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Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study

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Abstract

In this work, we propose to classify, by simulation, the shape variability (or non-Gaussianity) of the surface electromyogram (sEMG) amplitude probability density function (PDF), according to contraction level, using high-order statistics (HOS) and a recent functional formalism, the core shape modeling (CSM). According to recent studies, based on simulated and/or experimental conditions, the sEMG PDF shape seems to be modified by many factors as: contraction level, fatigue state, muscle anatomy, used instrumentation, and also motor control parameters. For sensitivity evaluation against these several sources (physiological, instrumental, and neural control) of variability, a large-scale simulation (25 muscle anatomies, ten parameter configurations, three electrode arrangements) is performed, by using a recent sEMG–force model and parallel computing, to classify sEMG data from three contraction levels (20, 50, and 80 % MVC). A shape clustering algorithm is then launched using five combinations of HOS parameters, the CSM method and compared to amplitude clustering with classical indicators [average rectified value (ARV) and root mean square (RMS)]. From the results screening, it appears that the CSM method obtains, using Laplacian electrode arrangement, the highest classification scores, after ARV and RMS approaches, and followed by one HOS combination. However, when some critical confounding parameters are changed, these scores decrease. These simulation results demonstrate that the shape screening of the sEMG amplitude PDF is a complex task which needs both efficient shape analysis methods and specific signal recording protocol to be properly used for tracking neural drive and muscle activation strategies with varying force contraction in complement to classical amplitude estimators.

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Abbreviations

ARV:

Average rectified value

CNS:

Central nervous system

CSM:

Core shape model

CV:

Conduction velocity

DD:

Double differential

E:

Excitatory drive

Fr:

Firing rate

HOS:

High-order statistics

IAP:

Intracellular action potentials

IPI:

The inter-pulse interval, the random time interval between two consecutive motor unit firings

IZ:

Innervation zone

Kr:

Kurtosis

L:

Linear

Lap:

Laplacian

MFPV:

Muscle fiber propagation velocity

MP:

Monopolar

MU:

Motor unit

MUAP:

Motor unit action potential

MUAPTs:

Motor unit action potential trains

MVC:

Maximum voluntary contraction

NL:

Non-linear

NP:

Non-propagating

PFRD:

Min–max peak firing rate difference

RMS:

Root mean square

RTE:

Recruitment threshold

sEMG:

Surface electromyogram

SD:

Single differential

SFAP:

Single fiber action potential

Sk:

Skewness

SNR:

Signal to noise ratio

TJ:

myo-Tendinous junction

CoVIPI :

Coefficient of variation of the IPIs

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Acknowledgments

The proposed study was partially supported by the National Sciences and Engineering Council of Canada (NSERC Grant CRDPJ 400014-10) and by a Ph.D. grant from the French ministry of research.

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Authors and Affiliations

  1. Multimodal Interaction Laboratory, SIS-McGill University, Montreal, Canada

    F. S. Ayachi

  2. BMBI Laboratory, Université de Technologie de Compiègne – CNRS UMR 7338, Compiègne, France

    S. Boudaoud & C. Marque

Authors
  1. F. S. Ayachi
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  2. S. Boudaoud
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  3. C. Marque
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Correspondence to S. Boudaoud.

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Ayachi, F.S., Boudaoud, S. & Marque, C. Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study. Med Biol Eng Comput 52, 673–684 (2014). https://doi.org/10.1007/s11517-014-1170-x

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  • DOI: https://doi.org/10.1007/s11517-014-1170-x

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