Literature DB >> 8897205

Analysis of the electromyographic interference pattern.

D B Sanders1, E V Stålberg, S D Nandedkar.   

Abstract

The electromyographic interference pattern (EMG-IP) contains information about the number, firing rate, and recruitment characteristics of motor units, and information regarding the waveforms of the recruited motor units. Muscle and nerve diseases produce characteristic changes in the IP that can be distinguished by IP analysis. This analysis complements analysis of the motor unit potentials. The electromyographer usually assesses the IP signals subjectively by their appearance on the oscilloscope screen and by their sound on the audio monitor. Techniques have been developed to automate IP analysis with and without force monitoring. These techniques give objective information, quantitate the degree of abnormality, and permit electromyographers-in-training to compare their subjective analysis of the IP with more objective findings.

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Year:  1996        PMID: 8897205     DOI: 10.1097/00004691-199609000-00003

Source DB:  PubMed          Journal:  J Clin Neurophysiol        ISSN: 0736-0258            Impact factor:   2.177


  8 in total

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Authors:  W Thomas Gregory; Amanda L Clark; Kimberly Simmons; Jau-Shin Lou
Journal:  Int Urogynecol J Pelvic Floor Dysfunct       Date:  2008-02-05

2.  Estimating the tendency of motor unit recruitment during steady-hold and rapid contractions using surface EMG and Turns-amplitude analysis.

Authors:  Li-Ling Pan; Chung-Huang Yu; Mei-Wun Tsai; Shun-Hwa Wei; Li-Wei Chou
Journal:  Eur J Appl Physiol       Date:  2015-07-23       Impact factor: 3.078

3.  Concentric Needle Quantitative EMG of Pubovisceralis Muscle Group: Normative Data from Asymptomatic Nulliparous Women.

Authors:  W Thomas Gregory; Teresa Worstell; Amanda L Clark; Jau-Shin Lou
Journal:  Female Pelvic Med Reconstr Surg       Date:  2010-05-01       Impact factor: 2.091

4.  Editorial: Inference, Causality and Control in Networks of Dynamical Systems: Data Science and Modeling Perspectives to Network Physiology With Implications for Artificial Intelligence.

Authors:  Paul Bogdan; Plamen Ch Ivanov; Sergio Pequito
Journal:  Front Physiol       Date:  2022-05-11       Impact factor: 4.755

5.  Quantitative anal sphincter electromyography in primiparous women with anal incontinence.

Authors:  W Thomas Gregory; Jau-Shin Lou; Kimberly Simmons; Amanda L Clark
Journal:  Am J Obstet Gynecol       Date:  2008-05       Impact factor: 8.661

6.  Dual simulated childbirth injuries result in slowed recovery of pudendal nerve and urethral function.

Authors:  Hai-Hong Jiang; Hui Q Pan; Marcus A Gustilo-Ashby; Bradley Gill; Jonathan Glaab; Paul Zaszczurynski; Margot Damaser
Journal:  Neurourol Urodyn       Date:  2009       Impact factor: 2.696

7.  Clinical and neurophysiological characterization of muscular weakness in severe COVID-19.

Authors:  Francesco Bax; Christian Lettieri; Alessandro Marini; Gaia Pellitteri; Andrea Surcinelli; Mariarosaria Valente; Riccardo Budai; Vincenzo Patruno; Gian Luigi Gigli
Journal:  Neurol Sci       Date:  2021-03-23       Impact factor: 3.307

8.  Recruitment in retractor bulbi muscle during eyeblink conditioning: EMG analysis and common-drive model.

Authors:  N F Lepora; J Porrill; C H Yeo; C Evinger; P Dean
Journal:  J Neurophysiol       Date:  2009-08-12       Impact factor: 2.714

  8 in total

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