Negentropy analysis of surface electromyogram signal

Author

Nazarpour, Kianoush ; Sharafat, Ahmad R. ; Firoozabadi, S.M.

Author_Institution

Dept. of Electr. Eng., Tarbiat Modarres Univ., Tehran

fYear

2005

fDate

17-20 July 2005

Firstpage

974

Lastpage

977

Abstract

This study deals with measuring the non-Gaussianity in surface electromyogram signal (sEMG). The signal was obtained from biceps brachii muscle during elbow flexion at four different levels of maximum voluntary contraction (MVC). Typically the sEMG generated from constant-force, constant angle, non-fatiguing contractions is modelled as a stochastic process, and its probability density function (pdf) is assumed to be Gaussian. Results of utilizing negentropy for characterizing the non-Gaussianity of sEMG signal indicate that its pdf is clearly non-Gaussian during light contractions (below 30% of MVC) and it tends to a Gaussian process at higher force levels. The results validate the application of higher order statistics (HOS) based methods in sEMG signal processing at low levels of MVC

Keywords

electromyography; entropy; higher order statistics; medical signal processing; stochastic processes; EMG; biceps brachii muscle; elbow flexion; higher order statistics; maximum voluntary contraction; negentropy analysis; nonGaussianity; probability density function; stochastic process; surface electromyogram signal; Electromyography; Gaussian distribution; Gaussian processes; Independent component analysis; Laplace equations; Muscles; Signal analysis; Signal processing; Stochastic processes; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on

Conference_Location

Novosibirsk

Print_ISBN

0-7803-9403-8

Type

conf

DOI

10.1109/SSP.2005.1628736

Filename

1628736