CNN classification from array

Hi I want to classify an ECG signal with 9 million samples using cnn and the adnotation array. I divided the signal into multiple ones into an array and now i have an array of 30 000 signals x 300 samples. I want to do cwt (continuous wavelet transform) for every line in the array and then classify them using cnn toolbox. Is there a way to do this using a for loop and extract the image in two different folders knowing the value of the first row (the adnotation)? For example if for line 5 the first row have 1 extract the figure from cwt in AF folder, if not extract it in N folder and then train my cnn?

main:

[signal,Fs,tm]=rdsamp('08455');
[ann]=rdann('08434','atr');
sig = signal(:,1);        %number of samples
samp=length(tm);
increm = 1/Fs;            %incrementation of samples
nr_c = tm(samp,1)/1.2;    %number of samples extracted
l_adn=length(ann);       
sig2 = filtrare(sig);
total = segmentare (nr_c,increm,sig2,l_adn,ann,Fs);

segmentare:

function y = segmentare(a,c,s,d,adn,f)
    y = zeros();
    samp =9205760;
    for i=1:a-1         %lines in matrix
        poz=(1.2/c);    % number of signals segmentated
        for j=1:poz     % columns
            if i==1 
                poz1=1;   %adnotation signal
            else poz1=poz*(i-1);
            end
            for l=1:d
                 if adn(l,1)>=poz1 && adn(l,1)<=(poz*i)
                    y(i,1)=1;
                 end
            end
            y(i,j+1) = s((poz*(i-1)+j),:);   %final matrix
            end
    end
    wavelet_continuu(y,a,f);    %wavelet transform for every signal
end