Combining repetitive curves into one average curve

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Dhakshenan Pushparajan 2021-2-16

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https://ww2.mathworks.cn/matlabcentral/answers/747252-combining-repetitive-curves-into-one-average-curve

评论： Star Strider 2021-2-24

So I have this curve that I obtained/created from mulitple discrete time signals. I was wondering if it was possible to combine these curves into one average curve? I need to do some calculations based on amplitude, peak width, etc. and it will be easier if the curves were averaged. The figure only shows 4 waves but theres approximately 1000 in the full dataset.

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dpb 2021-2-20

Same as I outlined above -- once you find the breakpoints, iterate through those pairs and then interpolate them over a common range to average.

Dhakshenan Pushparajan 2021-2-20

Thank you! I will try that ASAP

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Answer 1

Star Strider 2021-2-20

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https://ww2.mathworks.cn/matlabcentral/answers/747252-combining-repetitive-curves-into-one-average-curve#answer_628814

One option is to do something similar to the approach in How do I find and plot the average of action potentials from a trace?

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Star Strider 2021-2-24

在 MATLAB Online 中打开

Try this:

y = load('clean sample.txt');
t = linspace(0, numel(y), numel(y))/1000;                           % Assume 1kHz Sampling Frequency (Supply Correct Time Vector)
Ts = mean(diff(t));
Fs = 1/Ts;
FTy = fft(y-mean(y))/numel(y);                                      % Calculate & Plot Fourier Transform
Fv = linspace(0, 1, numel(t)/2+1)*Fs/2;
Iv = 1:numel(Fv);
figure
plot(Fv, abs(FTy(Iv))*2)
grid
xlim([0 30])
xlabel('Frequency')
ylabel('Amplitude')
yf = highpass(y, 0.001*Fs, Fs);                                     % Eliminate Baseline Variation
[pks,plocs] = findpeaks(yf, 'MinPeakProminence',0.5);               % Peaks & Location Indices
[vls,vlocs] = findpeaks(-yf, 'MinPeakProminence',0.5);              % Valleys & Location Indices
figure
plot(t, yf)                                                         % Detail Of Original Waveform
hold on
plot(t(plocs),pks,'^r') 
hold off
xlim([0 2.5])
xlabel('Time (s)')
ylabel('Amplitude (Unit)')
Vidx = mean(diff(vlocs));
for k = 1:numel(pks)
    if (plocs(k)+320) <= numel(t)
        idxrng = max(1,plocs(k)-55):min(numel(t),plocs(k)+320);
        ensb(k,1:numel(idxrng)) = yf(idxrng);
    end
end
figure
plot((1:numel(idxrng)), ensb)
grid
xlabel('Time (s)')
ylabel('Amplitude (Unit)')
ensb_mean = mean(ensb);
ensb_std = std(ensb);
tci = tinv(0.975,size(ensb,1)-1);                                   % 95% Critical t-Statistic
err = tci*ensb_std/sqrt(size(ensb,1));                              % 95% Confidence Intervals
figure
plot(t(1:numel(ensb_mean)), ensb_mean, '-b')
hold on
plot(t(1:numel(ensb_mean)), ensb_mean+err, '--r')
plot(t(1:numel(ensb_mean)), ensb_mean-err, '--r')
hold off
grid
xlabel('Time (s)')
ylabel('Amplitude (Unit)')
legend('Ensemble Average', '±95% Confidence Intervals')

producing:

.

Dhakshenan Pushparajan 2021-2-24

Thank you so much!! This is perfect

If you don't mind me asking, how did you get so good at MATLAB. I'm just starting out and want to improve

Star Strider 2021-2-24

As always, my pleasure!

I have been using MATLAB since the early 1990s, and have been involved in Answers for about the last 7 or 8 years. In addition to writing code for my own applications, working with Answers problems has been a benefit to me, because it allowed me to experiment and learn.

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