Speedup computation of HD-sEMG signals using a motor unit-specific electrical source model.
Med Biol Eng Comput
; 56(8): 1459-1473, 2018 Aug.
Article
in En
| MEDLINE
| ID: mdl-29359257
Nowadays, bio-reliable modeling of muscle contraction is becoming more accurate and complex. This increasing complexity induces a significant increase in computation time which prevents the possibility of using this model in certain applications and studies. Accordingly, the aim of this work is to significantly reduce the computation time of high-density surface electromyogram (HD-sEMG) generation. This will be done through a new model of motor unit (MU)-specific electrical source based on the fibers composing the MU. In order to assess the efficiency of this approach, we computed the normalized root mean square error (NRMSE) between several simulations on single generated MU action potential (MUAP) using the usual fiber electrical sources and the MU-specific electrical source. This NRMSE was computed for five different simulation sets wherein hundreds of MUAPs are generated and summed into HD-sEMG signals. The obtained results display less than 2% error on the generated signals compared to the same signals generated with fiber electrical sources. Moreover, the computation time of the HD-sEMG signal generation model is reduced to about 90% compared to the fiber electrical source model. Using this model with MU electrical sources, we can simulate HD-sEMG signals of a physiological muscle (hundreds of MU) in less than an hour on a classical workstation. Graphical Abstract Overview of the simulation of HD-sEMG signals using the fiber scale and the MU scale. Upscaling the electrical source to the MU scale reduces the computation time by 90% inducing only small deviation of the same simulated HD-sEMG signals.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Signal Processing, Computer-Assisted
/
Electricity
/
Electromyography
/
Models, Biological
/
Motor Neurons
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Med Biol Eng Comput
Year:
2018
Document type:
Article
Affiliation country:
France
Country of publication:
United States