MATLAB: Problem using bandpass IIR filter

bandpasseegiir

Hello, I am trying to use a bandpass IIR filter to filter by EEG data which is collected at a sampling rate of 1000. I am using the following code to do it, bpfilter = designfilt('bandpassiir', 'StopbandFrequency1',5,'PassbandFrequency1', 8, 'PassbandFrequency2', 35,'StopbandFrequency2', 40, 'StopbandAttenuation1', 60, 'PassbandRipple', 1, 'StopbandAttenuation2', 60, 'SampleRate', 1000);

But after filtering and plotting the spectrum I am getting a straight line parallel to the x-axis and zero amplitude. Whereas when I used FIR, I obtained a good spectrum. It is just that since my amplitudes matter hence I don't want to use an FIR filter due to its undulations in bandpass frequencies.

Best Answer

I would normally suggest using the filtfilt function, however the filters returned by designfilt appear to be more appropriate to the filter function (as the documentation recommends). That appeared to work well when I used it with a signal I synthesized to test it.

Try this:

FilteredEKG = filter(bpfilter, EKG);

with appropriate changes to use your variables.

Related Solutions

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.)

MATLAB: Designing a higher order lowpass filter to avoid Gibbs phenomenon

I could not get that code to work.

Try this:

Fs = 2000;                                                  % Sampling Frequency (Hz)
Fn = Fs/2;                                                  % Nyquist Frequency (Hz)
Wp = 400/Fn;                                                % Normalised Passband (Passband = 5 Hz To 40 Hz)
Ws = 450/Fn;                                                % Normalised Stopband (Passband = 2 Hz To 45 Hz)
Rp =  1;                                                    % Passband Ripple/Attenuation
Rs = 60;                                                    % Stopband Ripple/Attenuation
[n,Wp] = ellipord(Wp, Ws, Rp, Rs);                          % Calculate Elliptic Filter Optimum Order
[z,p,k] = ellip(n, Rp, Rs, Wp,'low');                       % Elliptic Filter
[sos,g] = zp2sos(z,p,k);                                    % Second-Order-Section For Stability
figure
freqz(sos, 2^26, Fs)

To use it with filtfilt:

y_filtered = filtfilt(sos, g, y);

Best Answer

Related Solutions

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

MATLAB: Designing a higher order lowpass filter to avoid Gibbs phenomenon

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