Title

Deep learning of facial depth maps for obstructive sleep apnea prediction

Document Type

Conference Proceeding

Publisher

IEEE

Place of Publication

Sydney, Australia

School

School of Science

RAS ID

28105

Comments

Originally published as: Islam, S. M., Mahmood, H., Al-Jumaily, A. A., & Claxton, S. (2018, December). Deep Learning of Facial Depth Maps for Obstructive Sleep Apnea Prediction. In 2018 International Conference on Machine Learning and Data Engineering (iCMLDE) (pp. 154-157). IEEE. Original paper available here

Abstract

Obstructive Sleep Apnea (OSA) occurs when obstruction happens repeatedly in the airway during sleep due to relaxation of the tongue and airway-muscles. Usual indicators of OSA are snoring, poor night sleep due to choking or gasping for air and waking up unrefreshed. OSA diagnosis is costly both in the monetary and timely manner. That is why many patients remain undiagnosed and unaware of their condition. Previous research has shown the link between facial morphology and OSA. In this paper, we investigate the application of deep learning techniques to diagnose the disease through depth map of human facial scans. Depth map will provide more information about facial morphology as compared to the plain 2-D color image. Even with very less amount of sample data, we are able to get around 69validation accuracy using transfer learning. We are predicting patients with above moderate > 15 or below moderate ≤ 15 OSA.

DOI

10.1109/iCMLDE.2018.00036

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