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Towards objective identification of myofascial trigger points: A high-density surface electromyography method
Seekaow Churproong1, Guangyuan Ren2, Chen Chen3
1Department of Electronic and Electrical Engineering, University of Bath, Bath, BA2 7AY, United Kingdom; Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand.
High-density surface electromyography (HD-sEMG) combined with machine learning shows promise for identifying myofascial trigger points (MTrPs). This approach analyzes muscle electrical activity and motor unit characteristics to differentiate between active and non-active MTrPs.
Area of Science:
- Neurology
- Biomedical Engineering
- Pain Management
Background:
- Myofascial trigger points (MTrPs) are a common cause of myofascial pain syndrome (MPS).
- Diagnosing MTrPs is challenging due to a lack of objective methods.
- High-density surface electromyography (HD-sEMG) offers potential for objective MTrP identification.
Purpose of the Study:
- To investigate the utility of HD-sEMG in differentiating active from non-active MTrPs.
- To analyze electromyography (EMG) and decomposed motor unit (MU) characteristics.
- To evaluate the performance of machine learning models in MTrP classification.
Main Methods:
- Compared EMG and MU activities in active (n=15) and non-MTrPs (n=22) in the trapezius muscles.
- Extracted MU activities using gradient Convolution Kernel Compensation (gCKC)-based decomposition during maximal contractions.
- Classified features using five machine learning models, including artificial neural networks (ANN).
Main Results:
- Active MTrPs showed lower EMG activity (RMS, MDF) and MU discharge rate, but higher MU number and MU action potential (MUAP) amplitude compared to non-MTrPs.
- The ANN classifier achieved 82.35% accuracy in sample-wise cross-validation.
- Key predictors for MTrP classification included MU number, MUAP amplitude, RMS, and MDF.
Conclusions:
- HD-sEMG is a promising tool for objective MTrP identification.
- Machine learning, particularly ANN-based classification, demonstrates strong potential for MTrP detection.
- Further research is needed to enhance classification accuracy for MTrP identification using EMG decomposition.
