MATLAB: How to normalize and re-sample ECG signal

biomedical signal processingecg signal processingphysionet

There is an ECG signal I want to normalize and re-sample at 200Hz. The signal is stored in first row of 16265m.mat

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I have no idea what you mean by ‘normalize’. When I looked it up, it seemed relatively noise-free, and the baseline was smooth and not offset. The sampling frequency is 128 Hz, so the easiest way to resample it is to use the Signal Processing Toolbox resample function:

y = resample(x, 200, 128);

The resample function incorporates filtering of the resampled signal, so I prefer it for signal processing.

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MATLAB: How to implement Pass Band Filter

The 200 Hz sampling frequency was the reason they could use the 2-40 Hz bandpass filter. If you’re dealing with atrial or ventricular tachyarrhythmias, and you need a 2-100 Hz passband, you will have to resample your signals at greater than 200 Hz. I would use 256 Hz, since that is a relatively common sampling frequency for EKGs, so you may be able to use some of them without resampling them at all.

MATLAB: How to Butterworth Filter with Bandpass [10 500] with sampling rate 1000

You have to divide your passband and stopband frequencies by the Nyquist frequency to normalise them:

Fs = 1000;                                          % Sampling Frequency
Fn = Fs/2;                                          % Nyquist Frequency
Wp = [10  495]/Fn;                                  % Norrmalised Passband Frequencies
Ws = [5   499]/Fn;                                  % Normalised Stopband Frequencies
Rp =  1;                                            % Passband Ripple (dB)
Rs = 25;                                            % Stopband Ripple (dB)
[n,Wn] = buttord(Wp, Ws, Rp, Rs);                   % Optimal Filter Order
[b,a] = butter(n, Wn);                              % Calculate Filter Coefficients
[sos,g] = tf2sos(b,a);                              % Convert To Second-Order Sections For Stability
figure(1)
freqz(sos, 4096, Fs)                                % Filter Bode Plot

You cannot have any frequency exactly at zero or exactly at the Nyquist frequency, so I designed your passband to be close enough to them to be stable. In your filter, you can also use a high-pass design with a lower cutoff of 10 Hz, since your upper passband is at the Nyquist frequency. The passband and stopband ripple values are necessary to specify in the design but irrelevant to a Butterworth filter. These are acceptable values for most filter designs (for example Chebyshev) that require them.

You would use the ‘sos’ and ‘g’ variables in your actual filter:

filtered_signal = filtfilt(sos, g, signal);

(This design works well in R2016a and should also work in earlier releases.)

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MATLAB: How to implement Pass Band Filter

MATLAB: How to Butterworth Filter with Bandpass [10 500] with sampling rate 1000

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