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

Unsupervised hierarchical fuzzy clustering methods in forecasting medical events from biomedical signals

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

1 Author(s)
Geva, A.B. ; Dept. of Electr. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel

Many problems in the field of biomedical signal processing can be reduced to a task of state recognition and event forecasting. We propose to apply clustering methods to grouping discontinuous related temporal patterns of a continuously sampled measurement. The vague switches from one stationary state to another are naturally treated by means of fuzzy clustering. In such cases an adaptive selection of the number of clusters (the number of underlying semi-stationary processes in the signal) can overcome the general non-stationary nature of biomedical signals and enables the formation of a warning cluster. The algorithm suggested for the clustering is a new recursive algorithm for hierarchical-fuzzy partition. The algorithm benefits from the advantages of hierarchical clustering while obtaining fuzzy clustering rules. Each pattern can have a non-zero membership in more than one sub-data-sets in the hierarchy. Optimal feature extraction and reduction is reapplied for each sub-data-set. A “natural” and feasible solution to the cluster validity problem is suggested by combining hierarchical and fuzzy concepts. The algorithm is shown to be effective for a variety of data sets with a wide dynamic range of both covariance matrices and number of members in each class. The new method is applied to the forecasting of biomedical events like generalized epileptic seizures from the EEG and heart rate signals

Published in:

Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on  (Volume:1 )

Date of Conference:

12-15 Oct 1997

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.