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Pattern Classification Techniques for EMG Signal Decomposition

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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.

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Authors and Affiliations

  1. Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada

    Sarbast Rasheed & Daniel Stashuk

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  1. Sarbast Rasheed
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  2. Daniel Stashuk
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Correspondence to Sarbast Rasheed .

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Editors and Affiliations

  1. Labo. Images, Signaux et, Systèmes Intelligents (LISSI) EA 3956, Université Paris 12, av. du Général de Gaille 61, Créteil, 94010, France

    Amine Naït-Ali

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© 2009 Springer-Verlag Berlin Heidelberg

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Rasheed, S., Stashuk, D. (2009). Pattern Classification Techniques for EMG Signal Decomposition. In: Naït-Ali, A. (eds) Advanced Biosignal Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89506-0_13

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  • DOI: https://doi.org/10.1007/978-3-540-89506-0_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89505-3

  • Online ISBN: 978-3-540-89506-0

  • eBook Packages: EngineeringEngineering (R0)

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