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

Content Provider	IEEE Xplore Digital Library
Author	Geva, A.B.
Copyright Year	1997
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel (Geva, A.B.)
Abstract	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.
Starting Page	41
Ending Page	45
File Size	564474
Page Count	5
File Format	PDF
ISBN	0780340531
ISSN	1062922X
DOI	10.1109/ICSMC.1997.625718
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering methods Clustering algorithms Signal processing algorithms Partitioning algorithms Signal processing Biomedical signal processing Biomedical measurements Switches Stationary state Feature extraction
Content Type	Text
Resource Type	Article

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