MULTISCANNING-EMG DE UNA AGUJA: DESARROLLO DEL SISTEMA Y OBTENCION DE UNA BASE DE DATOS DE NORMALIDAD

PID2019-109062RB-I00

Nombre agencia financiadora Agencia Estatal de Investigación
Acrónimo agencia financiadora AEI
Programa Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i
Subprograma Subprograma Estatal de Generación de Conocimiento
Convocatoria Proyectos I+D
Año convocatoria 2019
Unidad de gestión Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020
Centro beneficiario FUNDACIÓN INSTITUTO DE INVESTIGACIÓN SANITARIA DE NAVARRA
Identificador persistente http://dx.doi.org/10.13039/501100011033

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EMG probability density function: a new way to look at EMG signal filling from single motor unit potential to full interference pattern

Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
  • Navallas Irujo, Javier
  • Eciolaza Ferrando, Adrián
  • Mariscal, Cristina
  • 0000-0002-3122-9049
  • 0000-0002-9150-8955
An analytical derivation of the EMG signal's amplitude probability density function (EMG PDF) is presented and used to study how an EMG signal builds-up, or fills, as the degree of muscle contraction increases. The EMG PDF is found to change from a semi-degenerate distribution to a Laplacian-like distribution and finally to a Gaussian-like distribution. We present a measure, the EMG filling factor, to quantify the degree to which an EMG signal has been built-up. This factor is calculated from the ratio of two non-central moments of the rectified EMG signal. The curve of the EMG filling factor as a function of the mean rectified amplitude shows a progressive and mostly linear increase during early recruitment, and saturation is observed when the EMG signal distribution becomes approximately Gaussian. Having presented the analytical tools used to derive the EMG PDF, we demonstrate the usefulness of the EMG filling factor and curve in studies with both simulated signals and real signals obtained from the tibialis anterior muscle of 10 subjects. Both simulated and real EMG filling curves start within the 0.2 to 0.35 range and rapidly rise towards 0.5 (Laplacian) before stabilizing at around 0.637 (Gaussian). Filling curves for the real signals consistently followed this pattern (100% repeatability within trials in 100% of the subjects). The theory of EMG signal filling derived in this work provides (a) an analytically consistent derivation of the EMG PDF as a function of motor unit potentials and motor unit firing patterns; (b) an explanation of the change in the EMG PDF according to degree of muscle contraction; and (c) a way (the EMG filling factor) to quantify the degree to which an EMG signal has been built-up., This work was supported by the Ministerio de Ciencia e Innovacion of the Spanish Government under Grant PID2019-109062RB-I00.