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

• URL: http://arxiv.org/abs/2006.11408v1
• Date: Sun, 31 May 2020 04:14:38 GMT
• Title: Quasi-conformal Geometry based Local Deformation Analysis of Lateral Cephalogram for Childhood OSA Classification
• Authors: Hei-Long Chan, Hoi-Man Yuen, Chun-Ting Au, Kate Ching-Ching Chan, Albert Martin Li, Lok-Ming Lui
• Abstract summary: Craniofacial profile is one of the anatomical causes of obstructive sleep apnea(OSA) In this work, a novel approach to cephalometric analysis using quasi-conformal geometry based local deformation information was proposed for OSA classification.
• Score: 0.6524460254566903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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