alexa A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders
ISSN: 2155-9538

Journal of Bioengineering & Biomedical Science
Open Access

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Research Article

A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders

Muzaffar Khan*, Jai Karan Singh and Mukesh Tiwari

S.S.S. Institute of Science and Technology, Bhopal, India

*Corresponding Author:
Muzaffar Khan
S.S.S. Institute of Science and Technology
Bhopal, India
Tel: 075622 21127
E-mail: [email protected]

Received Date: November 26, 2015; Accepted Date: January 27, 2015; Published Date: February 11, 2016

Citation: Khan M, Singh JK, Tiwari M (2016) A Multi-Classifier Approach of EMG signal classification for Diagnosis of Neuromuscular Disorders. J Bioengineer & Biomedical Sci S3:003. doi:10.4172/2155-9538.S3-003

Copyright: © 2016 Khan M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders. This paper is aim at introducing an effective multi-classifier approach to enhance classification accuracy .The proposed system employs both time domain and time-frequency domain features of motor unit action potentials (MUAPs) extracted from an EMG signal. Different classification strategies including single classifier and multiple classifiers with time domain and time frequency domain features were investigated. Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classifier used predict class label (Myopathic, Neuropathic, or Normal) for a given MUAP. Extensive analysis is performed on clinical EMG database for the classification of neuromuscular diseases and it is found that the proposed methods provide a very satisfactory performance in terms overall classification accuracy.


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