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

Uterine EMG processing : dynamic detection associated with multiscale classification of events

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

3 Author(s)
Khalil, M. ; Univ. of Technol. of Troyes, Troyes, France ; Duchene, J. ; Marque, C.

Towards the goal of detecting preterm birth by characterizing the events in the uterine electromyogram (EMG), we propose a new approach for detection and classification of events in this signal. Detection is based on the Dynamic Cumulative Sum (DCS) of the local generalized likelihood ratio associated with a multiscale decomposition using wavelet transform. An unsupervised classification based on the comparison between variance-covariance matrices computed from selected scales has been implemented after detection. Finally a class identification based on a neural network is used. This algorithm of detection-classification-labelling gives satisfactory results on uterine EMG: in most cases more than 80% of events are well-detected and classified whatever the term of gestation

Published in:

[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint  (Volume:2 )

Date of Conference:

Oct 1999

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.