Time-varying delay estimators for measuring muscle fiber conduction velocity from the surface electromyogram. (September 2015)

• Record Type: Journal Article
• Authors: Ravier, Philippe; Farina, Dario; Buttelli, Olivier
• Is Part Of: Biomedical signal processing and control. Volume 22(2015)
• Page Start: 126
• Page End: 134
• Publication Date: 2015-09
• Subjects: Electromyography -- Time-varying delay estimators -- Conduction velocity -- Time-frequency representations; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.06.008
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Graphical abstract: Abstract : Highlights: Muscle fiber conduction velocity estimators during dynamic exercises are proposed. Time-varying delay estimators are compared by Monte–Carlo simulations. TFTS methods are robust to noise and accurate for the conduction velocity tracking. Abstract: Muscle fiber conduction velocity (MFCV) can be measured by estimating the time delay between surface EMG signals recorded by electrodes aligned with the fiber direction. In the case of dynamic contractions, the EMG signal is highly non-stationary and the time delay between recording sites may vary rapidly over time. Thus, the processing methods usually applied in the case of static contractions do not hold anymore and the delay estimation requires processing techniques that are adapted to non-stationary conditions. The current paper investigates several methods based on time-frequency approaches or adaptive filtering in order to solve the time-varying delay estimation problem. These approaches are theoretically analyzed and compared by Monte–Carlo simulations in order to determine if their performance is sufficient for practical applications. Moreover, results obtained on experimental signals recorded during cycling from the vastus medialis muscle are also shown. The study presents for the first time a set of approaches for instantaneous delay estimation from two-channels EMG signals.