• DocumentCode
    2392484
  • Title

    Detection of chaos in human fatigue mechanomyogarphy signals

  • Author

    Xie, Hong-Bo ; Zheng, Yong-Ping ; Jing-Yi, Guo

  • Author_Institution
    Jiangsu Univ., Zhenjiang, China
  • fYear
    2009
  • fDate
    3-6 Sept. 2009
  • Firstpage
    4379
  • Lastpage
    4382
  • Abstract
    We undertake the study of the chaotic nature of mechanomygraphy (MMG) signal by recourse to the recent developments in the field of nonlinear dynamics. The MMG signals were measured from biceps brachii muscle of 5 subjects during fatigue of isometric contraction at 80% maximal voluntary contraction (MVC) level. Deterministic chaotic character was detected in all data by using the Volterra-Wiener-Korenberg model and noise titration approach. The noise limit (NL), which is a power indicator of chaos of fatigue MMG signals, is 22.2000plusmn8.7293. Furthermore, we studied the nonlinear dynamic features of MMG signals by computing their correlation dimension D2, which is 3.3524plusmn0.3645 across all the subjects. These results indicate that MMG is a high-dimensional chaotic signal and support the use of the theory of nonlinear dynamics for the analysis and modeling the MMG signals.
  • Keywords
    biomechanics; biomedical measurement; chaos; medical signal detection; muscle; neurophysiology; MMG signal analysis; MMG signal modeling; Volterra-Wiener-Korenberg model; biceps brachii muscle; chaos detection; correlation dimension; high-dimensional chaotic signal; human fatigue mechanomyogarphy signals; isometric contraction fatigue; maximal voluntary contraction; noise limit; noise titration; nonlinear dynamics; Algorithms; Biomechanics; Humans; Muscle Contraction; Muscle Fatigue; Muscle, Skeletal; Myography; Nonlinear Dynamics; Signal Processing, Computer-Assisted;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
  • Conference_Location
    Minneapolis, MN
  • ISSN
    1557-170X
  • Print_ISBN
    978-1-4244-3296-7
  • Electronic_ISBN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/IEMBS.2009.5333485
  • Filename
    5333485