DocumentCode :
1205291
Title :
AR modeling of myoelectric interference signals during a ramp contraction
Author :
Kiryu, Tohru ; De Luca, Carlo J. ; Saitoh, Yoshiaki
Author_Institution :
Dept. of Inf. Eng., Niigata Univ., Japan
Volume :
41
Issue :
11
fYear :
1994
Firstpage :
1031
Lastpage :
1038
Abstract :
The authors investigated the time-varying behavior of the autoregressive (AR) parameters in a myoelectric (ME) signal detected during a linear force increasing contraction. The AR parameters of interest mere the reflection coefficients, the AR model spectrum, and the prediction errors. The authors used well-conditioned ME signals for which the complete time record of the motor units firings was available. In addition, the influence of the recruitment of a new motor unit, the conduction velocity of action potentials, and additive broad-band noise were investigated using simulated ME signals. The simulated ME signals were constructed from a selected group of the available motor unit action potential trains. The results revealed that, as the contraction progressed, the AR parameters displayed a time-varying behavior which coincided with the recruitment of newly recruited motor units whose spectrum of the waveform differed from that of the rest of the ME signal. This property of the AR parameters was obscured by the presence of broad-band noise and low-amplitude motor unit action potentials, both of which are more pronounced during low-level force contractions.
Keywords :
bioelectric potentials; muscle; physiological models; stochastic processes; time series; additive broad-band noise; autoregressive parameters; conduction velocity; linear force increasing contraction; low-amplitude motor unit action potentials; low-level force contractions; motor units firings; motor units recruitment; myoelectric interference signals; prediction errors; ramp contraction; time-varying behavior; waveform spectrum; Additive noise; Fatigue; Fluctuations; Interference; Muscles; Predictive models; Recruitment; Reflection; Signal analysis; Signal detection; Action Potentials; Computer Simulation; Electrophysiology; Humans; Isometric Contraction; Models, Biological; Neural Conduction; Signal Processing, Computer-Assisted;
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/10.335841
Filename :
335841
Link To Document :
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