Sort a bank of ECG samples to 2 groups

Hey,

I've a bank of 2 types of ECG samples: link, some of them were measured while exercising and some while resting

all the samples are 5 seconds long, and were measured at 1k[Hz] frequency

I'm having a hard time develop an algorithem to sort that bank to 2 groups (exercising and resting)

any ideas?

[Star Strider]

I rarely go to external sites to download data. Providiing that the size is within the limits to upload them here, use ths zip function to consolidate them into one file and then upload the .zip file.

Classifying them would likely be on the basis of heart rate and possibly R-deflection amplitude. Both of those should be higher in the exercising EKGs than resting EKGs, if on the same subjects.

[Portalboi]

unfortunatly all the files together are too big, I did it with 600 samples so you can still have something to look at, while remaining under the size limits.

please take a look at this and tell me if it's fine.

[Portalboi]

Would you mind showing me how to start?

Answer 1

[Star Strider]

This is the best I can do for these.

They required a significant amount of processing to remove the baseline variation and high-frequency noise. (The processing uses two filters, the first a notch filter to remove the 50 Hz line noise, and the second bandpass filter to eliminate baseline variation and high frequency noise. The detrend call removes any baseline variation left after filtering.) The ‘Before’ and ‘After’ plots are displayed for the first 20. The table at the end displays the rate and voltage for each filtered EKG. Some of them (for example #5 and #20) are so noisy that it is difficult to do anything with them. The noise can simply swamp the data, and it is difficult to do anything about that.

Of note, #2, #4, #14 and #18 display a strain pattern (significant S-T depression) that I find worrisome.

I’m not certain how best to classify them. There can be significant uncertainty since there can be individual variation that can make that difficult. It would be easier to classify the data for a single subject between rest and exercise than for all subjects.

Uz1 = unzip('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1254867/DataECG600Shorted.zip')

Uz1 = 1×600 cell array
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% Uz2 = unzip('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1254872/DataECG600Shorted.zip')
Fs = 1E+3;
Fn = Fs/2;
L = 5001;
t = linspace(0, L-1, L).'/Fs;

N = 20;

for k = 1:N%numel(Uz1)
    LD = load(Uz1{k});
    EKG1(:,k) = LD.thisSignal;
end

Wp = [45 55]/Fn;                                                    % Passband Frequency (Normalised)
Ws = [48 52]/Fn;                                                    % Stopband Frequency (Normalised)
Rp =  1;                                                            % Passband Ripple
Rs = 60;                                                            % Passband Ripple (Attenuation)
[n,Wp] = ellipord(Wp,Ws,Rp,Rs);                                     % Elliptic Order Calculation
[z,p,k] = ellip(n,Rp,Rs,Wp,'stop');                                 % Elliptic Filter Design: Zero-Pole-Gain 

[sos1,g1] = zp2sos(z,p,k);                                          % Second-Order Section For Stability

figure
freqz(sos1, 2^16, Fs)                                               % Filter Bode Plot

Wp = [0.20 60]/Fn;                                                  % Passband Frequency (Normalised)
Ws = [0.10 65]/Fn;                                                  % Stopband Frequency (Normalised)
Rp =  1;                                                            % Passband Ripple
Rs = 60;                                                            % Passband Ripple (Attenuation)
[n,Wp] = ellipord(Wp,Ws,Rp,Rs);                                     % Elliptic Order Calculation
[z,p,k] = ellip(n,Rp,Rs,Wp);                                        % Elliptic Filter Design: Zero-Pole-Gain 

[sos2,g2] = zp2sos(z,p,k);                                          % Second-Order Section For Stability

figure
freqz(sos2, 2^16, Fs)                                               % Filter Bode Plot

figure
for k = 1:N
    subplot(N/5,5,k)
    plot(t, EKG1(:,k))
    grid
    title(sprintf('%3d',k))
end
sgtitle('Original')

NFFT = 2^nextpow2(L);
Fv = linspace(0, 1, NFFT/2+1)*Fn

Fv = 1×4097

0 0.1221 0.2441 0.3662 0.4883 0.6104 0.7324 0.8545 0.9766 1.0986 1.2207 1.3428 1.4648 1.5869 1.7090 1.8311 1.9531 2.0752 2.1973 2.3193 2.4414 2.5635 2.6855 2.8076 2.9297 3.0518 3.1738 3.2959 3.4180 3.5400

Iv = 1:numel(Fv);
for k = 1:N
    FTEKG1(:,k) = fft(EKG1(:,k)-mean(EKG1(:,k)),NFFT)/L;
end

% NrSp = size(EKG1,2);

figure
for k = 1:N
    subplot(N/5,5,k)
    plot(Fv, abs(FTEKG1(Iv,k))*2)
    grid
    xlim([00 60])
    title(sprintf('EKG1 %3d',k))
end

for k = 1:size(EKG1,2)
    EKG1(:,k) = filtfilt(sos1,g1,EKG1(:,k));
    EKG1(:,k) = filtfilt(sos2,g2,EKG1(:,k));
    EKG1(:,k) = detrend(EKG1(:,k),7);
    
end
    
figure
for k = 1:N
    subplot(N/5,5,k)
    plot(t, EKG1(:,k))
    grid
    [pks,locs] = findpeaks(EKG1(:,k), 'MinPeakProminence',(max(EKG1(:,k)))*0.75);
    Ratev = 60./(diff(t(locs)));
    Rate(k,:) = mean(Ratev);
    RateSD(k,:) = std(Ratev);
    V(k,:) = mean(pks);
    VSD(k,:) = std(pks);
    title(sprintf('EKG1 %3d',k))
end
sgtitle('Filtered')

EKG1_Nr = (1:N).';
RateVoltage = table(EKG1_Nr,Rate,RateSD,V,VSD)

RateVoltage = 20×5 table
    EKG1_Nr     Rate     RateSD       V        VSD  
    _______    ______    _______    ______    ______

       1        67.27     1.5015    241.39     23.72
       2       58.626     2.1835    217.96    23.116
       3        133.7     1.0487    139.12    25.277
       4       69.981     4.2635    243.16    32.659
       5       108.31     49.523    9.7718    3.6276
       6       125.28     1.6384    178.06    9.3044
       7       167.41      76.17    146.59    21.607
       8       112.97     3.2368    115.67    3.8871
       9       101.82     2.5095    58.848    6.8476
      10       100.08    0.73725    67.919    19.926
      11          160     43.114    89.831     30.63
      12       95.054     37.807    146.45    50.934
      13        94.06     7.5359    99.355    6.2704
      14       63.155     3.6628    221.59    23.772
      15       89.628     6.7647    92.713    1.8705
      16       92.555     8.6227    90.693    5.4217

EDIT — (8 Jan 2023 at 11:54)

Added ‘RateSD’ and ‘VSD’ to calculations and to the table. Code otherwise unchanged.

.

[Portalboi]

Thank you so much for the help and all the explanations.

highly appreciate.

[Star Strider]

As always, my pleasure!

The standard deviation (specifically ‘coefficient of variation’, defined as the standard deviation divided by the mean) can be helpful is assessing the quality of the data. The larger it is, the less reliable the data are.

Answer 2

[Vilém Frynta]

Hello,

as Star Strider already mentioned, you can filter data based on the R-amplitude and or heart rate (time between R–R).

I have tried to plot your data and it seems there is quite a big difference in amplitudes, so it appears that it could be a reliable filter.

Also, I am not very confident whether this is valid option, but I tried to perform FFT on your data, and it looks like there might be a difference between resting ECG and excersing ECG. But it is possible that I have chosen some data that are "perfect" and this might not be the case for the rest of your data.

See images below;

== example 1 and it's FFT ==

== example 2 and it's FFT ==