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arXiv · 2006.11408

Quasi-conformal Geometry based Local Deformation Analysis of Lateral Cephalogram for Childhood OSA Classification

Hei-Long Chan·Hoi-Man Yuen·Chun-Ting Au·Kate Ching-Ching Chan·Albert Martin Li·Lok-Ming Lui
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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. 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 92.5% testing accuracy.

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Hei-Long Chan, Hoi-Man Yuen, Chun-Ting Au, Kate Ching-Ching Chan, Albert Martin Li, Lok-Ming Lui. 2020-05-31. Quasi-conformal Geometry based Local Deformation Analysis of Lateral Cephalogram for Childhood OSA Classification. https://arxiv.org/abs/2006.11408

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