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Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles.[1] EMG is performed using an instrument called an electromyograph, to produce a record called an electromyogram. An electromyograph detects the electrical potential generated by muscle cells[2] when these cells are electrically or neurologically activated. The signals can be analyzed to detect medical abnormalities, activation level, recruitment order or to analyze the biomechanics of human or animal movement.

Contents

Electrical characteristics

The electrical source is the muscle membrane potential of about -90 mV.[3] Measured EMG potentials range between less than 50 μV and up to 20 to 30 mV, depending on the muscle under observation.

Typical repetition rate of muscle motor unit firing is about 7–20 Hz, depending on the size of the muscle (eye muscles versus seat (gluteal) muscles), previous axonal damage and other factors. Damage to motor units can be expected at ranges between 450 and 780 mV.[citation needed]

History

The first documented experiments dealing with EMG started with Francesco Redi’s works in 1666. Redi discovered a highly specialized muscle of the electric ray fish (Electric Eel) generated electricity. By 1773, Walsh had been able to demonstrate that the Eel fish’s muscle tissue could generate a spark of electricity. In 1792, a publication entitled De Viribus Electricitatis in Motu Musculari Commentarius appeared, written by Luigi Galvani, in which the author demonstrated that electricity could initiate muscle contractions. Six decades later, in 1849, Dubios-Raymond discovered that it was also possible to record electrical activity during a voluntary muscle contraction. The first actual recording of this activity was made by Marey in 1890, who also introduced the term electromyography. In 1922, Gasser and Erlanger used an oscilloscope to show the electrical signals from muscles. Because of the stochastic nature of the myoelectric signal, only rough information could be obtained from its observation. The capability of detecting electromyographic signals improved steadily from the 1930s through the 1950s, and researchers began to use improved electrodes more widely for the study of muscles. Clinical use of surface EMG (sEMG) for the treatment of more specific disorders began in the 1960s. Hardyck and his researchers were the first (1966) practitioners to use sEMG. In the early 1980s, Cram and Steger introduced a clinical method for scanning a variety of muscles using an EMG sensing device.

It is not until the middle of the 1980s that integration techniques in electrodes had sufficiently advanced to allow batch production of the required small and lightweight instrumentation and amplifiers. At present, a number of suitable amplifiers are commercially available. In the early 1980s, cables that produced signals in the desired microvolt range became available. Recent research has resulted in a better understanding of the properties of surface EMG recording. Surface electromyography is increasingly used for recording from superficial muscles in clinical or kinesiological protocols, where intramuscular electrodes are used for investigating deep muscles or localized muscle activity.

There are many applications for the use of EMG. EMG is used clinically for the diagnosis of neurological and neuromuscular problems. It is used diagnostically by gait laboratories and by clinicians trained in the use of biofeedback or ergonomic assessment. EMG is also used in many types of research laboratories, including those involved in biomechanics, motor control, neuromuscular physiology, movement disorders, postural control, and physical therapy.

Procedure

There are two kinds of EMG in widespread use: surface EMG and intramuscular (needle and fine-wire) EMG. To perform intramuscular EMG, a needle electrode or a needle containing two fine-wire electrodes is inserted through the skin into the muscle tissue. A trained professional (most often a physiatrist or neurologist) observes the electrical activity while inserting the electrode. The insertional activity provides valuable information about the state of the muscle and its innervating nerve. Normal muscles at rest make certain, normal electrical sounds when the needle is inserted into them. Then the electrical activity when the muscle is at rest is studied. Abnormal spontaneous activity might indicate some nerve and/or muscle damage. Then the patient is asked to contract the muscle smoothly. The shape, size, and frequency of the resulting motor unit potentials are judged. Then the electrode is retracted a few millimeters, and again the activity is analyzed until at least 10–20 units have been collected. Each electrode track gives only a very local picture of the activity of the whole muscle. Because skeletal muscles differ in the inner structure, the electrode has to be placed at various locations to obtain an accurate study.

Intramuscular EMG may be considered too invasive or unnecessary in some cases. Instead, a surface electrode may be used to monitor the general picture of muscle activation, as opposed to the activity of only a few fibres as observed using a intramuscular EMG. This technique is used in a number of settings; for example, in the physiotherapy clinic, muscle activation is monitored using surface EMG and patients have an auditory or visual stimulus to help them know when they are activating the muscle (biofeedback).

A motor unit is defined as one motor neuron and all of the muscle fibers it innervates. When a motor unit fires, the impulse (called an action potential) is carried down the motor neuron to the muscle. The area where the nerve contacts the muscle is called the neuromuscular junction, or the motor end plate. After the action potential is transmitted across the neuromuscular junction, an action potential is elicited in all of the innervated muscle fibers of that particular motor unit. The sum of all this electrical activity is known as a motor unit action potential (MUAP). This electrophysiologic activity from multiple motor units is the signal typically evaluated during an EMG. The composition of the motor unit, the number of muscle fibres per motor unit, the metabolic type of muscle fibres and many other factors affect the shape of the motor unit potentials in the myogram.

Nerve conduction testing is also often done at the same time as an EMG to diagnose neurological diseases.

Some patients can find the procedure somewhat painful, whereas others experience only a small amount of discomfort when the needle is inserted. The muscle or muscles being tested may be slightly sore for a day or two after the procedure.

Normal results

Muscle tissue at rest is normally electrically inactive. After the electrical activity caused by the irritation of needle insertion subsides, the electromyograph should detect no abnormal spontaneous activity (i.e., a muscle at rest should be electrically silent, with the exception of the area of the neuromuscular junction, which is, under normal circumstances, very spontaneously active). When the muscle is voluntarily contracted, action potentials begin to appear. As the strength of the muscle contraction is increased, more and more muscle fibers produce action potentials. When the muscle is fully contracted, there should appear a disorderly group of action potentials of varying rates and amplitudes (a complete recruitment and interference pattern).

Abnormal results

EMG is used to diagnose two general categories of disease: neuropathies and myopathies.

Neuropathic disease has the following defining EMG characteristics:

Myopathic disease has these defining EMG characteristics:

  • A decrease in duration of the action potential
  • A reduction in the area to amplitude ratio of the action potential
  • A decrease in the number of motor units in the muscle (in extremely severe cases only)

Because of the individuality of each patient and disease, some of these characteristics may not appear in every case.

Abnormal results may be caused by the following medical conditions (please note this is nowhere near an exhaustive list of conditions that can result in abnormal EMG studies):

EMG signal decomposition

EMG signals are essentially made up of superimposed motor unit action potentials (MUAPs) from several motor units. For a thorough analysis, the measured EMG signals can be decomposed into their constituent MUAPs. MUAPs from different motor units tend to have different characteristic shapes, while MUAPs recorded by the same electrode from the same motor unit are typically similar. Notably MUAP size and shape depend on where the electrode is located with respect to the fibers and so can appear to be different if the electrode moves position. EMG decomposition is non-trivial, although many methods have been proposed.

Applications of EMG

EMG signals are used in many clinical and biomedical applications. EMG is used as a diagnostics tool for identifying neuromuscular diseases, assessing low-back pain, kinesiology, and disorders of motor control. EMG signals are also used as a control signal for prosthetic devices such as prosthetic hands, arms, and lower limbs.

EMG can be used to sense isometric muscular activity where no movement is produced. This enables definition of a class of subtle motionless gestures to control interfaces without being noticed and without disrupting the surrounding environment. These signals can be used to control a prosthesis or as a control signal for an electronic device such as a mobile phone or PDA.

EMG signals have been targeted as control for flight systems. The Human Senses Group at the NASA Ames Research Center at Moffett Field, CA seeks to advance man-machine interfaces by directly connecting a person to a computer. In this project, an EMG signal is used to substitute for mechanical joysticks and keyboards. EMG has also been used in research towards a "wearable cockpit," which employs EMG-based gestures to manipulate switches and control sticks necessary for flight in conjunction with a goggle-based display.

Unvoiced speech recognition recognizes speech by observing the EMG activity of muscles associated with speech. It is targeted for use in noisy environments, and may be helpful for people without vocal cords and people with aphasia.

EMG has also been used as a control signal for computers and other devices. An interface device based on EMG could be used to control moving objects, such as mobile robots or an electric wheelchair.[4] This may be helpful for individuals that cannot operate a joystick-controlled wheelchair. Surface EMG recordings may also be a suitable control signal for some interactive video games.[5]

A joint project involving Microsoft, the University of Washington in Seattle, and the University of Toronto in Canada has explored using muscle signals from hand gestures as an interface device.[6] A patent based on this research was submitted on June 26, 2008. [7]

See also

Notes

  1. ^ Kamen, Gary. Electromyographic Kinesiology. In Robertson, DGE et al. Research Methods in Biomechanics. Champaign, IL: Human Kinetics Publ., 2004.
  2. ^ MeSH Electromyography
  3. ^ Nigg B.M., & Herzog W., 1999. Biomechanics of the Musculo-Skeletal system. Wiley. Page:349.
  4. ^ Andreasen, DS.; Gabbert DG,: EMG Switch Navigation of Power Wheelchairs, RESNA 2006. [1]
  5. ^ Park, DG.; Kim, HC. Muscleman: Wireless input device for a fighting action game based on the EMG signal and acceleration of the human forearm. [2]
  6. ^ Hsu, Jeremy (2009-10-29). "The Future of Video Game Input: Muscle Sensors". Live Science. http://www.livescience.com/technology/091029-ttr-muscle-sensing.html. Retrieved 2010-01-16. 
  7. ^ "Recognizing Gestures from Forearm EMG Signals". United States Patent and Trademark Office. 2008-06-26. http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220090327171%22.PGNR.&OS=DN/20090327171&RS=DN/20090327171. Retrieved 2010-01-16. 

References

  • M. B. I. Reaz, M. S. Hussain, F. Mohd-Yasin, “Techniques of EMG Signal Analysis: Detection, Processing, Classification and Applications”, Biological Procedures Online, vol. 8, issue 1, pp. 11–35, March 2006
  • Nikias CL, Raghuveer MR. Bispectrum estimation: A digital signal processing framework. IEEE Proceedings on Communications and Radar. 1987;75(7):869–891.
  • Basmajian, JV.; de Luca, CJ. Muscles Alive - The Functions Revealed by Electromyography. The Williams & Wilkins Company; Baltimore, 1985.
  • Graupe D, Cline WK. Functional separation of EMG signals via ARMA identification methods for prosthesis control purposes. IEEE Transactions on Systems, Man and Cybernetics, 1975;5(2):252-259.
  • Kleissen RFM, Buurke JH, Harlaar J, Zilvold G. Electromyography in the biomechanical analysis of human movement and its clinical application. Gait Posture. 1998;8(2):143–158. doi: 10.1016/S0966-6362(98)00025-3. [PubMed]
  • Cram, JR.;Kasman, GS.; Holtz, J. Introduction to Surface Electromyography. Aspen Publishers Inc.; Gaithersburg, Maryland, 1998.
  • Ferguson, S.; Dunlop, G. Grasp Recognition From Myoelectric Signals. Procedures Australasian Conference Robotics and Automation 2002; pp. 78–83.
  • Stanford V. Biosignals offer potential for direct interfaces and health monitoring. Pervasive Computing, IEEE. 2004;3(1):99–103.
  • Wheeler KR, Jorgensen CC. Gestures as input: neuroelectric joysticks and keyboards. Pervasive Computing, IEEE. 2003;2(2):56–61.
  • Manabe, H.;Hiraiwa, A.; Sugimura, T. Unvoiced Speech Recognition using EMG-Mime Speech Recognition. Conference on Human Factors in Computing Systems 2003; pp. 794–795.

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