One approach could be to look for changes in the slope (using the gradient function here) with respect to the number of elements in the vector, and reject those with the number of slope changes below a certain threshold.
To illustrate —
changefcn = @(y) numel(y) - nnz(gradient(y)<0);
x = linspace(0, 1); % Use The Same Independent Variable For simplicity
Curve1 = 1./(1+x);
Changes1 = changefcn(Curve1)
figure
plot(x, Curve1)
text(mean(xlim),mean(ylim), sprintf('Changes = %2d',Changes1))
Curve2 = sin(15*pi*x)*0.1 - x;
Changes2 = changefcn(Curve2)
figure
plot(x, Curve2)
text(mean(xlim),mean(ylim), sprintf('Changes = %2d',Changes2))
.
