Different neural networks approaches for identification of obstructive sleep apnea

Sarah Qasim Ali, Abdulnasir Hossen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Obstructive sleep apnea (OSA) is one of the most common breathing-related sleep disorders affecting individuals of different age groups, genders and origins. It is characterized by short-duration of cessations in breathing during sleep due to the collapse of the upper airway. The golden standard and reliable test for the detection of OSA is conducted by specialized physicians performing a polysomnographic sleep study. However, this test is time/labor consuming, expensive and cumbersome. In this paper, a non-invasive technique employing three different artificial neural networks to analyze spectral and statistical features of the Heart Rate Variability (HRV) signal to identify OSA subjects from normal control is investigated. The artificial networks include the single perceptron network, the feedforward network with back-propagation and the probabilistic neural network. The highest performance on MIT standard data is achieved by the feedforward network with back propagation using wavelet-based frequency domain features with specificity, sensitivity, and accuracy of 90%, 100% and 96.67%, respectively.

Original languageEnglish
Title of host publicationISCAIE 2018 - 2018 IEEE Symposium on Computer Applications and Industrial Electronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages281-284
Number of pages4
ISBN (Electronic)9781538635278
DOIs
Publication statusPublished - Jul 5 2018
Event2018 IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2018 - Penang Island, Malaysia
Duration: Apr 28 2018Apr 29 2018

Other

Other2018 IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2018
CountryMalaysia
CityPenang Island
Period4/28/184/29/18

Keywords

  • ANN
  • HRV
  • OSA
  • Time-Domain features
  • Wavelet Packet Decomposition

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Instrumentation

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  • Cite this

    Ali, S. Q., & Hossen, A. (2018). Different neural networks approaches for identification of obstructive sleep apnea. In ISCAIE 2018 - 2018 IEEE Symposium on Computer Applications and Industrial Electronics (pp. 281-284). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAIE.2018.8405485