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binaryClassificationHRV.m
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%% Model selection
clear
addpath(genpath('lib'));
rng('default');
tic
warning('off')
% Load all files in database directory
dirlist = dir('results/ppi/');
files = cell([1 length(dirlist)-2]);
for kk = 3:length(dirlist)
files{kk-2} = dirlist(kk).name;
end
% Compute Hjorth parameters and labels for every subject
fprintf('Computing Hjorth parameters and labels for every subject\n');
labels = [];
hjorthParameters = [];
subjects = [];
for kk = 1:length(files)
subject = split(files{kk},'_');
subject = subject(1);
fprintf('Computing subject: %s...',string(subject));
load(strcat('results/labels/',string(subject),'_newlabels.mat'));
load(strcat('results/signals/',string(subject),'_psg.mat'),'nasalPressureArtifacts','nasalPressureFs', ...
'spo2Processed','spo2Fs','hypno','tHypno');
load(strcat('results/ppi/',string(subject),'_ppi.mat'));
% Compute Hjorth parameters. HRV included in hjorthParametersAux
[hjorthParametersAux, segments] = computeHjorthParametersAndHRV(spo2Processed,spo2Fs,ppi,ppiFs,ppiNoFilter);
% Compute labels
labelsAux = computeSegmentLabels(segments, abnormalSegments, apneaSegments, hypoSegments);
% Remove labels during nasal pressure artifacts
nasalPressureArtifactsSeconds = (nasalPressureArtifacts-1)/nasalPressureFs;
for ll=1:size(nasalPressureArtifactsSeconds,1)
for jj = 1:size(segments,1)
if ((segments(jj,1) > nasalPressureArtifactsSeconds(ll,1)) && (segments(jj,1) < nasalPressureArtifactsSeconds(ll,2))) || ...
(segments(jj,2) > nasalPressureArtifactsSeconds(ll,1)) && (segments(jj,2) < nasalPressureArtifactsSeconds(ll,2))
hjorthParametersAux(jj,:) = nan;
end
end
end
labelsAux(logical(sum(isnan(hjorthParametersAux),2)),:) = []; % Delete NaNs
hjorthParametersAux(logical(sum(isnan(hjorthParametersAux),2)),:) = []; % Delete NaNs
labels = [labels; labelsAux]; %#ok<*AGROW>
hjorthParameters = [hjorthParameters; hjorthParametersAux];
subjects = [subjects; kk*ones(size(labelsAux))];
fprintf('Done\n');
end
labels(labels==2) = 1;
% Random class balancing
fprintf('Random class balancing... ');
[hjorthParameters,labels] = balanceRandomBinary(hjorthParameters,labels,subjects);
fprintf('Done\n');
learnerMatrix = [hjorthParameters labels];
toc
% Select the best model using Classification Learner app with 5-folds based
% on AUC
%% Validation preprocessing
% Leave one out strategy
clear
addpath(genpath('lib'));
addpath(genpath('models'));
rng('default');
plotflag = false;
tic
warning('off')
% Load all files in database directory
dirlist = dir('results/ppi/');
files = cell([1 length(dirlist)-2]);
for kk = 3:length(dirlist)
files{kk-2} = dirlist(kk).name;
end
% Compute parameters and labels for every subject
fprintf('Computing Hjorth parameters and labels for every subject...\n');
hjorthParameters = [];
labels = [];
subjects = [];
hypnoLabels = [];
for kk = 1:length(files)
subject = split(files{kk},'_');
subject = subject(1);
fprintf('Computing subject: %s...',string(subject));
load(strcat('results/labels/',string(subject),'_newlabels.mat'));
load(strcat('results/signals/',string(subject),'_psg.mat'),'nasalPressureArtifacts','nasalPressureFs', ...
'spo2Processed','spo2Fs','hypno','tHypno');
load(strcat('results/ppi/',string(subject),'_ppi.mat'));
% Compute Hjorth parameters
[hjorthParametersAux, segments] = computeHjorthParametersAndHRV(spo2Processed,spo2Fs,ppi,ppiFs,ppiNoFilter);
% Compute labels
labelsAux = computeSegmentLabels(segments, abnormalSegments, apneaSegments, hypoSegments);
hypnoLabelsAux = computeHypnoLabels(segments,hypno,tHypno);
% Remove labels during nasal pressure artifacts
nasalPressureArtifactsSeconds = (nasalPressureArtifacts-1)/nasalPressureFs;
for ll=1:size(nasalPressureArtifactsSeconds,1)
for jj = 1:size(segments,1)
if ((segments(jj,1) > nasalPressureArtifactsSeconds(ll,1)) && (segments(jj,1) < nasalPressureArtifactsSeconds(ll,2))) || ...
(segments(jj,2) > nasalPressureArtifactsSeconds(ll,1)) && (segments(jj,2) < nasalPressureArtifactsSeconds(ll,2))
hjorthParametersAux(jj,:) = nan;
end
end
end
labelsAux(logical(sum(isnan(hjorthParametersAux),2)),:) = []; % Delete NaNs
hypnoLabelsAux(logical(sum(isnan(hjorthParametersAux),2)),:) = []; % Delete NaNs
hjorthParametersAux(logical(sum(isnan(hjorthParametersAux),2)),:) = []; % Delete NaNs
hjorthParameters = [hjorthParameters; hjorthParametersAux];
labels = [labels; labelsAux];
hypnoLabels = [hypnoLabels; hypnoLabelsAux];
subjects = [subjects; kk*ones(size(labelsAux))];
fprintf('Done\n');
end; clear labelsAux hjorthParametersAux hypnoLabelsAux kk
fprintf('\n');
labels(labels==2) = 1;
% Random class balancing
fprintf('Random class balancing... ');
[hjorthParameters,labels,subjects] = balanceRandomBinary(hjorthParameters,labels,subjects);
fprintf('Done\n');
toc
%% Validation using LOO strategy
tic
disp('Train and test using LOO strategy');
predictionsTest = [];
labelsTest = [];
cvhriTest = []; % new index to check correlation
subjectTest = [];
corrects = []; % rate of correct detections
for kk = 1:length(files)
% Train
trainedClassifier = trainBinaryClassifierHRV([hjorthParameters(subjects~=kk,:) labels(subjects~=kk)]);
% Test subject out
subject = split(files{kk},'_');
subject = subject(1);
fprintf('Computing subject: %s...',string(subject));
load(strcat('results/labels/',string(subject),'_newlabels.mat'));
if plotflag
load(strcat('results/signals/',string(subject),'_psg.mat'),'nasalPressureArtifacts','nasalPressureFs', ...
'nasalPressureProcessed','tNasalPressure','spo2Processed','spo2Fs','tSpo2','hypno','tHypno','tidalVolume');
else
load(strcat('results/signals/',string(subject),'_psg.mat'),'nasalPressureArtifacts','nasalPressureFs', ...
'spo2Processed','spo2Fs');
end
load(strcat('results/ppi/',string(subject),'_ppi.mat'));
% Compute Hjorth parameters
[hjorthParametersTestAux, segments] = computeHjorthParametersAndHRV(spo2Processed,spo2Fs,ppi,ppiFs,ppiNoFilter);
% Compute labels
labelsTestAux = computeSegmentLabels(segments, abnormalSegments, apneaSegments, hypoSegments);
% Remove labels during nasal pressure artifacts
nasalPressureArtifactsSeconds = (nasalPressureArtifacts-1)/nasalPressureFs;
for ll=1:size(nasalPressureArtifactsSeconds,1)
for jj = 1:size(segments,1)
if ((segments(jj,1) > nasalPressureArtifactsSeconds(ll,1)) && (segments(jj,1) < nasalPressureArtifactsSeconds(ll,2))) || ...
(segments(jj,2) > nasalPressureArtifactsSeconds(ll,1)) && (segments(jj,2) < nasalPressureArtifactsSeconds(ll,2))
hjorthParametersTestAux(jj,:) = nan;
end
end
end
labelsTestAux(logical(sum(isnan(hjorthParametersTestAux),2)),:) = []; % Delete NaNs
segments(logical(sum(isnan(hjorthParametersTestAux),2)),:) = []; % Delete NaNs
hjorthParametersTestAux(logical(sum(isnan(hjorthParametersTestAux),2)),:) = []; % Delete NaNs
% Predict
predictionsTestAux = trainedClassifier.predictFcn(hjorthParametersTestAux);
predictionsTest = [predictionsTest; predictionsTestAux];
labelsTestAux(labelsTestAux==2) = 1;
labelsTest = [labelsTest; labelsTestAux];
subjectTest = [subjectTest; ones(size(labelsTestAux))*kk];
correctsAux = mean(~xor(labelsTestAux,predictionsTestAux));
corrects = [corrects; correctsAux];
% CVHRI
segmentIndexes = floor(segments*ppiFs)+1;
cvhriTestAux = nan(1,size(segments,1));
for ll = 1:size(segmentIndexes,1)
if predictionsTestAux(ll) > 0
segmentFFT = abs(fft(detrend(ppi(segmentIndexes(ll,1):segmentIndexes(ll,2))),2^15));
segmentFFT = segmentFFT(1:length(segmentFFT)/2);
f = (linspace(0,0.5,numel(segmentFFT)))*ppiFs;
[peak,cvhriTestAux(ll)] = findpeaks(segmentFFT(f<0.1),f(f<0.1),'NPeaks',1,'SortStr','descend');
end
end; clear ll aux
cvhriTestAux = sum(cvhriTestAux,'omitnan')/size(segmentIndexes,1);
cvhriTest = [cvhriTest; cvhriTestAux];
% Plots
if plotflag
nasalPressureWithoutArtifacts = nasalPressureProcessed;
for ll=1:size(nasalPressureArtifacts,1)
nasalPressureWithoutArtifacts(nasalPressureArtifacts(ll,1):nasalPressureArtifacts(ll,2)) = nan;
end
figure
ax(1) = subplot(511);
hold on
for ll=1:size(apneas,1)
p(1) = patch([apneas(ll, 1) apneas(ll, 2) apneas(ll, 2) apneas(ll, 1)],[-6 -6 15 15],[1 0 0],'FaceAlpha',.3,'EdgeColor','none');
end; clear ll
for ll=1:size(hypopneas,1)
p(2) = patch([hypopneas(ll, 1) hypopneas(ll, 2) hypopneas(ll, 2) hypopneas(ll, 1)],[-6 -6 15 15],[0 0 1],'FaceAlpha',.3,'EdgeColor','none');
end; clear ll
for ll=1:size(doubts,1)
p(3) = patch([doubts(ll, 1) doubts(ll, 2) doubts(ll, 2) doubts(ll, 1)],[-6 -6 15 15],[0 0 0],'FaceAlpha',.3,'EdgeColor','none');
end; clear ll
plot(tNasalPressure,nasalPressureProcessed,'r')
plot(tNasalPressure,nasalPressureWithoutArtifacts,'b')
plot(tNasalPressure,medfilt1(tidalVolume,180*nasalPressureFs)+5,'k--');
plot(tNasalPressure,tidalVolume+5);
yline(5)
ylabel('Nasal pressure')
axis tight;
ax(2) = subplot(512);
plot(tSpo2,spo2Processed,'b'); axis tight;
hold on
for ll=1:size(apneas,1)
p(1) = patch([apneas(ll, 1) apneas(ll, 2) apneas(ll, 2) apneas(ll, 1)],[90 90 100 100],[1 0 0],'FaceAlpha',.3,'EdgeColor','none');
end; clear ll
for ll=1:size(hypopneas,1)
p(2) = patch([hypopneas(ll, 1) hypopneas(ll, 2) hypopneas(ll, 2) hypopneas(ll, 1)],[90 90 100 100],[0 0 1],'FaceAlpha',.3,'EdgeColor','none');
end; clear ll
for ll=1:size(doubts,1)
p(3) = patch([doubts(ll, 1) doubts(ll, 2) doubts(ll, 2) doubts(ll, 1)],[90 90 100 100],[0 0 0],'FaceAlpha',.3,'EdgeColor','none');
end; clear ll
ylabel('SpO2')
ax(3) = subplot(513);
p = plot(tHypno,hypno,'b'); axis tight;
hold on
for ll=1:size(apneas,1)
p(1) = patch([apneas(ll, 1) apneas(ll, 2) apneas(ll, 2) apneas(ll, 1)],[0 0 6 6],[1 0 0],'FaceAlpha',.3,'EdgeColor','none');
end; clear ll
for ll=1:size(hypopneas,1)
p(2) = patch([hypopneas(ll, 1) hypopneas(ll, 2) hypopneas(ll, 2) hypopneas(ll, 1)],[0 0 6 6],[0 0 1],'FaceAlpha',.3,'EdgeColor','none');
end; clear ll
for ll=1:size(doubts,1)
p(3) = patch([doubts(ll, 1) doubts(ll, 2) doubts(ll, 2) doubts(ll, 1)],[0 0 6 6],[0 0 0],'FaceAlpha',.3,'EdgeColor','none');
end; clear ll
ylabel('Hypnogram')
yticks(1:5);
yticklabels({'NREM3','NREM2','NREM1','REM','WAKE'})
ax(4) = subplot(514);
hold on
for ll = 1:size(segments,1)
if labelsTestAux(ll)>0
p(4) = patch([segments(ll, 1) segments(ll, 2) segments(ll, 2) segments(ll, 1)],[0 0 2 2],[1 0 0],'EdgeColor','none');
end
end; clear ll
plot(tPpi,ppi)
ylabel('Labels')
axis tight;
ax(5) = subplot(515);
hold on
for ll = 1:size(segments,1)
if predictionsTestAux(ll)==1 % apnea
p(2) = patch([segments(ll, 1) segments(ll, 2) segments(ll, 2) segments(ll, 1)],[0 0 2 2],[1 0 0],'EdgeColor','none');
end
end; clear ll
plot(tPpi,ppi)
ylabel('Predicted')
axis tight;
xlabel('Time (seconds)');
linkaxes(ax,'x');
set(gcf, 'Position', get(0, 'Screensize'));
pause;
end
fprintf('Done\n');
end; clear kk
toc
c = confusionmat(labelsTest,predictionsTest); %#ok<*NASGU>
plotconfusion(labelsTest',predictionsTest');
%% Save/Load results
save(strcat('results/classification/binaryClassificationHRV.mat'), ...
'corrects','cvhriTest','labelsTest','predictionsTest','subjectTest');
% load(strcat('results/classification/binaryClassification.mat'), ...
% 'corrects','cvhriTest','labelsTest','predictionsTest','subjectTest');
%% Exclude test errors
% Load AHI
load(strcat('results/AHI_new.mat'));
% 33 was wake most of the time
% 57 problems with nasal pressure signal. Not reliable annotations
exclude = [33 57];
for kk = 1:numel(exclude)
labelsTest(subjectTest==exclude(kk)) = nan;
predictionsTest(subjectTest==exclude(kk)) = nan;
cvhriTest(exclude(kk)) = nan;
corrects(exclude(kk)) = nan;
ahiDataset(exclude(kk)) = nan;
end
%% Segment detection
figure;
stem(corrects*100); ylim([0 100])
xlabel('Subject'); ylabel('Correct detections (%)')
% hold on; yline(mean(corrects)*100,'--');
hold on; plot(xlim,[mean(corrects,'omitnan')*100 mean(corrects,'omitnan')*100],'--');
mean(corrects,'omitnan')
% figure;
c = confusionmat(labelsTest,predictionsTest)
plotconfusion(labelsTest',predictionsTest');
%% Correlation
[rhoPearson,pvalPearson] = corr(ahiDataset,cvhriTest,'Type','Pearson','Rows','complete') %#ok<*ASGLU>
figure;
plot(ahiDataset,cvhriTest,'o');
xlabel('AHI'); ylabel('CVHRI')
hold on; xline(15,'--k');% yline(1/15,'--k')
hold on; xline(5,'--k');% yline(1/30,'--k')
xlim([0 70])
xticks(0:10:70)
% hold on; plot([15 15],ylim,'--');% plot(xlim,[1/15 1/15],'--')
% saveas(gca, 'results/images/binaryCorrelationOnlyPPI.eps','epsc');
%%
[rhoPearson,pvalPearson] = corr(ahiDataset(ahiDataset<15),cvhriTest(ahiDataset<15),'Type','Pearson','Rows','complete')
[rhoPearson,pvalPearson] = corr(ahiDataset(ahiDataset>=15),cvhriTest(ahiDataset>15),'Type','Pearson','Rows','complete')
%% Separate AHI<15 and AHI >= 15
ahiHigherThan15 = find(ahiDataset>=15);
ahiLowerThan15 = find(ahiDataset<15);
labelsTestAhiHigherThan15 = [];
predictionsTestAhiGreaterThan15 = [];
for kk = 1:numel(ahiHigherThan15)
labelsTestAhiHigherThan15 = [labelsTestAhiHigherThan15; labelsTest(subjectTest==ahiHigherThan15(kk))];
predictionsTestAhiGreaterThan15 = [predictionsTestAhiGreaterThan15; predictionsTest(subjectTest==ahiHigherThan15(kk))];
end
labelsTestAhiLowerThan15 = [];
predictionsTestAhiLowerThan15 = [];
for kk = 1:numel(ahiLowerThan15)
labelsTestAhiLowerThan15 = [labelsTestAhiLowerThan15; labelsTest(subjectTest==ahiLowerThan15(kk))];
predictionsTestAhiLowerThan15 = [predictionsTestAhiLowerThan15; predictionsTest(subjectTest==ahiLowerThan15(kk))];
end
c = confusionmat(labelsTestAhiHigherThan15,predictionsTestAhiGreaterThan15)
c = confusionmat(labelsTestAhiLowerThan15,predictionsTestAhiLowerThan15)