Performance Analysis of Muscular Paralysis Disease Using Machine Learning


  • Shubha V. Patel Research Scholar, Department of Electronics and Communication Engineering, BGS Institute of Technology, B. G. Nagara, Karnataka, India
  • S. L. Sunitha Associate Professor, Department of Electronics and Instrumentation Engineering, University BDT College of Engineering, Davangere, Karnataka, India



Electromyography, Wavelet Transform, Random Forest, Multilayer Perceptron, Mayosignal


Electromyography has been used for many years in regulating paralyzed limb. Captured and Processed EMG is an indication of human movements. EMG signal (called as Mayo signal) will be recorded by surface electrodes and needle electrodes. In this work, the combined time and frequency analysis has been carried out to extract the required features using Wavelet Transform tools. Further the classification has been carried by 2 different Machine Learning based algorithms, Random Forest (RF), and Multilayer Perceptron (MLP). The standard data set has been used for the purpose. The classifier model has used 80% data as a training set and the remaining 20% of data as the test set. The result shows that Random Forest and MLP perform better with an accuracy of 98 %. This classification model can serves as a promising candidate for analysis of muscular paralysis.


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How to Cite

Patel, S. V., & Sunitha, S. L. (2022). Performance Analysis of Muscular Paralysis Disease Using Machine Learning . Asian Journal of Computer Science and Technology, 11(1), 21–34.