Deep learning of facial depth maps for obstructive sleep apnea prediction

Islam, SM; Mahmood, H; Al-Jumaily, AA; Claxton, S

Deep learning of facial depth maps for obstructive sleep apnea prediction

Islam, SM Mahmood, H Al-Jumaily, AA

Claxton, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2018, 2019, pp. 154 - 157
Issue Date:: 2019-01-15

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Field	Value	Language
dc.contributor.author	Islam, SM	en_US
dc.contributor.author	Mahmood, H	en_US
dc.contributor.author	Al-Jumaily, AA https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Claxton, S	en_US
dc.date.issued	2019-01-15	en_US
dc.identifier.citation	Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2018, 2019, pp. 154 - 157	en_US
dc.identifier.isbn	9781728104041	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134542
dc.description.abstract	© 2018 IEEE. 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.	en_US
dc.relation.ispartof	Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2018	en_US
dc.relation.isbasedon	10.1109/iCMLDE.2018.00036	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Deep learning of facial depth maps for obstructive sleep apnea prediction	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. 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.

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http://hdl.handle.net/10453/134542