Comparison of neural networks for prediction of sleep apnea

Maali, Y; Al-Jumaily, A

Comparison of neural networks for prediction of sleep apnea

Maali, Y Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: NEUROTECHNIX 2013 - Proceedings of the International Congress on Neurotechnology, Electronics and Informatics, 2013, pp. 60 - 64
Issue Date:: 2013-12-03

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Field	Value	Language
dc.contributor.author	Maali, Y	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2013-12-03	en_US
dc.identifier.citation	NEUROTECHNIX 2013 - Proceedings of the International Congress on Neurotechnology, Electronics and Informatics, 2013, pp. 60 - 64	en_US
dc.identifier.isbn	9789898565808	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27025
dc.description.abstract	Sleep apnea (SA) is the most important and common component of sleep disorders which has several short term and long term side effects on health. There are several studies on automated SA detection but not too much works have been done on SA prediction. This paper discusses the application of artificial neural networks (ANNs) to predict sleep apnea. Three types of neural networks were investigated: Elman, cascade-forward and feed-forward back propagation. We assessed the performance of the models using the Receiver Operating Characteristic (ROC) curve, particularly the area under the ROC curves (AUC), and statistically compare the cross validated estimate of the AUC of different models. Based on the obtained results, generally cascade-forward model results are better with average of AUC around 80%.	en_US
dc.relation.ispartof	NEUROTECHNIX 2013 - Proceedings of the International Congress on Neurotechnology, Electronics and Informatics	en_US
dc.title	Comparison of neural networks for prediction of sleep apnea	en_US
dc.type	Conference Proceeding
utslib.for	170203 Knowledge Representation and Machine Learning	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Algarve, Portugal	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:

Sleep apnea (SA) is the most important and common component of sleep disorders which has several short term and long term side effects on health. There are several studies on automated SA detection but not too much works have been done on SA prediction. This paper discusses the application of artificial neural networks (ANNs) to predict sleep apnea. Three types of neural networks were investigated: Elman, cascade-forward and feed-forward back propagation. We assessed the performance of the models using the Receiver Operating Characteristic (ROC) curve, particularly the area under the ROC curves (AUC), and statistically compare the cross validated estimate of the AUC of different models. Based on the obtained results, generally cascade-forward model results are better with average of AUC around 80%.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/27025