Estimation of surface electromyogram spectral alteration using reduced-order autoregressive model

Abstract

A new method is proposed, based on the pole phase angle (PPA) of a second-order autoregressive (AR) model, to track spectral alteration during localised muscle fatigue when analysing surface myo-electric (ME) signals. Both stationary and non-stationary, simulated and real ME signals are used to investigate different methods to track spectral changes. The real ME signals are obtained from three muscles (the right vastus lateralis, rectus femoris and vastus medialis) of six healthy male volunteers, and the simulated signals are generated by passing Gaussian white-noise sequences through digital filters with spectral properties that mimic the real ME signals. The PPA method is compared, not only with spectra-based methods, such as Fourier and AR, but also with zero crossings (ZCs) and the first AR coefficient that have been proposed in the literature as computer efficient methods. By comparing the deviation (dev), in percent, between the linear regression of the theoretical and estimated mean frequencies of the power spectra for simulated stationary (s) and non-stationary (ns) signals, in general, it is found that the PPA method (dev_s=4.29; dev_ns=1.94) gives a superior performance to ZCs (dv_s=8.25) and the first AR coefficient (4.18<dev_s<21.8; 0.98<dev_ns<4.36) but performs slightly worse than spectra-based methods (0.33<dev_s<0.79; 0.41<dev_ns<1.07). However, the PPA method has the advantage that it estimates spectral alteration without calculating the spectra and therefore allows very efficient computation.