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spm_acfjc_spectral_entropy_spectrum.m
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spm_acfjc_spectral_entropy_spectrum.m
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function spm_acfjc_spectral_entropy_spectrum
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Author: %%
%%% Aldo Camargo %%
%%% University of Liege (Belgium) %%
%%% %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Paper: "Actigraphy assessments of circardian sleep-wake
%% cycles in the Vegetative and Minimally Conscious States"
%%
close all, clear all, clc
% Step 1: Reading and loading the file
file = spm_select(Inf, '.+\.AWD$', 'Select files ',...
[], [], '.AWD'); %Show all .AWD files in the directory datadir
[pathstr_ses,name_ses,ext_ses] = fileparts(file);
resolution = 1; % resolution of the epocs is one minute
% Step 1.1: Setting init values for the computation
graph_mean_day_spectral_entropy = true;
% Step 2: Setting the number of days of analysis
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Number of days to analysis %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
nday_analysis = 5;
isub = 1;
%---------------------------------------------
% Step 2: Loading the data into the variable acti
disp([' Reading file : ' num2str(file)]);
eval(['logfile = readcoglog(file);']);% uses 'readcoglog' to transfrom raw data in a usable format
acti = [];
for hh = 8:size(logfile,1)%the 8 first lines are not data movement data
acti(hh,1) = str2num(strvcat(cellstr(logfile{hh}(1))));%to chane cell array into number
end
start_date =strvcat(cellstr(logfile{2}(1)));
start_hour =strvcat(cellstr(logfile{3}(1)));
temp_start_hour = strsplit(start_hour,':');
int_start_hour = str2num(strvcat(temp_start_hour(1)));
int_start_min = str2num(strvcat(temp_start_hour(2)));
factor = 1440/resolution;
nbj = floor(size(acti,1)/factor); %to count the total number of full days (24h) of recording
disp([num2str(nbj) ' x 24h enregistr�es']);
timestart = strvcat(cellstr(logfile{3}(1)));% heure du d�but de l'enregistrement
heure= str2num(timestart(1:2));
min = str2num(timestart(4:5));
[n_days_recorded, nnn] = size(acti);
factor_conv_time = resolution/(60*24);
n_days_recorded = n_days_recorded*factor_conv_time
if(n_days_recorded < 5)
fprintf(1,'\n The number of days is less than 5 days ... Sorry... :( ... \n',n_days_recorded )
return;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% %%
%%% Step 3: Removing the first 2 hours of data of the first day %%
%%% %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
tf_delete = 2*60/resolution;
acti_days{1}(1:tf_delete) = 0;
acti(1:tf_delete) = 0;
%%%
% Step 4: Computing the Mesor, ampitudem and Acrophase of the first 4 days
%fprintf(1,'\n Computing the Messor, Amplitude and Acrophase of the %g days: ... \n', nday_analysis);
%
nday_analysis
size_tt = 24*60*nday_analysis/resolution;
y_tt = acti(1:resolution:size_tt);
t_tt = 1:resolution:size_tt;
t_tt = t_tt';
t_tt = t_tt./max(t_tt);
w = 2*pi;
alpha = .05; % 5%
factor_angle = 180/pi; % to convert radians to degrees
%[M_tt,Amp_tt,phi_tt,p_value_tt] = cosinor(t_tt,y_tt,w,alpha,false);
% disp('-------------------------------------------');
% fprintf(1,'\n Results for the first %g days \n', nday_analysis);
% disp('-------------------------------------------');
%
%
% fprintf(1,'Messor: = %g \n', M_tt);
% fprintf(1,'Amplitude: = %g \n', Amp_tt);
% fprintf(1,'Acrophase (degrees): = %g \n', (factor_angle)*phi_tt);
% fprintf(1,'p-value: = %g \n', p_value_tt);
% disp('-------------------------------------------');
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Step 4: Graphics of the data (first 4 days) %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
init_data_time = int_start_hour*60 + int_start_min;
day_t4 = 24*60*4;
tt_4 = 1:resolution:day_t4;
yy_4 = acti(1:resolution:day_t4);
% figure,
% subplot(1,2,1); plot(tt_4, yy_4); title('Day 1 to Day 4');
% day 1%
% considering that all the values start on different hours and mins
day_1 = (24*60*1 - init_data_time ) + 1;
t_1 = 1:resolution:day_1;
y_1 = acti(1:resolution:day_1);
y_1_median = medfilt1(y_1);
%figure,
%subplot(3,2,2); plot(t_1, y_1); title('Day 1');
% Finding morning and night information
init_day = 8;
end_day = 20;
if (int_start_hour > init_day && int_start_hour < end_day )
day_1_morning = end_day*60 - init_data_time;
day_1_night = day_1 - day_1_morning;
else
day_1_morning = 0;
day_1_night = day_1 - day_1_morning;
end
if ( day_1_morning > 0 )
t1_morning = 1:resolution:day_1_morning;
t1_night = day_1_morning+1:resolution:day_1;
y1_morning = acti(1:resolution:day_1_morning);
y1_night = acti(day_1_morning + 1:resolution:day_1);
% figure,
% subplot(2,1,1); plot(t1_morning, y1_morning);
% subplot(2,1,2); plot(t1_night, y1_night);
else
t1_morning = 0;
y1_morning = [];
y1_night = acti(day_1_morning + 1:resolution:day_1);
% figure,
t1_night = day_1_morning+1:resolution:day_1;
% subplot(2,1,2); plot(t1_night, y1_night);
end
if(t1_morning > 0 )
y1_morning_mean = (sum(y1_morning))/(length(t1_morning));
y1_night_mean = (sum(y1_night))/(length(t1_night));
else
y1_morning_mean = [];
y1_night_mean = [];
end
% day 2 %
day_2 = (24*60*2 - init_data_time ) + 1;
t_2 = (day_1 + 1):resolution:day_2;
y_2 = acti(day_1 + 1:resolution:day_2);
%figure,
%subplot(3,2,3); plot(t_2, y_2); title('Day 2');
% day and night
t2_morning = [];
y2_morning = [];
t2_night = [];
y2_morning = [];
count_morning_2 = 0;
y2_morning_mean = 0;
count_night_2 = 0;
y2_night_mean = 0;
for tt=(day_1 + 1):day_2
if((tt >= day_1 + 8*60) && (tt < day_1 + 20*60))
t2_morning(tt) = tt;
y2_morning(tt) = acti(tt);
t2_night(tt) = tt;
y2_night(tt) = 0;
count_morning_2 = count_morning_2 + 1;
y2_morning_mean = y2_morning_mean + y2_morning(tt);
else
t2_morning(tt) = tt;
y2_morning(tt) = 0;
t2_night(tt) = tt;
y2_night(tt) = acti(tt);
count_night_2 = count_night_2 + 1;
y2_night_mean = y2_night_mean + y2_night(tt);
end
end
y2_morning_mean = y2_morning_mean/count_morning_2;
y2_night_mean = y2_night_mean/count_night_2;
% plotting the day and night information
% t2_morning = (day_1 + 1):resolution:(day_2 - 12*60);
% t2_night = t2_morning:resolution:day_2;
% day 3 %
day_3 = (24*60*3 - init_data_time ) + 1;
t_3 = (day_2 + 1):resolution:day_3;
y_3 = acti(day_2 + 1:resolution:day_3);
% figure,
%subplot(3,2,4); plot(t_3, y_3); title('Day 3');
% day and night
t3_morning = [];
y3_morning = [];
t3_night = [];
y3_morning = [];
count_morning_3 = 0;
y3_morning_mean = 0;
count_night_3 = 0;
y3_night_mean = 0;
for tt=(day_2 + 1):day_3
if((tt >= day_2 + 8*60) && (tt < day_2 + 20*60))
t3_morning(tt) = tt;
y3_morning(tt) = acti(tt);
t3_night(tt) = tt;
y3_night(tt) = 0;
count_morning_3 = count_morning_3 + 1;
y3_morning_mean = y3_morning_mean + y3_morning(tt);
else
t3_morning(tt) = tt;
y3_morning(tt) = 0;
t3_night(tt) = tt;
y3_night(tt) = acti(tt);
count_night_3 = count_night_3 + 1;
y3_night_mean = y3_night_mean + y3_night(tt);
end
end
y3_morning_mean = y3_morning_mean/count_morning_3;
y3_night_mean = y3_night_mean/count_night_3;
% plotting the day and night information
%
% t2_morning =
% day 4 %
day_4 = (24*60*4 - init_data_time ) + 1;
t_4 = (day_3 + 1):resolution:day_4;
y_4 = acti(day_3 + 1:resolution:day_4);
% day 5 %
day_5 = (24*60*5 - init_data_time ) + 1;
t_5 = (day_4 + 1):resolution:day_5;
y_5 = acti(day_4 + 1:resolution:day_5);
%figure,
%subplot(3,2,5); plot(t_4, y_4); title('Day 4');
% day and night
t4_morning = [];
y4_morning = [];
t4_night = [];
y4_morning = [];
count_morning_4 = 0;
y4_morning_mean = 0;
count_night_4 = 0;
y4_night_mean = 0;
for tt4=(day_3 + 1):day_4
if((tt4 >= day_3 + 8*60) && (tt4 < day_3 + 20*60))
t4_morning(tt4) = tt4;
y4_morning(tt4) = acti(tt4);
t4_night(tt4) = tt4;
y4_night(tt4) = 0;
count_morning_4 = count_morning_4 + 1;
y4_morning_mean = y4_morning_mean + y4_morning(tt4);
else
t4_morning(tt4) = tt4;
y4_morning(tt4) = 0;
t4_night(tt4) = tt4;
y4_night(tt4) = acti(tt4);
count_night_4 = count_night_4 + 1;
y4_night_mean = y4_night_mean + y4_night(tt4);
end
end
y4_morning_mean = y4_morning_mean/count_morning_4;
y4_night_mean = y4_night_mean/count_night_4;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%% Computing the mean for every day %%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if(graph_mean_day_spectral_entropy)
temp = [ y_2'; y_3'; y_4'; y_5'];
y_mean = zeros(1,1440); % Because the epochs are on 1 min interval and is for 24 hours
for i=1:1440 % Because the epochs are on 1 min interval and is for 24 hours
y_mean(i) = mean([y_2(i) y_3(i) y_4(i) y_5(i)]);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Creating the chunk of data %%%
%%% %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Chunks every k min %%%
k = 120; %%%% 120 min = 2 h
% for day 2
% y2_total = y2_morning + y2_night;
% t2_total = t2_morning + t2_night;
% figure,
% subplot(2,2,1), plot(t2_total, y2_total);
% subplot(2,2,2), plot(t2_morning, y2_morning);
% subplot(2,2,3), plot(t2_night, y2_night);
% subplot(2,2,4), plot(t_2, y_2);
number_of_chunks = 24*60/k;
L_y2 = length(y_2); % because epocs of 1 min
j = 1;
for i=1:k:L_y2
y2_chunk{j} = y_2(i:i+k-1);
j = j + 1;
end
L_y3 = length(y_3); % because epocs of 1 min
j = 1;
for i=1:k:L_y2
y3_chunk{j} = y_3(i:i+k-1);
j = j + 1;
end
L_y4 = length(y_4); % because epocs of 1 min
j = 1;
for i=1:k:L_y2
y4_chunk{j} = y_4(i:i+k-1);
j = j + 1;
end
% I am considering day 5 because the first day is not considered. The
% reason is that day 1 is the first day where the patient has the
% acticmeter on his arm, and normally start between 12 pm and 2 pm, which
% pretty much reduces a lot the time for computation.
L_y5 = length(y_5); % because epocs of 1 min
j = 1;
for i=1:k:L_y2
y5_chunk{j} = y_5(i:i+k-1);
j = j + 1;
end
L_y5 = length(y_5); % because epocs of 1 min
j = 1;
for i=1:k:L_y2
y5_chunk{j} = y_5(i:i+k-1);
j = j + 1;
end
%save('y2chunk', 'y2_chunk', 'y3_chunk');
if(graph_mean_day_spectral_entropy)
L_ymean = length(y_mean); % because epocs of 1 min
j = 1;
for i=1:k:L_ymean
y_mean_chunk{j} = y_mean(i:i+k-1);
j = j + 1;
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Spectral Entropy %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%
%for dd = 2:5 % from day 2 to 5
windows_i = 60
if(graph_mean_day_spectral_entropy)
figure,
for k =1:6
SE = [];
TM = [];
for i = 1 : 15 : length(y_mean_chunk{k})
if i+windows_i-1 <= length(y_mean_chunk{k})
%aux = act(i:i+30-1);
aux = y_mean_chunk{k}(i:i+30-1);
Y = my_spectral_entropy(aux,1/60,[.0006 .0083],windows_i,length(aux),0);
SE = [SE Y];
TM = [TM i];
end
end
% subplot(3,2,k),plot(TM, SE), ylim([.7 1]);
end
figure,
for k =7:12
SE = [];
TM = [];
for i = 1 : 1 : length(y_mean_chunk{k}) % it was 15
if i+windows_i-1 <= length(y_mean_chunk{k})
%aux = act(i:i+30-1);
aux = y_mean_chunk{k}(i:i+30-1);
Y = my_spectral_entropy(aux,1/60,[.0006 .0083],windows_i,length(aux),0);
SE = [SE Y];
TM = [TM i];
end
end
% subplot(3,2,k-6),plot(TM, SE), ylim([.7 1]);
end
temp_y_mean_chunk = [];
for ii = 1:number_of_chunks
% temp_y_mean_chunk = [y_mean_chunk{2}; y_mean_chunk{3}; y_mean_chunk{4}; y_mean_chunk{5} ];
temp_y_mean_chunk = [temp_y_mean_chunk; y_mean_chunk{ii} ];
end
mean_y_mean_chunk = mean(temp_y_mean_chunk);
figure,
SE_mean = [];
TM_mean = [];
for i = 1 : 1: length(mean_y_mean_chunk) % it was 15
if i+windows_i-1 <= length(mean_y_mean_chunk)
%aux = act(i:i+30-1);
aux = mean_y_mean_chunk(i:i+30-1);
Y = my_spectral_entropy(aux,1/60,[.0006 .0083],windows_i,length(aux),0);
SE_mean = [SE_mean Y];
TM_mean = [TM_mean i];
end
end
% plot(TM_mean, SE_mean), ylim([.7 1]),title('Plot of the mean of chunks for the days 2,3,4 and 5 ');
crs = 'UWS';
graph_spectral_entropy(SE_mean, crs);
%save('y_mean_chunk_120min', 'TM_mean', 'SE_mean', 'mean_y_mean_chunk');
s1 = name_ses;
s2 = '.mat';
s = strcat(s1,s2);
pathstr_ses = strcat(pathstr_ses, '/');
path_copy = strcat(pathstr_ses, s)
copyfile('save_aux.mat', path_copy )
delete save_aux.mat
else
% Working for days 2, 3, 4 and 5
% day 2%
figure,
for k =1:6
SE = [];
TM = [];
for i = 1 : 15 : length(y2_chunk{k})
if i+windows_i-1 <= length(y2_chunk{k})
%aux = act(i:i+30-1);
aux = y2_chunk{k}(i:i+30-1);
Y = my_spectral_entropy(aux,1/60,[.0006 .0083],windows_i,length(aux),0);
SE = [SE Y];
TM = [TM i];
end
end
% subplot(3,2,k),plot(TM, SE), ylim([.7 1]);
end
figure,
for k =7:12
SE = [];
TM = [];
for i = 1 : 15 : length(y2_chunk{k})
if i+windows_i-1 <= length(y2_chunk{k})
%aux = act(i:i+30-1);
aux = y2_chunk{k}(i:i+30-1);
Y = my_spectral_entropy(aux,1/60,[.0006 .0083],windows_i,length(aux),0);
SE = [SE Y];
TM = [TM i];
end
end
% subplot(3,2,k-6),plot(TM, SE), ylim([.7 1]);
end
%%% day 3 %%%
figure,
for k =1:6
SE = [];
TM = [];
for i = 1 : 15 : length(y3_chunk{k})
if i+windows_i-1 <= length(y3_chunk{k})
%aux = act(i:i+30-1);
aux = y3_chunk{k}(i:i+30-1);
Y = my_spectral_entropy(aux,1/60,[.0006 .0083],windows_i,length(aux),0);
SE = [SE Y];
TM = [TM i];
end
end
% subplot(3,2,k),plot(TM, SE), ylim([.7 1]);
end
figure,
for k =7:12
SE = [];
TM = [];
for i = 1 : 15 : length(y3_chunk{k})
if i+windows_i-1 <= length(y3_chunk{k})
%aux = act(i:i+30-1);
aux = y3_chunk{k}(i:i+30-1);
Y = my_spectral_entropy(aux,1/60,[.0006 .0083],windows_i,length(aux),0);
SE = [SE Y];
TM = [TM i];
end
end
% subplot(3,2,k-6),plot(TM, SE), ylim([.7 1]);
end
%%% day 4 %%%%%%
figure,
for k =1:6
SE = [];
TM = [];
for i = 1 : 15 : length(y4_chunk{k})
if i+windows_i-1 <= length(y4_chunk{k})
%aux = act(i:i+30-1);
aux = y4_chunk{k}(i:i+30-1);
Y = my_spectral_entropy(aux,1/60,[.0006 .0083],windows_i,length(aux),0);
SE = [SE Y];
TM = [TM i];
end
end
% subplot(3,2,k),plot(TM, SE), ylim([.7 1]);
end
figure,
for k =7:12
SE = [];
TM = [];
for i = 1 : 15 : length(y4_chunk{k})
if i+windows_i-1 <= length(y4_chunk{k})
%aux = act(i:i+30-1);
aux = y4_chunk{k}(i:i+30-1);
Y = my_spectral_entropy(aux,1/60,[.0006 .0083],windows_i,length(aux),0);
SE = [SE Y];
TM = [TM i];
end
end
% subplot(3,2,k-6),plot(TM, SE), ylim([.7 1]);
end
%%% day 5 %%%%%%
figure,
for k =1:6
SE = [];
TM = [];
for i = 1 : 15 : length(y5_chunk{k})
if i+windows_i-1 <= length(y5_chunk{k})
%aux = act(i:i+30-1);
aux = y5_chunk{k}(i:i+30-1);
Y = my_spectral_entropy(aux,1/60,[.0006 .0083],windows_i,length(aux),0);
SE = [SE Y];
TM = [TM i];
end
end
% subplot(3,2,k),plot(TM, SE), ylim([.7 1]);
end
figure,
for k =7:12
SE = [];
TM = [];
for i = 1 : 15 : length(y5_chunk{k})
if i+windows_i-1 <= length(y5_chunk{k})
%aux = act(i:i+30-1);
aux = y5_chunk{k}(i:i+30-1);
Y = my_spectral_entropy(aux,1/60,[.0006 .0083],windows_i,length(aux),0);
SE = [SE Y];
TM = [TM i];
end
end
% subplot(3,2,k-6),plot(TM, SE), ylim([.7 1]);
end
end
end