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Characterizing the SEMG patterns with myofascial pain using a multi-scale wavelet model through machine learning

Yu-Ching Lin1, Nan-Ying Yu2, Ching-Fen Jiang3

  • 1Department of Physical Medicine and Rehabilitation, College of Medicine, National Cheng Kung University, Taiwan.

Journal of Electromyography and Kinesiology : Official Journal of the International Society of Electrophysiological Kinesiology
|June 12, 2018
PubMed
Summary

A new multi-scale wavelet model effectively interprets surface electromyography (SEMG) signals to identify neuromuscular activation changes in myofascial pain syndrome (MPS). This model shows promise for differentiating MPS patients from healthy individuals using machine learning techniques.

Keywords:
ClassificationMachine learningMulti-scale wavelet energyMyofascial pain syndromeSurface electromyography

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Area of Science:

  • Biomedical Engineering
  • Neuromuscular Physiology
  • Machine Learning in Healthcare

Background:

  • Myofascial Pain Syndrome (MPS) involves altered neuromuscular activation patterns.
  • Surface electromyography (SEMG) is a valuable tool for assessing muscle activity.
  • Interpreting complex SEMG signals, especially in pathological conditions, remains challenging.

Purpose of the Study:

  • To introduce and validate a novel multi-scale wavelet model for SEMG signal interpretation.
  • To assess the model's ability to characterize neuromuscular activation changes in MPS patients.
  • To apply machine learning methods for classifying MPS subjects based on SEMG data.

Main Methods:

  • Development of a multi-scale wavelet model for SEMG signal analysis.
  • Collection of SEMG data from trapezius muscles during shoulder extension in normal and MPS subjects.
  • Application of template matching and K-means clustering for classification and analysis of 2D feature graphs.

Main Results:

  • The multi-scale wavelet model demonstrated distinct feature patterns between normal and MPS subjects.
  • Classification accuracy reached 77% using template matching and 60% using K-means clustering for differentiating MPS patients.
  • High classification consistency (87% normal, 93% MPS) was observed between the machine learning methods.

Conclusions:

  • The proposed multi-scale wavelet model shows potential for characterizing interference pattern changes associated with MPS.
  • Machine learning applied to SEMG data interpreted by this model can aid in differentiating MPS patients.
  • Further validation could establish this model as a clinical tool for MPS diagnosis and management.