Detection of multiple sclerosis with visual evoked potentials - an unsupervised computational intelligence system.

Detection of multiple sclerosis with visual evoked potentials - an unsupervised computational intelligence system.

This paper describes the application of a novel unsupervised pattern recognition system to the classification of the visual evoked potentials (VEPs) of normal and multiple sclerosis (MS) patients. The method combines a traditional statistical feature extractor with a fuzzy clustering method, all imp...

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Détails bibliographiques
Publié dans:IEEE Transactions on information technology in biomedicine 4, 3 (2000).
Auteur principal: Dasey, T.J
Format: Article
Langue:English
Sujets:
ALOPEX.
Karhunen-Loeve expansion vectors.
Classification.
Feature extraction.
Fuzzy c-means.
Fuzzy clustering method.
Minimization.
Multiple sclerosis.
Objective error function.
Optimization routine.
Parallel neural network architecture.
Statistical feature extractor.
Unsupervised computational intelligence system.
Unsupervised pattern recognition system.
Visual evoked potentials.