Comparison of neural networks for prediction of sleep apnea

Yashar Maali, Adel Al-Jumaily

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)


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%.
Original languageEnglish
Title of host publicationNeurotechnix 2013
Subtitle of host publicationproceedings of the international congress on neurotechnology, electronics and informatics
EditorsAna Rita Londral, Pedro Encarnação, Jose Luis Pons
Place of PublicationVilamoura, Portugal
Number of pages5
ISBN (Print)9789898565808
Publication statusPublished - 2013
Externally publishedYes
EventInternational congress on neurotechnology, electronics and informatics - Vilamoura, Portugal
Duration: 18 Sep 201320 Sep 2013


ConferenceInternational congress on neurotechnology, electronics and informatics
CityVilamoura, Portugal


  • Sleep apnea
  • Neural networks
  • Prediction


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