Diagnosis of Pediatric Obstructive Sleep Apnea via Face Classification with Persistent Homology and Convolutional Neural Networks

TL;DR

This paper proposes a non-invasive, fast diagnostic method for pediatric obstructive sleep apnea using facial image analysis with persistent homology and convolutional neural networks, aiming to replace traditional sleep studies.

Contribution

It introduces a novel combination of computational topology and deep learning techniques for facial classification to diagnose sleep apnea.

Findings

Effective facial feature recognition for sleep apnea detection

Comparison of persistent homology and CNN approaches

Potential for non-invasive diagnosis

Abstract

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.