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Classification of EMG Signals Using Eigenvalue Decomposition-Based Time-Frequency Representation

Classification of EMG Signals Using Eigenvalue Decomposition-Based Time-Frequency Representation
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Author(s): Rishi Raj Sharma (Defence Institute of Advanced Technology, Pune, India), Mohit Kumar (Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, India) and Ram Bilas Pachori (Indian Institute of Technology, Indore, India)
Copyright: 2020
Pages: 23
Source title: Biomedical and Clinical Engineering for Healthcare Advancement
Source Author(s)/Editor(s): N. Sriraam (Ramaiah Institute of Technology, India)
DOI: 10.4018/978-1-7998-0326-3.ch006

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Abstract

Electromyogram (EMG) signals are commonly used by doctors to diagnose abnormality of muscles. Manual analysis of EMG signals is a time-consuming and cumbersome task. Hence, this chapter aims to develop an automated method to detect abnormal EMG signals. First, authors have applied the improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM-HT) method to obtain the time-frequency (TF) representation of motor unit action potentials (MUAPs) extracted from EMG signals. Then, the obtained TF matrices are used for features extraction. TF matrix has been sliced into several parts and fractional energy in each slice is computed. A percentile-based slicing is applied to obtain discriminating features. Finally, the features are used as an input to the classifiers such as random forest, least-squares support vector machine, and multilayer perceptron to classify the EMG signals namely, normal and ALS, normal and myopathy, and ALS and myopathy, and achieved accuracy of 83%, 80.8%, and 96.7%, respectively.

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