How to remove the noisy part of a heartbeat?

Question

I have a large pulse oxymetery signal. Some part of that is noisy and will corrupt my data if I use it. Do you have any strategy to automatically remove the noisy part? (Since the data is very long and there are many channels, I can't really do it manually).

Please find the picture attached to have a feeling of the signal.

Answer 1

You can filter it but you need to know the spectral characteristics of the signal so you can extract it or the spectral characteristics of the noise so you can suppress it. Do you have a signal that doesn't have noise or do you know where in the spectrum your signal of interest lies?

Answer 2

This might be the problem identical to removing outliers from time series. This problem can be solved by fitting the time series with a given model as shown in the this link. For example, try the following simulation codes.

xdata = (0:0.1:2*pi)';
y0 = sin(xdata);                % pure data
gnoise = y0.*randn(size(y0));   % noise component
ydata = y0 + gnoise;   

f = fittype('a*sin(b*x)');
fit1 = fit(xdata,ydata,f,'StartPoint',[1 1]);

plot(fit1,'r-',xdata,ydata,'k.',outliers,'m*')
xlim([0 2*pi])