Be able to Detect Breathing

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David Jones 2021-5-27

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评论： Mathieu NOE 2021-6-1

I dont have a great deal of experiance in matlab and would like some advice on detecting breathing,the samples taken in the figures attached are raw ADC inputs to matlab with no filtering , is there firstly something I could do to improve the waveforms? (pre filtering)

Also what is the best method (most reliable) to detect there is breathing and the rate of the breathing.

figures attached baseline(no one on the bed), Breathing (normal) and breathing2(shallow)

Thank you in advance for any help you can offer.

Kind Regards

David

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Mathieu NOE 2021-5-27

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hello David

yes you can clear tell that the bed is not empty and that someone is actually breathing by either saying the time signal has amplitude above 100 (so let's compute a smoothed rectified version of this signal) , this can be also observed in the frequency spectrum has you have peaks showing up between 0.1 and 1 Hz

to compite the rate of breathing you can either pick the highest amplitude peak in the fft spectrum (and find it's corresponding frequency) or you can compute the period between two zero crossing values (works also with a non zero threshold) as example attached shows

hope it helps

all the best

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Mathieu NOE 2021-5-27

在 MATLAB Online 中打开

now this is an improved version of my code, assuming between two samples we have dt = 100 seconds

I added some high frequency filtering to avoid the zero crossing function to be "fooled" by the noise

then I computed the time interval between succesive positive slope crossing points (therefore you can get the frequency too

I have the feeling I don't have the right sampling rate here , 100 seconds between two samples seems very long for me

i also lifted the threshold to 100, so when there is no signal you would get no points

clc
clearvars
load('Raw_Data.mat')
y = double(raw_ad_data_sine); % conversion to double
n=length(y);
dt = 100;
x=(1:n)*dt;
threshold = 100; % your value here
% remove first some high frequency noise
yy = smoothdata(y,'gaussian',10);
[t0_pos,s0_pos,t0_neg,s0_neg]= crossing_V7(yy,x,threshold,'linear'); % positive (pos) and negative (neg) slope crossing points 
% ind => time index (samples)
% t0 => corresponding time (x) values 
% s0 => corresponding function (y) values , obviously they must be equal to "threshold"
figure(1)
plot(x,yy,t0_pos,s0_pos,'+r',t0_neg,s0_neg,'+g','linewidth',2,'markersize',12);grid on
legend('signal','positive slope crossing points','negative slope crossing points');
xlabel('Time (s)');
% let's compute the period between crossing points , then frequency 
period_pos = diff(t0_pos); % seconds
freq_pos = 1./period_pos; % Hz
figure(2)
plot(t0_pos(2:end),period_pos,'+r','linewidth',2,'markersize',12);grid on
xlabel('Time (s)');
ylabel('period (s)');
figure(3)
plot(t0_pos(2:end),freq_pos,'+r','linewidth',2,'markersize',12);grid on
xlabel('Time (s)');
ylabel('Frequency (Hz)');

David Jones 2021-6-1

Great thanks for all your help

Mathieu NOE 2021-6-1

my pleasure !

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