabel("Amplitude")
% hold off
%
% subplot(2,1,2)
% [Pxx1,F1] = periodogram(full_eeg_cont(3,1:1*fs),[],length(full_eeg_cont(3,1:1*fs)),fs);
% plot(F1,10*log10(Pxx1),LineWidth=1.5,Color='black',lineStyle='-', DisplayName="T4")
% %plot(full_eeg_cont(3,1:1*fs),LineWidth=1.5,Color='black',lineStyle='-', DisplayName="T4");
% hold on
% [Pxx2,F2] = periodogram(full_eeg_cont(11,1:1*fs),[],length(full_eeg_cont(11,1:1*fs)),fs);
% plot(F2,10*log10(Pxx2),LineWidth=1.5,Color='black',lineStyle='--', DisplayName="F4")
% %plot(full_eeg_cont(11,1:1*fs),LineWidth=1.5,Color='black',lineStyle='--',DisplayName="F4");
% [Pxx3,F3] = periodogram(full_eeg_cont(12,1:1*fs),[],length(full_eeg_cont(12,1:1*fs)),fs);
% plot(F3,10*log10(Pxx3),LineWidth=1.5,Color='black',lineStyle=':', DisplayName="C4")
% %plot(full_eeg_cont(12,1:1*fs),LineWidth=1.5,color='black',lineStyle=':',DisplayName="C4");
% xlabel("Frequency(Hz)")
% ylabel("Power(dB)")
% hold off
%% generate mvmd decomposition
no_of_modes = 10;
dur=segLen;
for i=1:1
x=Class_Helthy{i,1};
tic
%clear u u_hat omega u1
[u, u_hat, omega] = MVMD_ver1(x, 1000, 0, no_of_modes, 0, 0, 1e-7);
toc
end
%% calculate imf's
u1(:,:,:)=permute(u,[3 1 2]);
imf_ch1(:,:)=u1(1,1:no_of_modes,:);
imf_ch2(:,:)=u1(2,1:no_of_modes,:);
imf_ch3(:,:)=u1(3,1:no_of_modes,:);
imf_ch4(:,:)=u1(4,1:no_of_modes,:);
imf_ch5(:,:)=u1(5,1:no_of_modes,:);
imf_ch6(:,:)=u1(6,1:no_of_modes,:);
imf_ch7(:,:)=u1(7,1:no_of_modes,:);
imf_ch8(:,:)=u1(8,1:no_of_modes,:);
imf_ch9(:,:)=u1(9,1:no_of_modes,:);
imf_ch10(:,:)=u1(10,1:no_of_modes,:);
imf_ch11(:,:)=u1(11,1:no_of_modes,:);
imf_ch12(:,:)=u1(12,1:no_of_modes,:);
imf_ch13(:,:)=u1(13,1:no_of_modes,:);
imf_ch14(:,:)=u1(14,1:no_of_modes,:);
imf_ch15(:,:)=u1(15,1:no_of_modes,:);
imf_ch16(:,:)=u1(16,1:no_of_modes,:);
imf_ch17(:,:)=u1(17,1:no_of_modes,:);
imf_ch18(:,:)=u1(18,1:no_of_modes,:);
imf_ch19(:,:)=u1(19,1:no_of_modes,:);
%% plot amplitude and magnitude graphs
% fig = figure;
% for i1=1:no_of_modes
% subplot(10,1,i1)
% plot(imf_ch2(i1,1:500),LineWidth=1);
% %plot(abs(fft(hilbert(imf_ch2(i1,1:500)))),LineWidth=1);
% ylabel(strcat('IMF',num2str(i1)),'fontweight','bold','fontsize',10);
%
% end
% han=axes(fig,'visible','off');
% han.XLabel.Visible='on';
% han.YLabel.Visible='on';
% ylabel(han,'Amplitude','fontweight','bold','fontsize',16);
% xlabel(han,'Time(samples)','fontweight','bold','fontsize',16);
%title(han,'yourTitle');
%%
%%
[m_1,n_1]=size(imf_ch1);
x_1=zeros(1,n_1);
x_2=zeros(1,n_1);
x_3=zeros(1,n_1);
x_4=zeros(1,n_1);
x_5=zeros(1,n_1);
x_6=zeros(1,n_1);
x_7=zeros(1,n_1);
x_8=zeros(1,n_1);
x_9=zeros(1,n_1);
x_10=zeros(1,n_1);
x_11=zeros(1,n_1);
x_12=zeros(1,n_1);
x_13=zeros(1,n_1);
x_14=zeros(1,n_1);
x_15=zeros(1,n_1);
x_16=zeros(1,n_1);
x_17=zeros(1,n_1);
x_18=zeros(1,n_1);
x_19=zeros(1,n_1);
%%
apn = zeros(10,19);
sham = zeros(10,19);
perm = zeros(10,19);
ren = zeros(10,19);
%% coarse to fine procedure
for i2=1:no_of_modes
x_1 = x_1+imf_ch1(11-i2,:);apn(i2,1) = ApEn(x_1,2,0.001);
sham(i2,1) = SampEn(x_1,2,0.2);perm(i2,1) = PermEn(x_1,2);ren(i2,1)= func_FE_RenyiEn(x_1,6250,2);
x_2 = x_2+imf_ch2(11-i2,:);apn(i2,2) = ApEn(x_2,2,0.001);
sham(i2,2) = SampEn(x_2,2,0.2);perm(i2,2) = PermEn(x_2,2);ren(i2,2)= func_FE_RenyiEn(x_2,6250,2);
x_3 = x_3+imf_ch3(11-i2,:);apn(i2,3) = ApEn(x_3,2,0.001);
sham(i2,3) = SampEn(x_3,2,0.2);perm(i2,3) = PermEn(x_3,2);ren(i2,3)= func_FE_RenyiEn(x_3,6250,2);
x_4 = x_4+imf_ch4(11-i2,:);apn(i2,4) = ApEn(x_4,2,0.001);
sham(i2,4) = SampEn(x_4,2,0.2);perm(i2,4) = PermEn(x_4,2);ren(i2,4)= func_FE_RenyiEn(x_4,6250,2);
x_5 = x_5+imf_ch5(11-i2,:);apn(i2,5) = ApEn(x_5,2,0.001);
sham(i2,5) = SampEn(x_5,2,0.2);perm(i2,5) = PermEn(x_5,2);ren(i2,5)= func_FE_RenyiEn(x_5,6250,2);
x_6 = x_6+imf_ch6(11-i2,:);apn(i2,6) = ApEn(x_6,2,0.001);
sham(i2,6) = SampEn(x_6,2,0.2);perm(i2,6) = PermEn(x_6,2);ren(i2,6)= func_FE_RenyiEn(x_6,6250,2);
x_7 = x_7+imf_ch7(11-i2,:);apn(i2,7) = ApEn(x_7,2,0.001);
sham(i2,7) = SampEn(x_7,2,0.2);perm(i2,7) = PermEn(x_7,2);ren(i2,7)= func_FE_RenyiEn(x_7,6250,2);
x_8 = x_8+imf_ch8(11-i2,:);apn(i2,8) = ApEn(x_8,2,0.001);
sham(i2,8) = SampEn(x_8,2,0.2);perm(i2,8) = PermEn(x_8,2);ren(i2,8)= func_FE_RenyiEn(x_8,6250,2);
x_9 = x_9+imf_ch9(11-i2,:);apn(i2,9) = ApEn(x_9,2,0.001);
sham(i2,9) = SampEn(x_9,2,0.2);perm(i2,9) = PermEn(x_9,2);ren(i2,9)= func_FE_RenyiEn(x_9,6250,2);
x_10 = x_10+imf_ch10(11-i2,:);apn(i2,10) = ApEn(x_10,2,0.001);
sham(i2,10) = SampEn(x_10,2,0.2);perm(i2,10) = PermEn(x_10,2);ren(i2,10)= func_FE_RenyiEn(x_10,6250,2);
x_11 = x_11+imf_ch11(11-i2,:);apn(i2,11) = ApEn(x_11,2,0.001);
sham(i2,11) = SampEn(x_11,2,0.2);perm(i2,11) = PermEn(x_11,2);ren(i2,11)= func_FE_RenyiEn(x_11,6250,2);
x_12 = x_12+imf_ch12(11-i2,:);apn(i2,12) = ApEn(x_12,2,0.001);
sham(i2,12) = SampEn(x_12,2,0.2);perm(i2,12) = PermEn(x_12,2);ren(i2,12)= func_FE_RenyiEn(x_12,6250,2);
x_13 = x_13+imf_ch13(11-i2,:);apn(i2,13) = ApEn(x_13,2,0.001);
sham(i2,13) = SampEn(x_13,2,0.2);perm(i2,13) = PermEn(x_13,2);ren(i2,13)= func_FE_RenyiEn(x_13,6250,2);
x_14 = x_14+imf_ch14(11-i2,:);apn(i2,14) = ApEn(x_14,2,0.001);
sham(i2,14) = SampEn(x_14,2,0.2);perm(i2,14) = PermEn(x_14,2);ren(i2,14)= func_FE_RenyiEn(x_14,6250,2);
x_15 = x_15+imf_ch15(11-i2,:);apn(i2,15) = ApEn(x_15,2,0.001);
sham(i2,15) = SampEn(x_15,2,0.2);perm(i2,15) = PermEn(x_15,2);ren(i2,15)= func_FE_RenyiEn(x_15,6250,2);
x_16 = x_16+imf_ch16(11-i2,:);apn(i2,16) = ApEn(x_16,2,0.001);
sham(i2,16) = SampEn(x_16,2,0.2);perm(i2,16) = PermEn(x_16,2);ren(i2,16)= func_FE_RenyiEn(x_16,6250,2);
x_17 = x_17+imf_ch17(11-i2,:);apn(i2,17) = ApEn(x_17,2,0.001);
sham(i2,17) = SampEn(x_17,2,0.2);perm(i2,17) = PermEn(x_17,2);ren(i2,17)= func_FE_RenyiEn(x_17,6250,2);
x_18 = x_18+imf_ch18(11-i2,:);apn(i2,18) = ApEn(x_18,2,0.001);
sham(i2,18) = SampEn(x_18,2,0.2);perm(i2,18) = PermEn(x_18,2);ren(i2,18)= func_FE_RenyiEn(x_18,6250,2);
x_19 = x_19+imf_ch19(11-i2,:);apn(i2,19) = ApEn(x_19,2,0.001);
sham(i2,19) = SampEn(x_19,2,0.2);perm(i2,19) = PermEn(x_19,2);ren(i2,19)= func_FE_RenyiEn(x_19,6250,2);
end
%% plot boxplots for sample entropy
boxplot(sham(:,:)',{1,2,3,4,5,6,7,8,9,10},Widths=0.3);
xlabel("Scale");
ylabel("Sample Entropy")
%% plot boxplots for permutation entropy
boxplot(perm(:,:)',{1,2,3,4,5,6,7,8,9,10},Widths=0.3);
xlabel("Scale");
ylabel("Permutation Entropy")
%% plot boxplots for approximate entropy
boxplot(apn(:,:)',{1,2,3,4,5,6,7,8,9,10},Widths=0.3);
xlabel("Scale");
ylabel("Approximate Entropy")
%% plot boxplots for renyi entropy
boxplot(ren(:,:)',{1,2,3,4,5,6,7,8,9,10},Widths=0.3);
xlabel("Scale");
ylabel("Renyi Entropy")
%%