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Two-stage binary classifier for neuromuscular disorders using surface electromyography feature extraction and

Jun-Woo Lee1, Myung-Jun Shin2, Myung-Hun Jang2

  • 1School of Mechanical Engineering, Punsan National University, Busan, Republic of Korea.

Medical Engineering & Physics
|December 1, 2021
PubMed
Summary

Surface electromyography (sEMG) shows promise for diagnosing neuromuscular disorders. This study achieved 86.9% accuracy classifying normal, myopathy, and neuropathy patients using sEMG signals and feature selection.

Keywords:
Feature extractionFeature selectionMachine learningNeuromuscular disordersSurface electromyography

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Needle electromyography is the standard for diagnosing neuromuscular disorders but can be invasive.
  • Surface electromyography (sEMG) offers a non-invasive alternative for assessing neuromuscular function.
  • Developing accurate sEMG-based diagnostic methods is crucial for improving patient care.

Purpose of the Study:

  • To evaluate the efficacy of sEMG in classifying individuals with normal neuromuscular function, myopathy, and neuropathy.
  • To develop and validate a machine learning model for sEMG-based diagnosis of neuromuscular disorders.
  • To compare the diagnostic accuracy of a feature-selected two-stage binary classifier with other classification approaches.

Main Methods:

  • sEMG signals were recorded during maximum voluntary isometric contractions and repetitive exercises.
  • Feature extraction involved activity and frequency analysis of sEMG signals to assess muscle activity and fatigue.
  • A two-stage binary classifier was implemented, with feature selection performed for each classification stage to optimize performance.

Main Results:

  • The feature-selected two-stage binary classifier achieved a diagnostic accuracy of 86.9%.
  • This accuracy surpassed that of a two-stage binary classifier without feature selection (82.3%) and a multi-class classifier (73.9%).
  • Statistical feature selection significantly improved the classification performance for differentiating between normal, myopathy, and neuropathy groups.

Conclusions:

  • sEMG analysis, combined with appropriate feature selection and classification algorithms, can accurately diagnose neuromuscular disorders.
  • This approach demonstrates the potential of sEMG as a reliable, non-invasive tool for clinical diagnosis of myopathy and neuropathy.
  • Further research can refine sEMG-based diagnostic systems for broader clinical application.