Literature DB >> 19272923

Resolving superimposed MUAPs using particle swarm optimization.

Hamid Reza Marateb1, Kevin C McGill.   

Abstract

This paper presents an algorithm to resolve superimposed action potentials encountered during the decomposition of electromyographic signals. The algorithm uses particle swarm optimization with a variety of features including randomization, crossover, and multiple swarms. In a simulation study involving realistic superpositions of two to five motor-unit action potentials, the algorithm had an accuracy of 98%.

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Year:  2008        PMID： 19272923      PMCID： PMC2673334          DOI： 10.1109/TBME.2008.2005953

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN： 0018-9294            Impact factor:   4.538


  6 in total

1.  EMG signal decomposition: how can it be accomplished and used?

Authors:  D Stashuk
Journal:  J Electromyogr Kinesiol       Date:  2001-06       Impact factor: 2.368

2.  Optimal resolution of superimposed action potentials.

Authors:  Kevin C McGill
Journal:  IEEE Trans Biomed Eng       Date:  2002-07       Impact factor: 4.538

3.  Improved resolution of pulse superpositions in a knowledge-based system EMG decomposition.

Authors:  S Hamid Nawab; Robert Wotiz; Carlo J De Luca
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2004

4.  A genetic algorithm for the resolution of superimposed motor unit action potentials.

Authors:  Joël R Florestal; Pierre A Mathieu; Réjean Plamondon
Journal:  IEEE Trans Biomed Eng       Date:  2007-12       Impact factor: 4.538

5.  Resolving superimposed motor unit action potentials.

Authors:  H Etawil; D Stashuk
Journal:  Med Biol Eng Comput       Date:  1996-01       Impact factor: 2.602

6.  A procedure for decomposing the myoelectric signal into its constituent action potentials--Part I: Technique, theory, and implementation.

Authors:  R S LeFever; C J De Luca
Journal:  IEEE Trans Biomed Eng       Date:  1982-03       Impact factor: 4.538
  6 in total
  5 in total

1.  A noninvasive method for coronary artery diseases diagnosis using a clinically-interpretable fuzzy rule-based system.

Authors:  Hamid Reza Marateb; Sobhan Goudarzi
Journal:  J Res Med Sci       Date:  2015-03       Impact factor: 1.852

2.  A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning.

Authors:  Mohammad R Mohebian; Hamid R Marateb; Marjan Mansourian; Miguel Angel Mañanas; Fariborz Mokarian
Journal:  Comput Struct Biotechnol J       Date:  2016-12-06       Impact factor: 7.271

3.  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

4.  Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions.

Authors:  Parviz Ghaderi; Marjan Nosouhi; Mislav Jordanic; Hamid Reza Marateb; Miguel Angel Mañanas; Dario Farina
Journal:  Front Neurosci       Date:  2022-03-09       Impact factor: 4.677

5.  A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN.

Authors:  Asim Waris; Muhammad Zia Ur Rehman; Imran Khan Niazi; Mads Jochumsen; Kevin Englehart; Winnie Jensen; Heidi Haavik; Ernest Nlandu Kamavuako
Journal:  Sensors (Basel)       Date:  2020-06-15       Impact factor: 3.576
  5 in total

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