Keeping F1-score (F1-measure) tract while Tuning C & sigma in SVM

I need to keep F1-score (F1-measure) tract while tuning C & Sigma in SVM, For example this code keep tract of the Accuracy, I need to change it to F1-Score but I was not able to do that…….

....
  %# read some training data
[labels,data] = libsvmread('./heart_scale');
%# grid of parameters
folds = 5;
[C,gamma] = meshgrid(-5:2:15, -15:2:3);
%# grid search, and cross-validation
cv_acc = zeros(numel(C),1);
for i=1:numel(C)
  cv_acc(i) = svmtrain(labels, data, ...
                  sprintf('-c %f -g %f -v %d', 2^C(i), 2^gamma(i), folds));
end
%# pair (C,gamma) with best accuracy
[~,idx] = max(cv_acc);
%# now you can train you model using best_C and best_gamma
best_C = 2^C(idx);
best_gamma = 2^gamma(idx);
%# ...

I have seen the following two links

http://stackoverflow.com/questions/9047459/retraining-after-cross-validation-with-libsvm/9049225#9049225

http://stackoverflow.com/questions/14024740/10-fold-cross-validation-in-one-against-all-svm-using-libsvm

I do understand that I have to first find the best C and gamma/sigma parameters over the training data, then use these two values to do a LEAVE-ONE-OUT crossvalidation classification experiment, So what I want now is to first do a grid-search for tuning C & sigma. Please I would prefer to use MATLAB-SVM and not LIBSVM