Methods for estimating muscle fibre conduction velocity from surface electromyographic signals

Abstract

The review focuses on the methods currently available for estimating muscle fibre conduction velocity (CV) from surface electromyographic (EMG) signals. The basic concepts behind the issue of estimating CV from EMG signals are discussed. As the action potentials detected at the skin surface along the muscle fibres are, in practice, not equal in shape, the estimation of the delay of propagation (and thus of CV) is not a trivial task. Indeed, a strictly unique definition of delay does not apply in these cases. Methods for estimating CV can thus be seen as corresponding to specific definitions of the delay of propagation between signals of unequal shape. The most commonly used methods for CV estimation are then reviewed. Together with classic methods, recent approaches are presented. The techniques are described with common notations to underline their relationships and to highlight when an approach is a generalisation of a previous one or when it is based on new concepts. The review identifies the difficulties of CV estimation and underlines the issues that should be considered by the investigator when selecting a particular method and detection system for assessing muscle fibre CV. The many open issues in CV estimation are also presented.

On the nature of the electromyographic signals recorded during vibration exercise

“…without shape variation (Farina and Merletti 2004). However, the assumption of pure temporal delay is not completely fulfilled and changes in the shape of the detected surface signals along the muscle fiber direction can be expected (Farina and Merletti 2004).…”

“…However, the assumption of pure temporal delay is not completely fulfilled and changes in the shape of the detected surface signals along the muscle fiber direction can be expected (Farina and Merletti 2004). The coherence function C xy (f ) (Bendat and Piersol 1980) was therefore employed as a measure of the degree to which y is linearly related to x as a function of frequency f , where P xx (f ) and P yy (f ) are the power spectral densities of x and y, and P xy (f ) is the cross power spectral density of x and y.…”

“…Moreover, by use of the phase method, band-pass filtering to select the peaks corresponding to the vibration frequency is no longer necessary, therefore avoiding possible phase distortion introduced by filtering (Ritzmann et al 2010). However, the phase method presents some limitations for the assessment of conduction velocity in the presence of shape changes between EMG signals recorded in adjacent channels (Farina and Merletti 2004). This was the reason why the phase method was not adopted for the estimation of the CV for the entire EMG signals (CV EMG ), but only for the components at the vibration frequency, CV VF .…”

“…6c), especially at higher vibration frequencies. Moreover, to estimate CV VF , instead of directly calculating the phase difference between the two bipolar signals (Farina and Merletti 2004), we estimated the phase of the impulse response function between the auto-correlation and cross-correlation (Eq. 3), which has been reported to exhibit a sharper peak at the true delay between the two bipolar signals (Hunter et al 1987).…”

“…The MF can be estimated as the first statistical moment of the EMG amplitude spectrum (Merletti et al 1990). The CV of the motor unit action potentials along the muscle fibers can be estimated using two or more recording electrodes placed on the skin along the muscle fibers with known inter-electrode distance (Hunter et al 1987;Farina and Merletti 2004;Farina and Negro 2007;Mischi et al 2010;Rabotti et al 2010).…”