Evidence map›Paper›PMID 29359257›Full record

ArticleMedical & biological engineering & computing2018

Speedup computation of HD-sEMG signals using a motor unit-specific electrical source model.

Vincent Carriou, Sofiane Boudaoud, Jeremy Laforet

Abstract read
PubMed Publisher
In one paragraph

Article in Medical & biological engineering & computing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Vincent CarriouCNRS UMR 7338 Biomechanics and Bioengineering, Centre de Recherche de Royallieu, Sorbonne University, Universite de Technologie de Compiegne, CS 60203, Compiegne, France. vincent.carriou@utc.fr.ORCID http://orcid.org/0000-0003-3807-0322
Sofiane BoudaoudCNRS UMR 7338 Biomechanics and Bioengineering, Centre de Recherche de Royallieu, Sorbonne University, Universite de Technologie de Compiegne, CS 60203, Compiegne, France.
Jeremy LaforetCNRS UMR 7338 Biomechanics and Bioengineering, Centre de Recherche de Royallieu, Sorbonne University, Universite de Technologie de Compiegne, CS 60203, Compiegne, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

ElectricityElectromyographyModels, BiologicalSignal Processing, Computer-AssistedAction PotentialsComputer SimulationHumansMotor NeuronsTime FactorsComputation timeHD-sEMG modelMotor unit electrical sourceOptimizationUpscaling

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.