Accepting and rejecting values in numeric array

I am analysing ECG data. I have a numeric array containing two colums.

The first column contains the location of detected ECG peaks while the second column contains the location of detected ECG troughs.

I want to align the values in each row so every ECG peak location is adjacent to the corresponding ECG trough location. Every ECG peak location is always <15 samples greater than the corresponding ECG trough location.

ie: For every row, I want to accept the values where the value in column 1 is <15 samples greater than the value in column 2.

When this condition is not met I want to delete the value that prevents this condition from being met.

In the example below I would like to delete 2953. I would then like to shift the values in column 1 up one row to replace the deleted value and add a NaN at the bottom of column 1 to complete the array. I would like to finish the code by deleting all NaNs.

226 214

543 530

858 846

1175 1162

1482 1469

1782 1770

2953 2965

2976 3836

3848 4419

4431 NaN

In another example, I would like to delete 2576 and 2908 and shift the values in column 1 up two rows to replace the deleted values and add a 2 x NaN at the bottom of column 1 to complete the array.

226 213

571 559

917 903

1261 1249

1605 1593

1939 1927

2270 2258

2576 2589

2602 2921

2908 3255

2932 3595

3267 3935

3607 NaN

3948 NaN

Is sombody able to help me create a script that will allow me to do this?

Thanks,

Sam

Answer 1

[KSSV]

A = randi(10,10,2) ;   % random data for demo 

% Remove values greater than 8 in column 1 
idx = A(:,1)>8 ; % get indices of values > 8 
A(:,1) = [ A(~idx,1) ; NaN(nnz(idx),1)]  % shift and append NaN's in the first column

A = 10×2

3 7 6 4 7 3 8 4 1 5 7 4 8 7 NaN 7 NaN 9 NaN 6

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[sam van Bohemen]

Hi KSSV,

Thanks for your reply.

However, I am not looking to remove values from column 1 that are greater than a particular number but rather I would like to remove values in column 1 that are not <= 15 samples greater than any value in column 2.

[KSSV]

Okay..that is not a problem..use the given example and extend it to your case. You need to change the condition of removing elemnts thats it.