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Class discriminator-based EMG classification approach for detection of neuromuscular diseases using discriminator-dependent decision rule (D3R) approach

Bhattacharya, Avik; Pahari, Purbanka; Basak, Piyali and Sarkar, Anasua LU (2019) 1st International Symposium on Signal and Image Processing, ISSIP 2017 In Advances in Intelligent Systems and Computing 727. p.49-56
Abstract

Classification of EMG signals is essential for diagnosis of motor neuron diseases like neuropathy and myopathy. Although a number of strategies have been implemented for classification, none of them are efficient enough to be implemented in clinical environment. In the present study, we use ensemble approach of support vector machines for classification of three classes (normal, myopathic and neuropathic) of clinical electromyogram (EMG). Our proposed approach uses time and time–frequency features extracted from EMG signals. By employing two types of feature set for same class discriminators, we are able to select the best feature set-discriminator pairs. The decision made by each selected classifier is used to generate the final class... (More)

Classification of EMG signals is essential for diagnosis of motor neuron diseases like neuropathy and myopathy. Although a number of strategies have been implemented for classification, none of them are efficient enough to be implemented in clinical environment. In the present study, we use ensemble approach of support vector machines for classification of three classes (normal, myopathic and neuropathic) of clinical electromyogram (EMG). Our proposed approach uses time and time–frequency features extracted from EMG signals. By employing two types of feature set for same class discriminators, we are able to select the best feature set-discriminator pairs. The decision made by each selected classifier is used to generate the final class for an input EMG signal through majority voting. Our proposed method yields higher accuracy of 94.67% over 89.67% for multiclass SVM classifier.

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author
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Discriminator-dependent decision rule, Electromyogram, Ensemble framework, MUAP classification
in
Advances in Intelligent Systems and Computing
volume
727
pages
8 pages
publisher
Springer Verlag
conference name
1st International Symposium on Signal and Image Processing, ISSIP 2017
external identifiers
  • scopus:85047400089
ISSN
2194-5357
ISBN
978-981-10-8863-6
9789811088629
DOI
10.1007/978-981-10-8863-6_6
language
English
LU publication?
no
id
864b16ed-45a0-475a-90a1-09035ffd9f84
date added to LUP
2018-09-13 10:13:48
date last changed
2018-09-19 09:28:24
@inproceedings{864b16ed-45a0-475a-90a1-09035ffd9f84,
  abstract     = {<p>Classification of EMG signals is essential for diagnosis of motor neuron diseases like neuropathy and myopathy. Although a number of strategies have been implemented for classification, none of them are efficient enough to be implemented in clinical environment. In the present study, we use ensemble approach of support vector machines for classification of three classes (normal, myopathic and neuropathic) of clinical electromyogram (EMG). Our proposed approach uses time and time–frequency features extracted from EMG signals. By employing two types of feature set for same class discriminators, we are able to select the best feature set-discriminator pairs. The decision made by each selected classifier is used to generate the final class for an input EMG signal through majority voting. Our proposed method yields higher accuracy of 94.67% over 89.67% for multiclass SVM classifier.</p>},
  author       = {Bhattacharya, Avik and Pahari, Purbanka and Basak, Piyali and Sarkar, Anasua},
  booktitle    = {Advances in Intelligent Systems and Computing},
  isbn         = {978-981-10-8863-6},
  issn         = {2194-5357},
  keyword      = {Discriminator-dependent decision rule,Electromyogram,Ensemble framework,MUAP classification},
  language     = {eng},
  pages        = {49--56},
  publisher    = {Springer Verlag},
  title        = {Class discriminator-based EMG classification approach for detection of neuromuscular diseases using discriminator-dependent decision rule (D3R) approach},
  url          = {http://dx.doi.org/10.1007/978-981-10-8863-6_6},
  volume       = {727},
  year         = {2019},
}