Correcting for baseline drift and determining the isoelectric line of an ECG

I'll preface this question by saying that I'm a relative novice at Matlab. I'm extracting signal data from ECG PDFs using 'imread', converting the image into the xy coordinate plane, then attempting to determine the integral of the P, QRS, and T waves. However, I'm having trouble with baseline deviation which is also making it difficult to determine the isoelectric line. I should say that I had to artificially increase the sampling frequency / pixel density to an arbitrarily high 10,000 Hz using 'linspace' and 'pchip' so that I could accurately determine the onset and offset points of each wave. Any suggestions would be greatly appreciated.

Note: I can't provide the original ECG PDFs but I've attached the extracted xy coordinate data

Answer 1

[Star Strider]

Try this:

Y = load('sample_data_y.mat');
T = load('sample_data_x.mat');
EKGII = Y.lead2_extended_trace_y;
t = T.lead2_extended_trace_x;

Ts = mean(diff(t));                                     % Sampling Interval
Fs = 1/Ts;                                              % Sampling Frequency
Fn = Fs/2;                                              % Nyquist Frequency
L = numel(t);                                           % Signal Length
FT_EKGII = fft(EKGII-mean(EKGII))/L;                    % Subtract The Mean To See The Other Peaks More Clearly
Fv = linspace(0, 1, fix(L/2)+1)*Fn;                     % Frequency Vector
Iv = 1:numel(Fv);                                       % Index Vector

figure
plot(Fv, abs(FT_EKGII(Iv))*2)
grid
xlim([0 150])

Wp =  3;
Ws =  1;
Rp =  1;
Rs = 50;
[n,Wp] = ellipord(Wp/Fn, Ws/Fn, Rp, Rs);
[z,p,k] = ellip(n, Rp, Rs, Wp,'high');
[sos1,g1] = zp2sos(z,p,k);

figure
freqz(sos1, 2^14, Fs)
set(subplot(2,1,1), 'XLim', [0 150])
set(subplot(2,1,2), 'XLim', [0 150])
sgtitle('Highpass Filter')

Wp =  [3  75];
Ws =  [1  80];
Rp =  1;
Rs = 50;
[n,Wp] = ellipord(Wp/Fn, Ws/Fn, Rp, Rs);
[z,p,k] = ellip(n, Rp, Rs, Wp);
[sos2,g2] = zp2sos(z,p,k);

figure
freqz(sos2, 2^14, Fs)
set(subplot(2,1,1), 'XLim', [0 150])
set(subplot(2,1,2), 'XLim', [0 150])
sgtitle('Bandpass Filter')

EKGII_filtered = filtfilt(sos2, g2, EKGII);             % Change The First Two Arguments To Select The Filter

figure
subplot(2,1,1)
plot(t, EKGII)
grid
subplot(2,1,2)
plot(t, EKGII_filtered)
grid

The baseline drift is relatively easy to eliminate with a highpass filter, and it also elimnates the D-C offset. I added a second optional bandpass filter design for you to experiment with, to elimiinate some of the high-frequency noise (adjust the upper limits of the filter to get the result you want). This is definitely abnormal EKG, with pronounced Q-waves, wide QRS complexes, and a number of artifacts I cannot identify. It appears to be from a very sick heart.

[Pallavi Bugga]

Thanks Star Strider! This is exactly what I needed. Out of curiosity, do you think bandpass filtering performs superiorly to detrending using a polynomial fit? More specifically, which method do you feel preserves the original data the best?

And yes, this ECG is from a pathological data set which is making it especially difficult to analyze.

[Star Strider]

As always, my pleasure!

I prefer filtering (either highpass or bandpass) because polynomial fits can be very difficult to do with any reasonable amount of accuracy, especially when the objective is to eliminate baseline drift. Polynomials will try to fit every part of the EKG trace, not only the baseline, and are only reliable if the baseline drift is truly periodic. Baseline variations are characteristically not at all predictable, so any sort of curve-fitting approach is likely not going to be worthwhile.

Also, on a second reading, the other ‘artifacts’ I was concerned about before appear to be prominent U-waves. This entire picture could reflect significant hyperkalemia. I’ve just never seen any EKG of such a sick heart.

[Pallavi Bugga]

That's a good point. Thanks for the feedback!

[Star Strider]

Thank you!

As always, my pleasure!