Decision making in electromyography using wavelet-type analysis and fuzzy clustering

Author

Geva, Amir B. ; Gath, Isak

Author_Institution

Dept. of Electr. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel

Volume

1

fYear

1996

fDate

14-17 Oct 1996

Firstpage

276

Abstract

Classification of motor unit action potentials in electromyography is to be based on an optimal method for feature extraction, matched to the special characteristics of the signal, and on an efficient method of pattern analysis. For the feature extraction stage, wavelet-type representation of the motor unit action potentials has been compared to conventional orthogonal decomposition using Karhunen-Loewe transformation (KLT). Classification of the feature vectors was carried out using a modified version of the unsupervised optimal fuzzy clustering algorithm (UOFC). By application of the algorithms to test data comprised of 130 labeled motor unit action potentials it could be verified that the wavelet-type decomposition was significantly superior to the KLT

Keywords

decision theory; electromyography; feature extraction; fuzzy set theory; medical signal processing; optimisation; pattern classification; transforms; wavelet transforms; Karhunen-Loeve transformation; Karhunen-Loewe transformation; decision-making; electromyography; feature extraction; motor unit action potential classification; orthogonal decomposition; pattern analysis; unsupervised optimal fuzzy clustering algorithm; wavelet-type decomposition; Decision making; Electromyography; Feature extraction; Karhunen-Loeve transforms; Matching pursuit algorithms; Neuromuscular; Pattern analysis; Shape; Signal analysis; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1996., IEEE International Conference on

Conference_Location

Beijing

ISSN

1062-922X

Print_ISBN

0-7803-3280-6

Type

conf

DOI

10.1109/ICSMC.1996.569780

Filename

569780