在 C++ 中的一系列数字中查找重复模式

Question

I am trying to implement an auto grid detection system for an electrocardiogram, ecg, paper see the figure below.The idea behind is to add the pixel values(only considered the red channel) by going through pixel by pixel of the ecg image as shown in the code below.

QImage image("C:/Users/.../Desktop/ECGProject/electrocardiogram.jpg");
std::vector<int> pixelValues;
for (int y = 0; y < img.height(); y++)
{
   int rowSumR = 0, rowSumG = 0, rowSumB = 0;
   for (int x = 0; x < img.width(); x++)
   {
       QRgb rgb = img.pixel(x, y);
       rowSumR += qRed(rgb);
   }
   rowSumR /= img.width();
   const int &value = rowSumR/4;
   pixelValues.push_back(value)
}

The vector pixelValues contains summed values which has repeated pattern in ay direction. The goal is to detect those repeated pattern (for instance the line drawn in black color on in the ecg image is the interest or what I am looking to identify in ay direction). I also draw the summed pixel value in y direction using matlab(see the figure below) and the red circles are the pattern I am interested in. Any suggestion/algorithm to find these repeated pattern would be appreciated. [![Ecg paper][1]][1] [![enter image description here][2]][2]

Answer 1

If you need to identify the number of bold red grid lines and "cut off" the similar patterns associated with each "period" in it I would suggest using of pitch tracking algorithms used in speech processing. One such approach, which computes the so-called pitch track is described in this work: https://www.diva-portal.org/smash/get/diva2:14647/FULLTEXT01.pdf If you need help implementing that algorithm I can do it for you if you provide me the data.

Answer 2

I wrote a following program for you in matlab:

load data.txt

y = data(:,2);
yr = resample(y,10,1);

xhat = cceps(yr);
figure(1)
subplot(2,1,1)
plot(0:length(xhat)-1,xhat)
subplot(2,1,2)
plot(0:length(yr)-1,yr)

maxima = zeros(10000,1);
cnt = 1;
for i = 2:length(xhat)-1
    
    if xhat(i-1) < xhat(i) && xhat(i+1) < xhat(i)
        maxima(cnt) = i-1;
        cnt = cnt + 1;
    end
end
maxima(cnt:end) = [];
disp(maxima(1:10)/10)

The cepstra are a signal processing tool, which allow detection of periodicity. It actually deconvolve signals. Say, in our case, we have an impuls train and some pattern convolved. Cepstral analysis 'decouples' the impuls train and the pattern. The impuls train period results in a maximum at given time spot in the cepstrum. If you run this program you can state from the output that the fine grained periodicity has mean period of 3.5 pixels and the greedy periodicity (you marked the corresponding impulses red) has mean period of 23.4 pixels (note the interpolation). Based on this observation you can try by the correlation analysis to refine the local placement of impulses with a technique known from speech processing as pitch-analysis (which is based on the correlation analysis). This last step might be necessary since there are apparent irregularities in peaks placement. Let me know if you have further doubts.