butterworth and baseline removal filtering

Question

yasaman 2023-2-23

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1917660-butterworth-and-baseline-removal-filtering

编辑： Star Strider 2023-2-23

Hello. I want to apply butterworth and baseline wandering removal filter on ECG signal. I searched mathworks and found this solution;

does anybody knows what are this variables? such as{ a, b ,Wn , N }

and why the formula of Wn is diffrent :Wn = 12*2/f and Wn=[f1 f2]*2/fs ?

I didn't understand the meaning of the comments

% ======================= start filtered ============================= %%
Wn = 12*2/fs;
N = 3;                                                                  % order of 3 less processing
[a,b] = butter(N,Wn,'low');                                             % bandpass filtering
ecg_l = filtfilt(a,b, signal); 
%%%% baseline wander filter
f1=0.5;                                                                      % cuttoff low frequency to get rid of baseline wander
f2=15;                                                                     % cuttoff frequency to discard high frequency noise
Wn=[f1 f2]*2/fs;                                                           % cutt off based on fs
N = 3;                                                                     % order of 3 less processing
[a,b] = butter(N,Wn);                                                      % bandpass filtering
ecg_new = filtfilt(a,b, signal);

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Answer 1

Star Strider 2023-2-23

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1917660-butterworth-and-baseline-removal-filtering#answer_1178395

编辑：Star Strider 2023-2-23

The ‘baseline wander filter’ is a bandpass filter with a passband of 0.5 to 15 Hz. (This is too restrictive in my opinion. The upper passband should likely be about 45 Hz.)

To get the appropriate arguments for butter, first use the buttord function.

Also, if you design such a filter, the appropriate butter call is:

[z,p,k] = butter(n,Wn);

[sos,g] = zp2sos(z,p,k);

(to be certain that the filter is stable, use zp2sos to create a second-order-section implementation), then:

ecg_filt = filtfilt(sos, g, signal);

to filter it.

I prefer elliptic (ellipord, ellip) filters for their computational efficiency.

EDIT — Corrected typographical errors.

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