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
Link To Document :
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