Document Detail


Detection of hidden rhythms in surface EMG signals with a non-linear time-series tool.
MedLine Citation:
PMID:  10624740     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The analysis of the surface electromyographic (sEMG) signal is particularly attractive because it provides relatively easy access to those physiological processes that allow the muscle to generate force and movement. In this paper, one of the possible applications of recurrence plot strategy to the analysis of sEMG is described. Recurrence Quantification Analysis (RQA) is an efficient time-series analysis tool pertaining to the class of non-linear dynamics time-domain processing. We analysed sEMG recorded on the biceps brachii during isometric contraction both at constant (CF) and non constant force (NCF). For comparison purposes, experimental data were analysed over epochs of 1 s so that the hypothesis of sEMG stationarity could be accepted. The analysis concerned one of the most widely used frequency parameters (namely the median frequency, MDF) and one parameter (i.e., the percent determinism %DET) extracted using the non-linear technique. Our main results are: (i) the gross average evaluated for all subjects on %DET data shows a comparable variation with respect to MDF throughout the course of CF experiments; (ii) %DET seems able to detect motor unit (MU) synchronisation; (iii) during non constant force experiments, %DET is more effective than MDF in detecting sEMG changes determined by brisk transients of force output.
Authors:
G Filligoi; F Felici
Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Medical engineering & physics     Volume:  21     ISSN:  1350-4533     ISO Abbreviation:  Med Eng Phys     Publication Date:    1999 Jul-Sep
Date Detail:
Created Date:  2000-01-28     Completed Date:  2000-01-28     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9422753     Medline TA:  Med Eng Phys     Country:  ENGLAND    
Other Details:
Languages:  eng     Pagination:  439-48     Citation Subset:  IM    
Affiliation:
Department INFOCOM, Faculty of Engineering, Università degli studi la Sapienza, Rome, Italy. filligoi@infocom.ing.uniromal.it
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MeSH Terms
Descriptor/Qualifier:
Electromyography / instrumentation,  methods*,  statistics & numerical data
Female
Humans
Isometric Contraction / physiology
Least-Squares Analysis
Linear Models
Male
Muscle, Skeletal / physiology
Nonlinear Dynamics*
Surface Properties
Time Factors

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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