  Obstructive sleep apnea is a serious condition causing a litany of health
problems especially in the pediatric population. However, this chronic
condition can be treated if diagnosis is possible. The gold standard for
diagnosis is an overnight sleep study, which is often unobtainable by many
potentially suffering from this condition. Hence, we attempt to develop a fast
non-invasive diagnostic tool by training a classifier on 2D and 3D facial
images of a patient to recognize facial features associated with obstructive
sleep apnea. In this comparative study, we consider both persistent homology
and geometric shape analysis from the field of computational topology as well
as convolutional neural networks, a powerful method from deep learning whose
success in image and specifically facial recognition has already been
demonstrated by computer scientists.
