By Topic

Neural network based methods for ECG data compression

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
$33 $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)
R. Kanna ; Center for Multimedia Comput., Multimedia Univ., Cyberjaya, Malaysia ; C. Eswaran ; N. Sriraam

ECG data compression algorithms are important for storage, transmission and analysis. An essential requirement of the compression algorithms is that the significant morphological features of the signal should not be lost upon reconstruction. In this paper two different neural network based methods are investigated for ECG data compression. The first method uses filters for attenuating noise and interferences, a radial-basis function network for the detection of R-points for separating the waveform into different cycles and finally multilayer back propagation networks for data compression. In the second method, the back propagation networks are used as nonlinear predictors for achieving the data compression. Compression results obtained by using the two different methods are evaluated based on standard MIT-BIH ECG Test Database.

Published in:

Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on  (Volume:5 )

Date of Conference:

18-22 Nov. 2002