Research Article Open Access

Determination of Neuromuscular Diseases Using Complexity of Electromyogram Signals

Kawser Ahammed1 and Mosabber Uddin Ahmed2
  • 1 Jatiya Kabi Kazi Nazrul Islam University, Bangladesh
  • 2 University of Dhaka, Bangladesh


Characterization of Electromyogram (EMG) signals is important for identifying neuromuscular diseases. Although various techniques were implemented for classification, none of them were implemented in the complexity domain. In this study, characterization of EMG signals recorded from healthy, neuropathic and myopathic subjects has been performed in complexity domain based on Multiscale Entropy (MSE) method. To do this, the multiscale entropy method has been applied to an EMG database publicly available in PhysioNet. The complexity profile curves obtained with the MSE approach have shown promising classification among healthy subject, neuropathic and myopathic patients in terms of complexity. One way ANOVA test has shown statistically significant differences (p<0.01) among these three classes. Moreover, Support Vector Machine (SVM) has demonstrated a classification accuracy of 86.1% for characterizing EMG signals of neuromuscular disorders. Furthermore, myopathic and neuropathic patients have been recognized with 66.7% sensitivity at 95.8% specificity and 100% sensitivity at 100% specificity respectively.

Neuroscience International
Volume 10 No. 1, 2019, 8-12


Submitted On: 10 October 2019 Published On: 7 December 2019

How to Cite: Ahammed, K. & Ahmed, M. U. (2019). Determination of Neuromuscular Diseases Using Complexity of Electromyogram Signals. Neuroscience International, 10(1), 8-12.

  • 2 Citations



  • Electromyogram
  • Neuromuscular Diseases
  • Complexity
  • Multiscale Entropy
  • Support Vector Machine