-
Notifications
You must be signed in to change notification settings - Fork 0
/
binaryClassification.m
397 lines (319 loc) · 14.5 KB
/
binaryClassification.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
%% 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 Hj�rth parameters and labels for every subject
fprintf('Computing Hj�rth 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
[hjorthParametersAux, segments] = computeHjorthParameters(spo2Processed,spo2Fs,ppi,ppiFs);
% 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 Hj�rth 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] = computeHjorthParameters(spo2Processed,spo2Fs,ppi,ppiFs);
% 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 = trainBinaryClassifierOnlySpo2([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] = computeHjorthParameters(spo2Processed,spo2Fs,ppi,ppiFs);
% 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/binaryClassificationOnlySpo2.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)