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Analysis and Biophysics of Surface EMG for Physiotherapists and Kinesiologists: Toward a Common Language With Rehabilitation Engineers

Overview
Journal Front Neurol
Specialty Neurology
Date 2020 Nov 12
PMID 33178118
Citations 38
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Abstract

Recent decades have seen a move toward evidence-based medicine to inform the clinical decision-making process with reproducible findings from high-quality research studies. There is a need for objective, quantitative measurement tools to increase the reliability and reproducibility of studies evaluating the efficacy of healthcare interventions, particularly in the field of physical and rehabilitative medicine. Surface electromyography (sEMG) is a non-invasive measure of muscle activity that is widely used in research but is under-utilized as a clinical tool in rehabilitative medicine. Other types of electrophysiological signals (e.g., electrocardiography, electroencephalography, intramuscular EMG) are commonly recorded by healthcare practitioners, however, sEMG has yet to successfully transition to clinical practice. Surface EMG has clear clinical potential as an indicator of muscle activation, however reliable extraction of information requires knowledge of the appropriate methods for recording and analyzing sEMG and an understanding of the underlying biophysics. These concepts are generally not covered in sufficient depth in the standard curriculum for physiotherapists and kinesiologists to encourage a confident use of sEMG in clinical practice. In addition, the common perception of sEMG as a specialized topic means that the clinical potential of sEMG and the pathways to application in practice are often not apparent. The aim of this paper is to address barriers to the translation of sEMG by emphasizing its benefits as an objective clinical tool and by overcoming its perceived complexity. The many useful clinical applications of sEMG are highlighted and examples provided to illustrate how it can be implemented in practice. The paper outlines how fundamental biophysics and EMG signal processing concepts could be presented to a non-technical audience. An accompanying tutorial with sample data and code is provided which could be used as a tool for teaching or self-guided learning. The importance of observing sEMG in routine use in clinic is identified as an essential part of the effective communication of sEMG recording and signal analysis methods. Highlighting the advantages of sEMG as a clinical tool and reducing its perceived complexity could bridge the gap between theoretical knowledge and practical application and provide the impetus for the widespread use of sEMG in clinic.

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References
1.
Stanton R, Ada L, Dean C, Preston E . Biofeedback improves activities of the lower limb after stroke: a systematic review. J Physiother. 2011; 57(3):145-55. DOI: 10.1016/S1836-9553(11)70035-2. View

2.
Webster J . Reducing motion artifacts and interference in biopotential recording. IEEE Trans Biomed Eng. 1984; 31(12):823-6. DOI: 10.1109/TBME.1984.325244. View

3.
HJORTH R, Walsh J, Willison R . The distribution and frequency of spontaneous fasciculations in motor neurone disease. J Neurol Sci. 1973; 18(4):469-74. DOI: 10.1016/0022-510x(73)90140-8. View

4.
Drost G, Stegeman D, van Engelen B, Zwarts M . Clinical applications of high-density surface EMG: a systematic review. J Electromyogr Kinesiol. 2006; 16(6):586-602. DOI: 10.1016/j.jelekin.2006.09.005. View

5.
Li X, Zhou P, Aruin A . Teager-Kaiser energy operation of surface EMG improves muscle activity onset detection. Ann Biomed Eng. 2007; 35(9):1532-8. DOI: 10.1007/s10439-007-9320-z. View