Classification of Childhood Obstructive Sleep Apnea based on X-ray images analysis by Quasi-conformal Geometry

Abstract

Craniofacial profile is one of the anatomical causes of obstructive sleep apnea (OSA). By medical research, cephalometry provides information on patients’ skeletal structures and soft tissues. In this work, a novel approach to cephalometric analysis using quasi-conformal geometry based local deformation information was proposed for OSA classification. Our study was a retrospective analysis based on 60 case-control pairs with accessible lateral cephalometry and polysomnography (PSG) data (mean age: 8.9 ± 2.3 years). By using the quasi-conformal geometry to study the local deformation around 15 landmark points, and combining the results with three linear distances between landmark points, a total of 1218 information features were obtained per subject. A L2 norm based classification model was built. Under experiments, our proposed model achieves high testing accuracy. The accurate classification tool provides us with a reliable and accurate screening tool for childhood OSA.