Techniques of EMG signal analysis and classification of neuromuscular diseases

DOI

10.2991/iccasp-16.2017.71How to use a DOI?

Keywords

Electromyogram (EMG), Wavelet Transform (WT), Principle Component Analysis (PCA), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLPNN), Probabilistic Neural Network (PNN).

Abstract

Artificial intelligence techniques are being used effectively in medical diagnostic tools to increase the diagnostic accuracy and provide additional knowledge. Electromyography (EMG) signals are becoming increasingly im-portant in clinical and biomedical applications. Detection, processing and classification of EMG signals are very desirable because it allows a more standardized evaluation to discriminate between different neuromuscular diseases. This paper reviews a brief explanation of the different features extraction and classification tech-niques for classifying EMG signals used in literatures. Wavelet Transform (WT), Principle Component Analysis (PCA), and Independent Component Analysis (ICA) are different feature extraction techniques. Literature pre-sents different techniques to classify EMG data such as probabilistic neural network (PNN), Support Vector Machine (SVM), Artificial Neural Networks (ANN), etc. In this paper neuromuscular disease classification from electromyography (EMG) signals are proposed based on different combination of features extraction methods and types of classifiers. Combination of WT and SVM improved the classification accuracy than other combi-nations such as DWT with ANN, ICA with MLPN, PCA with ANN and DWT with PNN.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - V. Kehri
AU  - R. Ingle
AU  - R. Awale
AU  - S. Oimbe
PY  - 2016/12
DA  - 2016/12
TI  - Techniques of EMG signal analysis and classification of neuromuscular diseases
BT  - Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)
PB  - Atlantis Press
SP  - 481
EP  - 487
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasp-16.2017.71
DO  - 10.2991/iccasp-16.2017.71
ID  - Kehri2016/12
ER  -