Classification of EMG Signals Using Spectral Features Extracted from Dominant Motor Unit Action Potential
Anju Krishna V1, Paul Thomas2
1Anju Krishna V, Department of Electronics and Communication Engineering, Kerala University, Mar Baselios College of Engineering and Technology, Trivandrum (Kerala), India.
2Paul Thomas, Department of Electronics and Communication Engineering, Kerala University, Mar Baselios College of Engineering and Technology Trivandrum (Kerala), India.
Manuscript received on 15 June 2015 | Revised Manuscript received on 25 June 2015 | Manuscript Published on 30 June 2015 | PP: 196-200 | Volume-4 Issue-5, June 2015 | Retrieval Number: E4129064515/15©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, disease classification of electromyogram (EMG signal) based on the spectral features extracted from the dominant motor unit action potential (MUAP) is discussed. This scheme provides an improved accuracy and reduces the computational complexity to a great extend. The MUAPs are extracted from the EMG signal using a matlab program known as EMGLAB and the highest energy MUAP is selected as dominant MUAP. The main goal of this study is to extract the relevant spectral features for the classification so that the redundant features can be eliminated. For spectral feature extraction direct and DWT based methods are used. K-nearest neighborhood (KNN) classifier is used for the classification purpose. The performance is evaluated using three clinical dataset in terms of specificity sensitivity and accuracy. The results show that the classification based on the proposed method gives better accuracy than the existing methods for disease classification.
Keywords: Electromyography (EMG), Motor Unit Action Potential(MUAP), EMGLAB, Amyotrophic Lateral Sclerosis(ALS), Myopathy, K-Nearest Neighborhood (KNN) Classifier.
Scope of the Article: Classification