Techniques of EMG signal analysis and classification of neuromuscular diseases
- V. Kehri, R. Ingle, R. Awale, S. Oimbe
- Corresponding Author
- V. Kehri
Available Online December 2016.
- https://doi.org/10.2991/iccasp-16.2017.71How to use a DOI?
- Electromyogram (EMG), Wavelet Transform (WT), Principle Component Analysis (PCA), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLPNN), Probabilistic Neural Network (PNN).
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - International Conference on Communication and Signal Processing 2016 (ICCASP 2016) PB - Atlantis Press UR - https://doi.org/10.2991/iccasp-16.2017.71 DO - https://doi.org/10.2991/iccasp-16.2017.71 ID - Kehri2016/12 ER -