Literature DB >> 21175408

Intramuscular EMG signal decomposition.

Hossein Parsaei1, Daniel W Stashuk, Sarbast Rasheed, Charles Farkas, Andrew Hamilton-Wright.   

Abstract

Information regarding motor unit potentials (MUPs) and motor unit fi ring patterns during muscle contractions is useful for physiological investigation and clinical examinations either for the understanding of motor control or for the diagnosis of neuromuscular disorders. In order to obtain such information, composite electromyographic (EMG) signals are decomposed (i.e., resolved into their constituent motor unit potential trains [MUPTs]). The goals of automatic decomposition techniques are to create a MUPT for each motor unit that contributed significant MUPs to the original composite signal. Diagnosis can then be facilitated by decomposing a needle-detected EMG signal, extracting features of MUPTs, and finally analyzing the extracted features (i.e., quantitative electromyography). Herein, the concepts of EMG signals and EMG signal decomposition techniques are explained. The steps involved with the decomposition of an EMG signal and the methods developed for each step, along with their strengths and limitations, are discussed and compared. Finally, methods developed to evaluate decomposition algorithms and assess the validity of the obtained MUPTs are reviewed and evaluated.

Entities:  

Mesh:

Year:  2010        PMID: 21175408     DOI: 10.1615/critrevbiomedeng.v38.i5.20

Source DB:  PubMed          Journal:  Crit Rev Biomed Eng        ISSN: 0278-940X


  12 in total

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Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2018-11-20       Impact factor: 3.802

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5.  A Novel Framework Based on FastICA for High Density Surface EMG Decomposition.

Authors:  Maoqi Chen; Ping Zhou
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2015-03-11       Impact factor: 3.802

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7.  Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition.

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Journal:  Neural Plast       Date:  2016-08-25       Impact factor: 3.599

8.  Comparative Analysis of Wavelet-based Feature Extraction for Intramuscular EMG Signal Decomposition.

Authors:  M Ghofrani Jahromi; H Parsaei; A Zamani; M Dehbozorgi
Journal:  J Biomed Phys Eng       Date:  2017-12-01

9.  Intramuscular EMG Decomposition Basing on Motor Unit Action Potentials Detection and Superposition Resolution.

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Journal:  Front Neurol       Date:  2018-01-23       Impact factor: 4.003

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Journal:  Med Biol Eng Comput       Date:  2019-01-26       Impact factor: 2.602

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