Cut a signal into heart cycle pieces

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Hi guys,
I have a signal from a photophlethysmographic (PPT) sensor showing contours of the heart pulse.
My signal needs to be cut/subdivided into signal pieces for a single heart beat. Each single heart beats needs to be saved in the workspace.
So when the slope of the signal is getting values different than 0, the data should be cut and saved to workspace.
Is there any possibilities I could do so ? Does anybody have suggestions ?
  3 Comments
John D'Errico
John D'Errico on 3 Jan 2019
Edited: John D'Errico on 3 Jan 2019
This is not an answer, since I don't have your data signal, nor even the signal processing toolbox. But since it looks like you want to use the local minima to break the signal up, just use findpeaks.
  1. Find the locations of each local minimum. help findpeaks
  2. Use those locations to break your signal into pices, probably as independent cells orf a cell array.
  3. Subtract off the local average value for each section. This will detrend the problem, helping them to now overlap.
You do NOT want to create the segments as separate variables in your workspace. That is a particular place in programming hell where you do not want to live. Instead, learn to use arrays, and here, cell arrays or even structures.

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Accepted Answer

Chris Turnes
Chris Turnes on 3 Jan 2019
I don't have your data, but using something that looks roughly similar (attached), here's an approach. Furthering John's comment, if you have access to R2017b or beyond, you can use islocalmin to find the minima.
>> tf = islocalmin(x, 'FlatSelection', 'center');
>> t = 1:length(x);
>> plot(t, x, t(tf), x(tf), 'rx')
>> legend('Input', 'Local minima')
locmin.png
Once you have them, you can do a cute little trick to segment the signal: you can call cumsum to get unique labels for each heartbeat:
>> glabels = 1 + cumsum(tf);
With these labels, you can uniquely identify any individual heartbeat you've detected without having to explicitly construct each one. For example, to plot the 4th heartbeat you've segmented:
>> idxFourth = glabels == 4;
>> plot(t, x, t(idxFourth), x(idxFourth));
>> legend('All', 'Fourth')
fourth.png
You could also mean-center each heartbeat as John suggests by using the grouptransform function of R2018b (though you'll need to pack your data into a table):
>> Tx = table(x', glabels', 'VariableNames', { 'Data', 'Heartbeat' });
>> Txmc = grouptransform(Tx, 'Heartbeat', 'meancenter');
>> plot(t, Txmc.Data)
meancenter.png
  4 Comments
Chris Turnes
Chris Turnes on 7 Jan 2019
grouptransform was introduced in R2018b, unfortunately, so it seems you won't have access to that function. An alternative approach is to simply loop through the number of groups you have and manually re-normalize using indexing. For example, something like
for k = 1:max(glabels)
thisGroup = D(glabels == k);
D(glabels == k) = thisGroup - mean(thisGroup);
end

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