Pattern Classification Techniques for EMG Decomposition

Rasheed, Sarbast and Stashuk, Daniel (2009) Pattern Classification Techniques for EMG Decomposition. In: Advanced Biosignal Processing, Editor Amine Naït-Ali. Springer-Verlag Berlin Heidelberg, Berlin Heidelberg, Germany, pp. 267-389. ISBN Print 978-3-540-89505-3; On-line 978-3-540-89506-0

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Official URL: http://link.springer.com/chapter/10.1007%2F978-3-5...

Abstract

The electromyographic (EMG) signal decomposition process is addressed by developing different pattern classification approaches. Single classifier and multiclassifier approaches are described for this purpose. Single classifiers include: certainty-based classifiers, classifiers based on the nearest neighbour decision rule: the fuzzy k-NN classifiers, and classifiers that use a correlation measure as an estimation of the degree of similarity between a pattern and a class template: the matched template filter classifiers. Multiple classifier approaches aggregate the decision of the heterogeneous classifiers aiming to achieve better classification performance. Multiple classifier systems include: one-stage classifier fusion, diversity-based one-stage classifier fusion,hybrid classifier fusion, and diversity-based hybrid classifier fusion schemes.

Item Type: Book Section
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Depositing User: Dr. Sarbast Rasheed
Date Deposited: 10 May 2016 08:40
Last Modified: 10 May 2016 08:40
URI: http://eprints.auis.edu.krd/id/eprint/29

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