Iam not certain what result you want, however to get the smallest amplitude change, using gradient on the original signal is likely not going to produce the result you want, since the minimum gradient is going to be the minimum of the entire record, likely returning the value of the deepest Q- or S-deflection. Taking the gradient of the abolute value instead may do what you want.
Try this —
LD = load(websave('A','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1112930/A.mat'));
data = LD.A;
Fs = 1024;
L = numel(data)
t = linspace(0, L-1, L)/Fs;
Ft = gradient(data, 1/Fs);
[minFt,idx] = min(abs(Ft)) % the smallest change in the signal's amplitude
minidx = find(Ft == min(abs(Ft)))
Ft2 = gradient(Ft); % Second Derivative
resolution = diff(data);
min_resolution = min(resolution(resolution>0))
figure
subplot(2,1,1)
plot(t, data, 'DisplayName','Data')
hold on
plot(t(minidx), data(minidx), '+r', 'DisplayName','Gradient = 0')
hold off
grid
title('Original')
legend('Location','best')
xlim([0, 10])
subplot(2,1,2)
plot(t, Ft, 'DisplayName','Gradient')
grid
title ('Derivative')
xlim([0, 10])
That returns several values of a flat gradient, all representing inflection points in the EKG trace. Calculating the second derivative will allow the determination of the inflection points being a minimum, maximum, or saddle point. This may be helpful, however I am not certain what you want.
The minimum resolution appears to be equal to 1, however that likely needs to be calibrated with respect to the actual voltage values of the EKG (the R-deflection is normally about 1±0.2 mV) to be useful. It will likely be necessary to check the gain on the instrumentation.
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