Original Editor - Ahmet Kocyigit

Top Contributors - Ahmet Kocyigit and Kim Jackson

Introduction[edit | edit source]

Electromyography (EMG) is one of the many electrodiagnostic tests conducted to study the electrical functions of the human body. [1] While there are many different types of sensors and protocols for EMG, it can be described as both a neurological and a musculoskeletal test that is targeted to the peripheral nervous system pathway.

Definition[edit | edit source]

Electromyography is a process in which the electrical signals of the muscles are captured via an electrode. EMG tests can provide data about the impulses from the nerves responsible for contraction and the reactions of the muscle fibers to the said impulses. [2] Depending on the device used, the resulting raw data can be exported as a graph called an electromyograph, therefore in some cases giving the name electromyography to the original test.

Nerve Conduction Studies (Electromyelogram)[edit | edit source]

EMG tests are generally followed by an electromyelogram otherwise called NCSs (Nerve Conduction Studies). Being a more active testing approach than EMG, NCSs include electrical inputs to observe the reaction of the nerves more specifically. Parameters such as action potential, latency, amplitude and conduction velocity are observed with EMG after an artificial electrical signal is administered. Data gathered from an NCS can be used to determine the type and extent of damage to a nerve. [3]

Clinical Physiology[edit | edit source]

In an electromyograph, a value called "Motor Unit Potential" (MUP) is observed for the target muscle. These are the electrical potential created by the muscle to execute a voluntary contraction. Inferences are then made from these MUPs depending on the frequency, amplitude and time taken to generate the contraction.

EMG uses the electrophysiological properties of muscles to allow the differentiation of myopathy from neuropathy and can help determine the type and progression of these conditions, which may include; [4]

Types of EMG[edit | edit source]

Needle EMG[edit | edit source]

The electrode is placed within the tip of a needle, which is inserted into the target muscle and can be relocated as necessary. Needle EMG is the preferred method for diagnostic purposes due to being more targeted and reliable than a surface electrode. Although the process is considered safe, the potential risks of pain, bleeding, infection, and pneumothorax remain as a result of the needle being used. [5]

Surface EMG (sEMG)[edit | edit source]

Surface measurements of muscle activity are generally reserved for research purposes. Using an adhesive electrode on the skin over the targeted area enables an easier test. However, a singular superficial electrode measurement picks up signals from multiple muscle fibers and all the tissue in between, compromising signal integrity thus making it non-viable for diagnostic uses. [6]

EMG in Rehabilitation[edit | edit source]

Electromyography has also found uses within certain fields of rehabilitation, biofeedback therapy being one of them. Conditioning of biological action is a proven concept. This approach has been successfully used with visual and auditory feedback in the past, and converting the outputs of an electromyograph into similar feedback had varying degrees of success in coordinating muscle movement for the muscles of the pelvic floor. [7] The same principle also showed promise for patients with recent knee surgeries although to a lesser extent. [8] Alternative uses for the surface variance of EMG have also been tested to find mixed results, one of them being the inspiratory muscles. [9]

EMG in Research[edit | edit source]

Electrophysiological properties of the human body are still a subject of vigorous study due to the intricacies and complexity of the nervous system as a whole. EMG has been proven to be an invaluable tool in collecting data and helped build some of the current concepts of the musculoskeletal system in the literature. As research progresses, combined use of EMG with other types of electrodiagnostic tools resulted in a vast array of studies to discover and evaluate new approaches for rehabilitation such as motor imagery and sensorial feedback. [10] Studies aiming to implement EMG into more specific areas such as activities of daily living have also been prevalent, especially with the progress of technological adaptations of EMG. [11]

Technological Research and Development[edit | edit source]

Thanks to the multidisciplinary research on the subject, EMG has not remained only to be a clinical testing device. From gait analysis [12] to wheelchairs using a human-machine interface with EMG sensors [13] this technology proved to be an exciting prospect. This potential also paved the way for sensor technology to become more accessible and less costly. [14]

SparkFun Electronics Muscle Sensor v3.jpeg

While it is impossible to refuse the fact that the problems of objectivity undoubtedly increased proportionally with the ease of access to these devices [6], it also allowed for many new areas and approaches to come to life in the field of rehabilitation, much like the 3d printing technology.

References[edit | edit source]

  1. Electromyography: MedlinePlus Medical Encyclopedia [Internet]. [cited 2022 Nov 27]. Available from: https://medlineplus.gov/ency/article/003929.htm
  2. Chernecky CC, Berger BJ. Laboratory Tests and Diagnostic Procedures. Elsevier Health Sciences; 2012. 1235 p.
  3. Silver J. Chapter 1 - What is an EMG? In: Weiss L, Silver J, Weiss J, editors. Easy EMG [Internet]. Edinburgh: Butterworth-Heinemann; 2004 [cited 2022 Nov 28]. p. 1–4. Available from: https://www.sciencedirect.com/science/article/pii/B9780750674317500063
  4. Fournier E, Tabti N. Chapter 16 - Clinical electrophysiology of muscle diseases and episodic muscle disorders. In: Levin KH, Chauvel P, editors. Handbook of Clinical Neurology [Internet]. Elsevier; 2019 [cited 2022 Nov 28]. p. 269–80. (Clinical Neurophysiology: Diseases and Disorders; vol. 161). Available from: https://www.sciencedirect.com/science/article/pii/B9780444641427000539
  5. Rubin DI. Chapter 16 - Needle electromyography: Basic concepts. In: Levin KH, Chauvel P, editors. Handbook of Clinical Neurology [Internet]. Elsevier; 2019 [cited 2022 Nov 28]. p. 243–56. (Clinical Neurophysiology: Basis and Technical Aspects; vol. 160). Available from: https://www.sciencedirect.com/science/article/pii/B9780444640321000163
  6. 6.0 6.1 Felici F, Del Vecchio A. Surface Electromyography: What Limits Its Use in Exercise and Sport Physiology? Frontiers in Neurology [Internet]. 2020 [cited 2022 Nov 28];11. Available from: https://www.frontiersin.org/articles/10.3389/fneur.2020.578504
  7. Patcharatrakul T, Pitisuttithum P, Rao SSC, Gonlachanvit S. Chapter 37 - Biofeedback therapy. In: Rao SSC, Lee YY, Ghoshal UC, editors. Clinical and Basic Neurogastroenterology and Motility [Internet]. Academic Press; 2020 [cited 2022 Nov 28]. p. 517–32. Available from: https://www.sciencedirect.com/science/article/pii/B9780128130377000376
  8. Xie YJ, Wang S, Gong QJ, Wang JX, Sun FH, Miyamoto A, et al. Effects of electromyography biofeedback for patients after knee surgery: A systematic review and meta-analysis. J Biomech. 2021 May 7;120:110386.
  9. Dos Reis IMM, Ohara DG, Januário LB, Basso-Vanelli RP, Oliveira AB, Jamami M. Surface electromyography in inspiratory muscles in adults and elderly individuals: A systematic review. J Electromyogr Kinesiol. 2019 Feb;44:139–55.
  10. Brambilla C, Pirovano I, Mira RM, Rizzo G, Scano A, Mastropietro A. Combined Use of EMG and EEG Techniques for Neuromotor Assessment in Rehabilitative Applications: A Systematic Review. Sensors (Basel). 2021 Oct 22;21(21):7014.
  11. Jarque-Bou NJ, Sancho-Bru JL, Vergara M. A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues. Sensors (Basel). 2021 Apr 26;21(9):3035.
  12. Nandy A, Chakraborty S, Chakraborty J, Venture G. 8 - A low-cost electromyography (EMG) sensor-based gait activity analysis. In: Nandy A, Chakraborty S, Chakraborty J, Venture G, editors. Modern Methods for Affordable Clinical Gait Analysis [Internet]. Academic Press; 2021 [cited 2022 Nov 28]. p. 101–27. Available from: https://www.sciencedirect.com/science/article/pii/B9780323852456000102
  13. Kaur A. Wheelchair control for disabled patients using EMG/EOG based human machine interface: a review. J Med Eng Technol. 2021 Jan;45(1):61–74.
  14. Clark RA, Thilarajah S, Williams G, Kahn M, Heywood S, Tan HH, et al. Chapter 1 - Kits for wearable sensor systems: exploring software and hardware system design, building guides, and opportunities for clinical rehabilitation. In: Godfrey A, Stuart S, editors. Digital Health [Internet]. Academic Press; 2021 [cited 2022 Nov 28]. p. 1–25. Available from: https://www.sciencedirect.com/science/article/pii/B9780128189146000107