Influence of estimators of spectral density on the analysis of electromyographic and vibromyographic signals.
Electromyographic (EMG) and vibromyographic (VMG) signals are related to electrical and mechanical muscle activity, respectively. It is known that variations in their frequency components are related to changes in muscle activity and fatigue. The aims of this study were: (1) to analyse the resolution, variance and bias of different estimations of power spectral density function (PSD); and (2) to evaluate the influence of the spectral estimation method on three indices calculated from the PSD of EMG and VMG signals: mean (f(m)) and median (f(c)) frequencies and the ratio of high and low frequency components (H/L ratio) to select the most suitable estimator. Myographic signals were recorded from the sternomastoid muscle, an accessory respiratory muscle, during breathing. For non-parametric methods, Welch periodograms and correlograms were analysed with different windows. Autoregressive (AR) moving average (MA) and ARMA models with different orders were evaluated in the parametric methods. The reproducibility of the results was also studied. Frequency indices, particularly the H/L ratio and f(c), changed considerably when varying the following parameters of the estimators: periodogram with segment durations longer than 150 ms in EMG and with any duration in VMG signals; correlogram with window length shorter than 10% of the total number of samples; and AR models with an order lower than 10, 20 and 40 in f(c), fm and H/L ratio, respectively, in both myographic signals.
Mañanas
M A
MA
Department of Automatic Control (ESAII), Biomedical Engineering Research Center, Technical University of Catalonia (UPC), Barcelona, Spain. manyanas@creb.upc.es
Jané
R
R
Fiz
J A
JA
Morera
J
J
Caminal
P
P
D004576:Electromyography / Q000379:methods; D006801:Humans; D009213:Myography / Q000379:methods*; D015203:Reproducibility of Results; D012119:Respiration; D012132:Respiratory Muscles / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic; D014732:Vibration