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

by Muzaffar Khan, Jaikaran Singh, Mukesh Tiwari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 4
Year of Publication: 2016
Authors: Muzaffar Khan, Jaikaran Singh, Mukesh Tiwari
10.5120/ijca2016907710

Muzaffar Khan, Jaikaran Singh, Mukesh Tiwari . A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders. International Journal of Computer Applications. 133, 4 ( January 2016), 13-18. DOI=10.5120/ijca2016907710

@article{ 10.5120/ijca2016907710,
author = { Muzaffar Khan, Jaikaran Singh, Mukesh Tiwari },
title = { A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 4 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 13-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number4/23773-2016907710/ },
doi = { 10.5120/ijca2016907710 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:30:12.841026+05:30
%A Muzaffar Khan
%A Jaikaran Singh
%A Mukesh Tiwari
%T A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 4
%P 13-18
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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 was 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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Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machine EMG Discrete wavelet Transform K-nearest neighborhood (KNN)

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