Signal Quality Indicators for breathing signal

Table of Contents

Acknowledgements Introduction Loading breathing data SQI Loading signal after exercise Loading irregular breathing signal Exersizes

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details <https://www.gnu.org/licenses/>.

This code was developed by Miodrag Bolic for the book PERVASIVE CARDIAC AND RESPIRATORY MONITORING DEVICES: https://github.com/Health-Devices/CARDIAC-RESPIRATORY-MONITORING

Dependencies: files PEX_T1.mat, Free_T1.mat and Irregular_T1.mat. Code: br_rate_est.m

Acknowledgements

This notebook uses data from the database described in the paper: Shafiq, G., Veluvolu, K. Multimodal chest surface motion data for respiratory and cardiovascular monitoring applications. Sci Data 4, 170052 (2017). https://doi.org/10.1038/sdata.2017.52

% Changing the path from main_folder to a particular chapter
main_path=fileparts(which('Main_Content.mlx'));
if ~isempty(main_path)
    %addpath(append(main_path,'/Chapter2'))
    cd (append(main_path,'/Chapter9/Signal Quality'))
    addpath(append(main_path,'/Service'))
end
SAVE_FLAG=0; % saving the figures in a file

Introduction

In this notebook, we are going to load respiratory belt and temperature sensor data and to show the waveforms as well as to compute several signal quality indicators.

Loading breathing data

% Loading breathing data obtained after exercize

load('PEX_T1.mat')

figure

fs=100;

lim=105*fs:115*fs;

t=1/fs:1/fs:length(mp36_s(1,:))/fs;

[m,i_max]=findpeaks(mp36_s(4,lim));

[m,i_min]=findpeaks(-mp36_s(4,lim));

i_all=[i_min,i_max];

%plot(t/60,mp36_s(1,:))

subplot(211)

hold on

plot(t(lim)-t(lim(1)),smooth(mp36_s(2,lim),25))

for i=1:length(i_all)

plot([i_all(i)/fs, i_all(i)/fs],ylim.*ones(1,2),'b')

end

hold off

text(i_min/fs,-1*ones(1,length(i_min)),'Expiration')

text(i_max(1:end-1)/fs,ones(1,length(i_max(1:end-1))),'Inspiration')

xlabel('Time(s)')

ylabel('Amplitude(mV)')

title('Breathing signal from the respiratory belt')

subplot(212)

hold on

plot(t(lim)-t(lim(1)),mp36_s(4,lim))

xlabel('Time(s)')

ylabel('\DeltaT (degrees)')

title('Breathing signal from the temperature sensor')

for i=1:length(i_all)

plot([i_all(i)/fs, i_all(i)/fs],ylim.*ones(1,2),'b')

end

hold off

exportgraphics(gcf,"Fig9.3.jpg", 'Resolution',600)

%text(i_min/fs,ylim*ones(1,length(i_min)),'expiration')

%text(i_max/fs,-ylim*ones(1,length(i_max)),'inspiration')

SQI

Loading signal after exercise

load('PEX_T1.mat')
%load('Free_T1.mat')
t=1/fs:1/fs:length(mp36_s(1,:))/fs;

resp=mp36_s(4,:);

% Define frequency ranges of interest fot the signal

Fmask_low_sig=br_rate_est(resp',fs)/60-0.1;

Fmask_high_sig=br_rate_est(resp',fs)/60+0.1;

Fmask_low_all=0.05;

Fmask_high_all=1;

% Compute periodogram

NyquistF = fs/2;

FResBPM = 0.5;

Nfft = (60*2*NyquistF)/FResBPM; %number of bins in power spectrum

[Pxx,F] = periodogram(resp,hamming(length(resp)),Nfft,fs);

% Compute power in the frequency band of the signal of interest

FMask1 = (F >= Fmask_low_sig)&(F <= Fmask_high_sig);

PowerSig = sum(Pxx(FMask1));

% Compute power in the total frequency band

FMask2 = (F >= Fmask_low_all)&(F <= Fmask_high_all);

PowerAll = sum(Pxx(FMask2));

% Compute signal quality indices = ratio of the power in the

PowerRatio=PowerSig/PowerAll;

if PowerRatio < 0.9 && PowerRatio > 0.5

pSQI=1

else

pSQI=0

end

pSQI = 1

figure

% subplot(211)

% plot(t,mp36_s(2,:))

% xlabel('Time(s)')

% ylabel('Amplitude(mV)')

% title('Signal from respiratory belt')

plot(t,mp36_s(4,:))

xlabel('Time(s)')

ylabel('\DeltaT (degrees)')

title('Breathing signal from the temperature sensor after exercise')

annonation_save('a)',"Fig9.4a.jpg", SAVE_FLAG);

figure

[normalizedACF, lags] = autocorr(resp, 'NumLags',1000,'NumSTD',2);

plot(lags/fs,normalizedACF)

xlabel('Time (s)')

ylabel('Sample autocorrelation')

title('Sample autocorrelation function of the signal in Fig9.4a)')

ylim([-1,1])

annonation_save('b)',"Fig9.4b.jpg", SAVE_FLAG);

Loading irregular breathing signal

load('Irregular_T1.mat')
t=1/fs:1/fs:length(mp36_s(1,:))/fs;

resp=mp36_s(4,:);

% Define frequency ranges of interest fot the signal

Fmask_low_sig=br_rate_est(resp',fs)/60-0.1;

Fmask_high_sig=br_rate_est(resp',fs)/60+0.1;

Fmask_low_all=0.05;

Fmask_high_all=1;

% Compute periodogram

NyquistF = fs/2;

FResBPM = 0.5;

Nfft = (60*2*NyquistF)/FResBPM; %number of bins in power spectrum

[Pxx,F] = periodogram(resp,hamming(length(resp)),Nfft,fs);

% Compute power in the frequency band of the signal of interest

FMask1 = (F >= Fmask_low_sig)&(F <= Fmask_high_sig);

PowerSig = sum(Pxx(FMask1));

% Compute power in the total frequency band

FMask2 = (F >= Fmask_low_all)&(F <= Fmask_high_all);

PowerAll = sum(Pxx(FMask2));

% Compute signal quality indices = ratio of the power in the

PowerRatio=PowerSig/PowerAll;

if PowerRatio < 0.9 && PowerRatio > 0.5

pSQI=1

else

pSQI=0

end

pSQI = 0

figure

% subplot(211)

% plot(t,mp36_s(2,:))

% xlabel('Time(s)')

% ylabel('Amplitude(mV)')

% title('Signal from respiratory belt')

plot(t,mp36_s(4,:))

xlabel('Time(s)')

ylabel('\DeltaT (degrees)')

title('Signal from the temperature sensor during irregular breathing')

annonation_save('c)',"Fig9.4c.jpg", SAVE_FLAG);

figure

[normalizedACF, lags] = autocorr(resp, 'NumLags',1000,'NumSTD',2);

plot(lags/fs,normalizedACF)

xlabel('Time (s)')

ylabel('Sample autocorrelation')

title('Sample autocorrelation function of the signal in Fig9.4c)')

ylim([-1,1])

annonation_save('d)',"Fig9.4d.jpg", SAVE_FLAG);

Exersizes

Excersize 1: Show ACF for cases when respiratory belt waveform is selected instead of thermal sensor waveform.

Excersize 2: Instead of loading waveform obtained after exercise from the file 'PEX_T1.mat', load the waveform 'Free_T1.mat' that corresponds to the free breathing signal. Compute PowerRatio and show ACF. Why is the quality lower than in the case of the waveform after exercise?

Excersize 3: How does the length of the signal affects the PowerRatio and ACF. Reduce signals to 60 sec.