Comparison of Neural Networks for Prediction of Sleep Apnea

Publisher:
SCITEPRESS
Publication Type:
Conference Proceeding
Citation:
NEUROTECHNIX: International Congress on Neurotechnology, Electronics and Informatics, 2013
Issue Date:
2013-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2012007530OK.pdf3.58 MB
Adobe PDF
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 net-works (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: