Issue with FFT/Power Spectral Density

Question

Sebastian Daneli 2021-11-4

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1579132-issue-with-fft-power-spectral-density

评论： Paul 2021-11-8

ecg_stress_test.mat

Can someone explain what I'm doing wrong here? I wanna evaluate the PSD of ECG data (electrocardiogram), but I end up with this enormous peak in the fourier domain that I don't know what to do with. Where does this come from? What to do about it?

clear

load ecg_stress_test.mat

n=length(ecg_data);

fhat=fft(ecg_data,n);

PSD=fhat.*conj(fhat)/n;

figure

subplot(2,1,1), plot(ecg_data), xlim([0 length(ecg_data)])

subplot(2,1,2), plot(PSD), xlim([0 length(PSD)])

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Answer 1

Star Strider 2021-11-5

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1579132-issue-with-fft-power-spectral-density#answer_824674

在 MATLAB Online 中打开

Sebastian Daneli ecg_stress_test.mat

Subtract the mean of the EKG data before calculating the fft. Also, it likely needs to be rescaled, because the amplitude should be between ±1 mV.

The signal needs to be filtered. Some of the noise is broadband, however much can be filtered with a frequency-selective filte —

LD = load('Sebastian Daneli ecg_stress_test.mat')

LD = struct with fields:

ecg_data: [450001×1 double] fs: 250

EKG = LD.ecg_data;

Fs = LD.fs;

L = numel(EKG);

t = linspace(0, 1, L)/Fs;

[EKGfilt,df] = bandpass(EKG, [1 25], Fs, 'ImpulseResponse','iir');

figure

subplot(3,1,1)

plot(t, EKG)

grid

subplot(3,1,2)

idx = 200000:201000;

plot(t(idx), EKG(idx))

grid

subplot(3,1,3)

idx = 200000:201000;

plot(t(idx), EKGfilt(idx))

grid

Fn = Fs/2;

NFFT = 2^nextpow2(L);

FTEKGfilt = fft(EKGfilt,NFFT)/L;

Fv = linspace(0, 1, NFFT/2+1)*Fn;

Iv = 1:numel(Fv);

[ampmax,idx] = max(abs(FTEKGfilt(Iv))*2);

fprintf('Amplitude = %.2f mV at %.2f Hz',ampmax,Fv(idx))

Amplitude = 1.20 mV at 1.92 Hz

figure

plot(Fv, abs(FTEKGfilt(Iv))*2)

grid

The pwelch function is probably the mose frequently used method to determine the power spectral density. To see the the signal spectrum and power spectrum as a function of time, use the pspectrum or spectrogram functions.

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