Literature DB >> 15709654

Physiologically based simulation of clinical EMG signals.

Andrew Hamilton-Wright1, Daniel W Stashuk.   

Abstract

An algorithm that generates electromyographic (EMG) signals consistent with those acquired in a clinical setting is described. Signals are generated using a model constructed to closely resemble the physiology and morphology of skeletal muscle, combined with line source models of commonly used needle electrodes positioned in a way consistent with clinical studies. The validity of the simulation routines is demonstrated by comparing values of statistics calculated from simulated signals with those from clinical EMG studies of normal subjects. The simulated EMG signals may be used to explore the relationships between muscle structure and activation and clinically acquired EMG signals. The effects of motor unit (MU) morphology, activation, and neuromuscular junction activity on acquired signals can be analyzed at the fiber, MU and muscle level. Relationships between quantitative features of EMG signals and muscle structure and activation are discussed.

Mesh:

Year:  2005        PMID: 15709654     DOI: 10.1109/TBME.2004.840501

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


  8 in total

1.  A muscle architecture model offering control over motor unit fiber density distributions.

Authors:  Javier Navallas; Armando Malanda; Luis Gila; Javier Rodríguez; Ignacio Rodríguez
Journal:  Med Biol Eng Comput       Date:  2010-06-10       Impact factor: 2.602

2.  Adaptive certainty-based classification for decomposition of EMG signals.

Authors:  Sarbast Rasheed; Daniel Stashuk; Mohamed Kamel
Journal:  Med Biol Eng Comput       Date:  2006-03-23       Impact factor: 2.602

3.  Comparative evaluation of motor unit architecture models.

Authors:  Javier Navallas; Armando Malanda; Luis Gila; Javier Rodriguez; Ignacio Rodriguez
Journal:  Med Biol Eng Comput       Date:  2009-08-25       Impact factor: 2.602

4.  Techniques of EMG signal analysis: detection, processing, classification and applications.

Authors:  M B I Raez; M S Hussain; F Mohd-Yasin
Journal:  Biol Proced Online       Date:  2006-03-23       Impact factor: 3.244

5.  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

6.  Evaluating Muscle Synergies With EMG Data and Physics Simulation in the Neurorobotics Platform.

Authors:  Benedikt Feldotto; Cristian Soare; Alois Knoll; Piyanee Sriya; Sarah Astill; Marc de Kamps; Samit Chakrabarty
Journal:  Front Neurorobot       Date:  2022-07-12       Impact factor: 3.493

7.  Parkinson's Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer.

Authors:  Rafael Anicet Zanini; Esther Luna Colombini
Journal:  Sensors (Basel)       Date:  2020-05-03       Impact factor: 3.576

8.  Can Wavelet Denoising Improve Motor Unit Potential Template Estimation?

Authors:  Hasanzadeh S H; Parsaei H; Movahedi M M
Journal:  J Biomed Phys Eng       Date:  2020-04-01
  8 in total

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