Unforutnately, that is broadband, relatively high-amplitude noise that completely obscures the underlying EKG signal in that part of the record. There is simply no way (that I am aware of) to recover it, without removing essential parts of the EKG itself. If the noise amplitude were lower, so that it did not completely obscure the underlying EKG signal, then it might be possible to recover at least the R-deflections and possibly more, using either wavelet denoising or the Savitzky-Golay filter. However here that is not the situation.
My analysis (done offline since the online Run feature has problems with .mat files) —
LD = load('Siria Fumagalli EKG.mat');
t = LD.EKG(1,:);
EKG = LD.EKG(2,:);
L = numel(t);
Fs = 1/mean(diff(t));
Fn = Fs/2;
NFFT = 2^nextpow2(L);
FTEKG = fft(EKG - mean(EKG), NFFT) / L;
Fv = linspace(0, 1, NFFT/2+1)*Fn;
Iv = 1:numel(Fv);
figure
plot(Fv, abs(FTEKG(Iv))*2)
grid
xlabel('Frequency (Hz)')
ylabel('Amplitude')
xlim([0 15])
EKGFilt = lowpass(EKG, 0.2, Fs, 'ImpulseResponse','iir');
figure
subplot(2,1,1)
plot(t, EKG)
grid
xlabel('t (s)')
ylabel('Amplitude')
subplot(2,1,2)
plot(t, EKGFilt)
grid
xlabel('t (s)')
ylabel('Amplitude')
You have my sympathies.
.
