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MainScript.m
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MainScript.m
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%% PDSB Project - Cuffless Blood Pressure Estimation
%%% Group B1
% Diogo Antunes no. 86979; Diogo Batista, no. 86767; Pedro Osório, no. 89777
%% 1. Initialization
clc;
close all;
clear all;
addpath('./dataset');
addpath('./feature-extraction');
addpath('./pca');
addpath('./preprocessing');
addpath('./Model');
global subject fs data
%% 2. Load and plot the signals
% The following signals:
% 's03386-2577-07-30-13-04m',
% 's30297-2903-04-30-12-46m', and
% 'p016129-2177-10-25-21-42m'
% have all three signals: ECG, PPG, and ABP.
filenames = {'s03386-2577-07-30-13-04m', ...
's30297-2903-04-30-12-46m', ...
'p016129-2177-10-25-21-42m'};
fs = 125; % Hz
for subject = 1 : length(filenames)-1
files(subject) = load(['./dataset/' char(filenames(subject)) '_data.mat']);
% Raw data
predata(subject).ecg = files(subject).data.signal(:,strcmp({files(1).data.labels.Description},'II'));
predata(subject).ppg = files(subject).data.signal(:,strcmp({files(1).data.labels.Description},'PLETH'));
predata(subject).abp = files(subject).data.signal(:,strcmp({files(1).data.labels.Description},'ABP'));
% Normalise PPG
predata(subject).ppg = predata(subject).ppg / max(predata(subject).ppg);
% Extra processing step to remove low freq in PPG signals
signals = [predata(subject).ecg, predata(subject).ppg, predata(subject).abp];
signals = nanremoval(signals);
predata(subject).ecg = signals(:,1);
predata(subject).ppg = signals(:,2);
predata(subject).abp = signals(:,3);
% HP filter PPG
HPFilt_PPG(predata);
% Plot Global signal
subplot(length(filenames), 3, 3*subject-2)
plot(predata(subject).ecg)
title(['ECG ', num2str(subject)])
subplot(length(filenames), 3, 3*subject-1)
plot(predata(subject).ppg)
title(['PPG ', num2str(subject)])
subplot(length(filenames), 3, 3*subject)
plot(predata(subject).abp)
title(['ABP ', num2str(subject)])
end
%% 3. Preprocessing
data = struct([]);
predata_denoised = predata;
for subject = 1 : length(predata)
% Remove NaN values
signals = [predata(subject).ecg, predata(subject).ppg, predata(subject).abp];
signals = nanremoval(signals);
% Remove bad segments
if (subject == 2)
signals = crop(signals, fs, 20, 4);
else
signals = crop(signals, fs, 20, 3);
end
predata(subject).ecg = signals{1};
predata(subject).ppg = signals{2};
predata(subject).abp = signals{3};
% Remove noise (very low and very high freq components) - Every signal
% section - ECG , PPG and ABP
predata_denoised(subject) = data_denoising(predata(subject));
% WIP
disp('Data has been denoised')
% Create QRS complex intervals
qrsIntervals(predata_denoised(subject));
disp('Data segmented into cardiac cycles')
end
%% Plot QRS complexes in ECG, PPG and ABP
figure
for i = 2 : 5
subplot(3, 4, i - 1)
plot(data(i).ecg)
subplot(3, 4, i - 1 + 4)
plot(data(i).ppg)
subplot(3, 4, i - 1 + 8)
plot(data(i).abp)
end
%% 4. Feature Extraction
FeatureExtraction();
%% 5. Dimensionality Analysis (PCA)
% Subject 1
DimensionalityAnalysis(1);
% Subject 2
DimensionalityAnalysis(2);
% Full Dataset
DimensionalityAnalysis(0);
%% 6.1.a. Stepwise Regression Models with Subject 1 and 2 Training Sets
predictorVars = [data.ST; ...
data.DT; ...
data.PIR; ...
data.PPG_k; ...
data.dppgH; ...
data.dppgW; ...
data.ddppgH; ...
data.ddppgW; ...
data.ddppgPH; ...
data.ddppgFH; ...
data.heartRate; ...
data.AI; ...
data.LASI; ...
data.S1; ...
data.S2; ...
data.S3; ...
data.S4; ...
data.IPA; ...
data.pat_p; ...
data.pat_f; ...
data.pat_d ]';
SBPresponse = [data.SBP]';
[bSBP, seSBP, pvalSBP, finalModelSBP, statsSBP] = stepwisefit(predictorVars, SBPresponse);
DBPresponse = [data.DBP]';
[bDBP, seDBP, pvalDBP, finalModelDBP, statsDBP] = stepwisefit(predictorVars, DBPresponse);
% b - estimated coefficients
% se - standard errors
% pval - p-values
% finalModel - logical vector indicating which terms are in the final model
% stats - additional statistics (including intercept)
disp(['The RMSE of the SBP model with subject 1 and 2 dataset is: ', num2str(statsSBP.rmse)])
disp(['The RMSE of the DBP model with subject 1 and 2 dataset is: ', num2str(statsDBP.rmse)])
%% 6.1.b. Stepwise Regression Models with Subject 1 and 2 Training Sets, and Cross-Validation
nLeftOut = length(data);
errorsSBP = zeros(nLeftOut, 1);
errorsDBP = zeros(nLeftOut, 1);
leftOutList = randperm(length(data), nLeftOut);
for k = 1 : length(leftOutList)
test = leftOutList(k);
predictorVars = [data.ST; ...
data.DT; ...
data.PIR; ...
data.PPG_k; ...
data.dppgH; ...
data.dppgW; ...
data.ddppgH; ...
data.ddppgW; ...
data.ddppgPH; ...
data.ddppgFH; ...
data.heartRate; ...
data.AI; ...
data.LASI; ...
data.S1; ...
data.S2; ...
data.S3; ...
data.S4; ...
data.IPA; ...
data.pat_p; ...
data.pat_f; ...
data.pat_d ]';
leftOutFeats = predictorVars(test, :);
predictorVars(test, :) = [];
SBPresponse = [data.SBP]';
SBPresponse(test) = [];
[bSBP, seSBP, pvalSBP, finalModelSBP, statsSBP] = stepwisefit(predictorVars, SBPresponse);
DBPresponse = [data.DBP]';
DBPresponse(test) = [];
[bDBP, seDBP, pvalDBP, finalModelDBP, statsDBP] = stepwisefit(predictorVars, DBPresponse);
testSBPestimate = stepwiseFittedModel(statsSBP.intercept, finalModelSBP, bSBP, leftOutFeats);
testDBPestimate = stepwiseFittedModel(statsDBP.intercept, finalModelDBP, bDBP, leftOutFeats);
errorsSBP(k) = data(test).SBP - testSBPestimate;
errorsDBP(k) = data(test).DBP - testDBPestimate;
end
errorsSBP = abs(errorsSBP);
errorsDBP = abs(errorsDBP);
% Root-mean-squared errors (RMSE)
rmseSBP = sqrt(sum(errorsSBP .^ 2) / length(errorsSBP));
rmseDBP = sqrt(sum(errorsDBP .^ 2) / length(errorsDBP));
disp(['The RMSE of the SBP model with subject 1 and 2 dataset after cross-validation is: ', num2str(rmseSBP)])
disp(['The RMSE of the DBP model with subject 1 and 2 dataset after cross-validation is: ', num2str(rmseDBP)])
% Mean absolute errors (MAE)
maeSBP = mean(errorsSBP);
maeDBP = mean(errorsDBP);
disp(['The MAE of the SBP model with subject 1 and 2 dataset after cross-validation is: ', num2str(maeSBP)])
disp(['The MAE of the DBP model with subject 1 and 2 dataset after cross-validation is: ', num2str(maeDBP)])
% Standard deviation (STD)
stdSBP = std(errorsSBP);
stdDBP = std(errorsDBP);
disp(['The STD of the SBP model with subject 1 and 2 dataset after cross-validation is: ', num2str(stdSBP)])
disp(['The STD of the DBP model with subject 1 and 2 dataset after cross-validation is: ', num2str(stdDBP)])
%% 6.2.a. Stepwise Regression Models with Subject 1 Training Set
sampleSubject = [data.subject];
dataset1 = [data(sampleSubject == 1)];
dataset2 = [data(sampleSubject == 2)];
predictorVars = [dataset1.ST; ...
dataset1.DT; ...
dataset1.PIR; ...
dataset1.PPG_k; ...
dataset1.dppgH; ...
dataset1.dppgW; ...
dataset1.ddppgH; ...
dataset1.ddppgW; ...
dataset1.ddppgPH; ...
dataset1.ddppgFH; ...
dataset1.heartRate; ...
dataset1.AI; ...
dataset1.LASI; ...
dataset1.S1; ...
dataset1.S2; ...
dataset1.S3; ...
dataset1.S4; ...
dataset1.IPA; ...
dataset1.pat_p; ...
dataset1.pat_f; ...
dataset1.pat_d ]';
SBPresponse = [dataset1.SBP]';
[bSBP, seSBP, pvalSBP, finalModelSBP, statsSBP] = stepwisefit(predictorVars, SBPresponse);
DBPresponse = [dataset1.DBP]';
[bDBP, seDBP, pvalDBP, finalModelDBP, statsDBP] = stepwisefit(predictorVars, DBPresponse);
disp(['The RMSE of the SBP model with subject 1 dataset is: ', num2str(statsSBP.rmse)])
disp(['The RMSE of the DBP model with subject 1 dataset is: ', num2str(statsDBP.rmse)])
%% 6.2.b. Subject 1 Training Set and Cross-validation
nLeftOut = length(dataset1);
errorsSBP = zeros(nLeftOut, 1);
errorsDBP = zeros(nLeftOut, 1);
leftOutList = randperm(length(dataset1), nLeftOut);
for k = 1 : length(leftOutList)
test = leftOutList(k);
predictorVars = [dataset1.ST; ...
dataset1.DT; ...
dataset1.PIR; ...
dataset1.PPG_k; ...
dataset1.dppgH; ...
dataset1.dppgW; ...
dataset1.ddppgH; ...
dataset1.ddppgW; ...
dataset1.ddppgPH; ...
dataset1.ddppgFH; ...
dataset1.heartRate; ...
dataset1.AI; ...
dataset1.LASI; ...
dataset1.S1; ...
dataset1.S2; ...
dataset1.S3; ...
dataset1.S4; ...
dataset1.IPA; ...
dataset1.pat_p; ...
dataset1.pat_f; ...
dataset1.pat_d ]';
leftOutFeats = predictorVars(test, :);
predictorVars(test, :) = [];
SBPresponse = [dataset1.SBP]';
SBPresponse(test) = [];
[bSBP, seSBP, pvalSBP, finalModelSBP, statsSBP] = stepwisefit(predictorVars, SBPresponse);
DBPresponse = [dataset1.DBP]';
DBPresponse(test) = [];
[bDBP, seDBP, pvalDBP, finalModelDBP, statsDBP] = stepwisefit(predictorVars, DBPresponse);
testSBPestimate = stepwiseFittedModel(statsSBP.intercept, finalModelSBP, bSBP, leftOutFeats);
testDBPestimate = stepwiseFittedModel(statsDBP.intercept, finalModelDBP, bDBP, leftOutFeats);
errorsSBP(k) = dataset1(test).SBP - testSBPestimate;
errorsDBP(k) = dataset1(test).DBP - testDBPestimate;
end
errorsSBP = abs(errorsSBP);
errorsDBP = abs(errorsDBP);
% Root-mean-squared errors (RMSE)
rmseSBP = sqrt(sum(errorsSBP .^ 2) / length(errorsSBP));
rmseDBP = sqrt(sum(errorsDBP .^ 2) / length(errorsDBP));
disp(['The RMSE of the SBP model with subject 1 dataset after cross-validation is: ', num2str(rmseSBP)])
disp(['The RMSE of the DBP model with subject 1 dataset after cross-validation is: ', num2str(rmseDBP)])
% Mean absolute errors (MAE)
maeSBP = mean(errorsSBP);
maeDBP = mean(errorsDBP);
disp(['The MAE of the SBP model with subject 1 dataset after cross-validation is: ', num2str(maeSBP)])
disp(['The MAE of the DBP model with subject 1 dataset after cross-validation is: ', num2str(maeDBP)])
% Standard deviation (STD)
stdSBP = std(errorsSBP);
stdDBP = std(errorsDBP);
disp(['The STD of the SBP model with subject 1 dataset after cross-validation is: ', num2str(stdSBP)])
disp(['The STD of the DBP model with subject 1 dataset after cross-validation is: ', num2str(stdDBP)])
%% 6.2.c. Subject 2 Testing Set
nTests = length(dataset2);
errorsSBP = zeros(nTests, 1);
errorsDBP = zeros(nTests, 1);
testSet = randperm(length(dataset2), nTests);
for k = 1 : length(testSet)
testFeats = [dataset2(testSet(k)).ST; ...
dataset2(testSet(k)).DT; ...
dataset2(testSet(k)).PIR; ...
dataset2(testSet(k)).PPG_k; ...
dataset2(testSet(k)).dppgH; ...
dataset2(testSet(k)).dppgW; ...
dataset2(testSet(k)).ddppgH; ...
dataset2(testSet(k)).ddppgW; ...
dataset2(testSet(k)).ddppgPH; ...
dataset2(testSet(k)).ddppgFH; ...
dataset2(testSet(k)).heartRate; ...
dataset2(testSet(k)).AI; ...
dataset2(testSet(k)).LASI; ...
dataset2(testSet(k)).S1; ...
dataset2(testSet(k)).S2; ...
dataset2(testSet(k)).S3; ...
dataset2(testSet(k)).S4; ...
dataset2(testSet(k)).IPA; ...
dataset2(testSet(k)).pat_p; ...
dataset2(testSet(k)).pat_f; ...
dataset2(testSet(k)).pat_d ]';
testSBPestimate = stepwiseFittedModel(statsSBP.intercept, finalModelSBP, bSBP, testFeats);
testDBPestimate = stepwiseFittedModel(statsDBP.intercept, finalModelDBP, bDBP, testFeats);
errorsSBP(k) = dataset2(testSet(k)).SBP - testSBPestimate;
errorsDBP(k) = dataset2(testSet(k)).DBP - testDBPestimate;
end
errorsSBP = abs(errorsSBP);
errorsDBP = abs(errorsDBP);
% Root-mean-squared errors (RMSE)
rmseSBP = sqrt(sum(errorsSBP .^ 2) / length(errorsSBP));
rmseDBP = sqrt(sum(errorsDBP .^ 2) / length(errorsDBP));
disp(['The RMSE of the SBP model with subject 1 dataset after testing with subject 2 samples is: ', num2str(rmseSBP)])
disp(['The RMSE of the DBP model with subject 1 dataset after testing with subject 2 samples is: ', num2str(rmseDBP)])
% Mean absolute errors (MAE)
maeSBP = mean(errorsSBP);
maeDBP = mean(errorsDBP);
disp(['The MAE of the SBP model with subject 1 dataset after testing with subject 2 samples is: ', num2str(maeSBP)])
disp(['The MAE of the DBP model with subject 1 dataset after testing with subject 2 samples is: ', num2str(maeDBP)])
% Standard deviation (STD)
stdSBP = std(errorsSBP);
stdDBP = std(errorsDBP);
disp(['The STD of the SBP model with subject 1 dataset after testing with subject 2 samples is: ', num2str(stdSBP)])
disp(['The STD of the DBP model with subject 1 dataset after testing with subject 2 samples is: ', num2str(stdDBP)])
%% 6.3.a. Stepwise Regression Models with Subject 2 Dataset
predictorVars = [dataset2.ST; ...
dataset2.DT; ...
dataset2.PIR; ...
dataset2.PPG_k; ...
dataset2.dppgH; ...
dataset2.dppgW; ...
dataset2.ddppgH; ...
dataset2.ddppgW; ...
dataset2.ddppgPH; ...
dataset2.ddppgFH; ...
dataset2.heartRate; ...
dataset2.AI; ...
dataset2.LASI; ...
dataset2.S1; ...
dataset2.S2; ...
dataset2.S3; ...
dataset2.S4; ...
dataset2.IPA; ...
dataset2.pat_p; ...
dataset2.pat_f; ...
dataset2.pat_d ]';
SBPresponse = [dataset2.SBP]';
[bSBP, seSBP, pvalSBP, finalModelSBP, statsSBP] = stepwisefit(predictorVars, SBPresponse);
DBPresponse = [dataset2.DBP]';
[bDBP, seDBP, pvalDBP, finalModelDBP, statsDBP] = stepwisefit(predictorVars, DBPresponse);
disp(['The RMSE of the SBP model with subject 2 dataset is: ', num2str(statsSBP.rmse)])
disp(['The RMSE of the DBP model with subject 2 dataset is: ', num2str(statsDBP.rmse)])
%% 6.3.b. Subject 2 Training Set and Cross-validation
nLeftOut = length(dataset2);
errorsSBP = zeros(nLeftOut, 1);
errorsDBP = zeros(nLeftOut, 1);
leftOutList = randperm(length(dataset2), nLeftOut);
for k = 1 : length(leftOutList)
test = leftOutList(k);
predictorVars = [dataset2.ST; ...
dataset2.DT; ...
dataset2.PIR; ...
dataset2.PPG_k; ...
dataset2.dppgH; ...
dataset2.dppgW; ...
dataset2.ddppgH; ...
dataset2.ddppgW; ...
dataset2.ddppgPH; ...
dataset2.ddppgFH; ...
dataset2.heartRate; ...
dataset2.AI; ...
dataset2.LASI; ...
dataset2.S1; ...
dataset2.S2; ...
dataset2.S3; ...
dataset2.S4; ...
dataset2.IPA; ...
dataset2.pat_p; ...
dataset2.pat_f; ...
dataset2.pat_d ]';
leftOutFeats = predictorVars(test, :);
predictorVars(test, :) = [];
SBPresponse = [dataset2.SBP]';
SBPresponse(test) = [];
[bSBP, seSBP, pvalSBP, finalModelSBP, statsSBP] = stepwisefit(predictorVars, SBPresponse);
DBPresponse = [dataset2.DBP]';
DBPresponse(test) = [];
[bDBP, seDBP, pvalDBP, finalModelDBP, statsDBP] = stepwisefit(predictorVars, DBPresponse);
testSBPestimate = stepwiseFittedModel(statsSBP.intercept, finalModelSBP, bSBP, leftOutFeats);
testDBPestimate = stepwiseFittedModel(statsDBP.intercept, finalModelDBP, bDBP, leftOutFeats);
errorsSBP(k) = dataset2(test).SBP - testSBPestimate;
errorsDBP(k) = dataset2(test).DBP - testDBPestimate;
end
errorsSBP = abs(errorsSBP);
errorsDBP = abs(errorsDBP);
% Root-mean-squared errors (RMSE)
rmseSBP = sqrt(sum(errorsSBP .^ 2) / length(errorsSBP));
rmseDBP = sqrt(sum(errorsDBP .^ 2) / length(errorsDBP));
disp(['The RMSE of the SBP model with subject 2 dataset after cross-validation is: ', num2str(rmseSBP)])
disp(['The RMSE of the DBP model with subject 2 dataset after cross-validation is: ', num2str(rmseDBP)])
% Mean absolute errors (MAE)
maeSBP = mean(errorsSBP);
maeDBP = mean(errorsDBP);
disp(['The MAE of the SBP model with subject 2 dataset after cross-validation is: ', num2str(maeSBP)])
disp(['The MAE of the DBP model with subject 2 dataset after cross-validation is: ', num2str(maeDBP)])
% Standard deviation (STD)
stdSBP = std(errorsSBP);
stdDBP = std(errorsDBP);
disp(['The STD of the SBP model with subject 2 dataset after cross-validation is: ', num2str(stdSBP)])
disp(['The STD of the DBP model with subject 2 dataset after cross-validation is: ', num2str(stdDBP)])
%% 6.3.c. Subject 1 test set
nTests = length(dataset1);
errorsSBP = zeros(nTests, 1);
errorsDBP = zeros(nTests, 1);
testSet = randperm(length(dataset1), nTests);
for k = 1 : length(testSet)
testFeats = [dataset1(testSet(k)).ST; ...
dataset1(testSet(k)).DT; ...
dataset1(testSet(k)).PIR; ...
dataset1(testSet(k)).PPG_k; ...
dataset1(testSet(k)).dppgH; ...
dataset1(testSet(k)).dppgW; ...
dataset1(testSet(k)).ddppgH; ...
dataset1(testSet(k)).ddppgW; ...
dataset1(testSet(k)).ddppgPH; ...
dataset1(testSet(k)).ddppgFH; ...
dataset1(testSet(k)).heartRate; ...
dataset1(testSet(k)).AI; ...
dataset1(testSet(k)).LASI; ...
dataset1(testSet(k)).S1; ...
dataset1(testSet(k)).S2; ...
dataset1(testSet(k)).S3; ...
dataset1(testSet(k)).S4; ...
dataset1(testSet(k)).IPA; ...
dataset1(testSet(k)).pat_p; ...
dataset1(testSet(k)).pat_f; ...
dataset1(testSet(k)).pat_d ]';
testSBPestimate = stepwiseFittedModel(statsSBP.intercept, finalModelSBP, bSBP, testFeats);
testDBPestimate = stepwiseFittedModel(statsDBP.intercept, finalModelDBP, bDBP, testFeats);
errorsSBP(k) = dataset1(testSet(k)).SBP - testSBPestimate;
errorsDBP(k) = dataset1(testSet(k)).DBP - testDBPestimate;
end
errorsSBP = abs(errorsSBP);
errorsDBP = abs(errorsDBP);
% Root-mean-squared errors (RMSE)
rmseSBP = sqrt(sum(errorsSBP .^ 2) / length(errorsSBP));
rmseDBP = sqrt(sum(errorsDBP .^ 2) / length(errorsDBP));
disp(['The RMSE of the SBP model with subject 2 dataset after testing with subject 1 samples is: ', num2str(rmseSBP)])
disp(['The RMSE of the DBP model with subject 2 dataset after testing with subject 1 samples is: ', num2str(rmseDBP)])
% Mean absolute errors (MAE)
maeSBP = mean(errorsSBP);
maeDBP = mean(errorsDBP);
disp(['The MAE of the SBP model with subject 2 dataset after testing with subject 1 samples is: ', num2str(maeSBP)])
disp(['The MAE of the DBP model with subject 2 dataset after testing with subject 1 samples is: ', num2str(maeDBP)])
% Standard deviation (STD)
stdSBP = std(errorsSBP);
stdDBP = std(errorsDBP);
disp(['The STD of the SBP model with subject 2 dataset after testing with subject 1 samples is: ', num2str(stdSBP)])
disp(['The STD of the DBP model with subject 2 dataset after testing with subject 1 samples is: ', num2str(stdDBP)])
disp(['The RMSE of the SBP model is: ', num2str(statsSBP.rmse)])
disp(['The RMSE of the DBP model is: ', num2str(statsDBP.rmse)])