MATLAB: How to untilt the graphed data

datagraphimage analysisimage processing

Hello guys.

So I had some respiratory flow rate vs. time. I needed to find to graph volume of ventilation vs. time so I took the running integral of the flow rate data. However, taking this running integral results in volume data that is tilted. I need to untilt the data before analyzing it but I was unsure how to do it. The help would be greatly appreciated. I used cumtrapz to integrate the flow rate data.

Also, I attached an image of the graph as it may help.

Best Answer

It looks as though you are integrating a constant as well as your data. I don’t have your data so I can only suggest possible approaches.

The easiest approach would be to subtract the mean of ‘flow_data’ from ‘flow_data’ before you do the integration.

A more complicated approach would be to use a highpass or bandpass filter on the integrated data to eliminate the descending baseline.

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: Zeros on [b,a] output of butter filter

The filter is unstable. This can easily be remedied by converting to zero-pole-gain realization, and then second-order section implementation:

ftype = 'low';
fs = 32000
fn = fs/2;                                %(fs = 32000)
fc = 125/fn;                              %normalised passband frequency = 0.0078
fz = 135/fn;                              %normalised stopband frequency = 0.0084
[n, wn] = buttord(fc,fz,3,60);            %(n = 60, wn = 0.0078)
[z,p,k] = butter(n, wn, ftype);         % Use Zero-Pole-Gain Representation
[sos,g] = zp2sos(z,p,k);                % Second-Order Section For Stability
figure
freqz(sos, 2^14, fs)                        % Filter Bode Plot

Then use:

signal_filtered = filtfilt(sos,g,signal);

to do the actual filtering.

Best Answer

Related Solutions

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

MATLAB: Zeros on [b,a] output of butter filter

Related Question