Automatic detection of obstructive sleep apnea using facial images

摘要

Obstructive sleep apnea (OSA) is a medical condition in which the airway is repetitively obstructed and resulting in sleep disruption. Previous research has shown that this condition may be the cause or the result of the craniofacial structure, and that specific facial features such as `face width' or `eye width' are correlated with the risk of OSA. In this study we developed two automatic image processing systems that processed facial images and determined the likelihood of a subject having OSA, based on a dataset of photographs from 365 apnea and control subjects. In our first approach, an algorithm was developed to calculate craniofacial photographic features that were previously shown to be useful for OSA discrimination. These features were processed with a logistic classifier and the resulting system achieved an accuracy of 70% in discriminating patients with clinically significant OSA from controls. In our second approach, a neural network was designed to automatically process the frontal and profile photographs directly and classify the patient as a normal or OSA. It achieved an accuracy of 62%.