Cart (Loading....) | Create Account
Close category search window
 

K9. Detection of Sleep Apnea Events using analysis of thoraco-abdominal excursion signals and adaptive neuro-fuzzy inference system (ANFIS)

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Abdel-Mageed, F.Z. ; Facualty of Eng., Mansoura Univ., Mansoura, Egypt ; Chadi, F.E.Z.A. ; Salah, H.M. ; Loza, S.F.

This paper describes an adaptive neuro-fuzzy inference system (ANFIS) for detection of Sleep Apnea Events using Thoracic and Abdominal Excursion signals. Mean amplitude sum analysis, and phase angle difference analysis using both Piecewise Linear Approximation (PLA) and phase difference measurements have been used to classify Normal, Obstructive Sleep Apnea (OSA), Hypopnea and Central Apnea events. A hybrid learning algorithm using a combination of Steepest Descent and Least Squares Estimation (LSE) was used to identify the parameters of ANFIS. The performance of ANFIS was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the Sleep Apnea events with an accuracy level of more than 95%.

Published in:

Radio Science Conference (NRSC), 2012 29th National

Date of Conference:

10-12 April 2012

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.