The term “muscle fatigue” is used to denote a transient decrease in the capacity to perform physical actions (Enoka and Duchateau, 2008), and it can be measured as a change in electromyographic activity (Edwards, 1981). In the present study, the EMG signals were decomposed by the wavelet technique and then quantified by principal component analysis. Wavelet analysis is a technique that provides information on the time-frequency variation of the signal so that the amplitude, timing and frequency content can all be resolved simultaneously. Wavelets in non-linearly scaled time- frequency windows can provide an optimal time or frequency resolution for the non- stationary EMG signal during dynamic contractions. When the muscle is fatigued, a strengthening of low frequency components and a reduction in intensity of high frequency components modifies the spectrum of the SEMG signal (Singh et al., 2007). Principal component analysis (PCA) can be applied to quantify spectra shifts. The relative signal frequency components associated with the contribution of high and low frequency content within the EMG signal were explained by the angle formed between the PCI and PCII loading scores(θ). Higher values of θ represent a relatively large low frequency component, while lower values of θ represent a relatively large high frequency component (Hodson-Tole and Wakeling, 2008b). It has been shown that the θ is very sensitive to the frequency shift that corresponds to spectral differences between types of MUs in both fine wire (Hodson-Tole and Wakeling, 2007) and surface EMG (Wakeling, 2004; Wakeling and Rozitis, 2004; Wakeling et al., 2006).
In the present study, fatigue-related changes in the EMG data were identified as an increase of EMG intensity and a decrease of EMG MPF as a function of %endurance time for the tested muscles (except BB) under both fast and slow speed conditions. Our findings were in agreement with previous investigations showing that a
compression of the power spectrum to lower frequencies is typically observed during a fatiguing contraction (Bonato et al., 2001; Dimitrova and Dimitrov, 2003). The increase in EMG intensity might be due to one or more of several factors including the recruitment of additional MUs to compensate for the loss of force (Dimitrova and Dimitrov, 2003), impaired excitation-contraction coupling(Stephenson et al., 1995; Lamb, 2002), increased firing rate, and/or synchronization of motor unit recruitment (Freund, 1983; Newham et al., 1983).
The angle θ is formed by the first two principal components of the spectra. Principal component analysis extracts the important features in the signal, so some variables, such as movement artifacts (De Luca, 1997), were given lower order components and were excluded. In the present study, the first two principal components (PCI and PCII), accounting for more than 85% of the EMG signal, were considered. It has been shown that θ to be very sensitive to the frequency shift that corresponds to spectral differences between difference types of motor units(Wakeling, 2009b). A higher value of θ represents relatively more low frequency signal content and it can be associated with the recruitment of slower motor units. A smaller θ value,
associated with relatively more high frequency content, can be associated with the recruitment of faster motor units (Hodson-Tole and Wakeling, 2008b).
The time-dependent shift in mean power frequency (MPF) of electromyographic (EMG) signals to lower frequency components during the fatigue process was reflected by the changes in θ. Higher-frequency source spectra are generated by faster motor units due to the faster conduction velocity of their motor unit action potentials (Wakeling, 2009b), however, faster motor units fatigue more quickly. It is expected an increase in θ during fatigue, as reflected by the progressive fatigue of faster motor units being recruited. The θ increased almost linearly with % endurance time, there was a significant difference between the first and last endurance time window.
The changes in θ during fatigue may be influenced by the initial increase of motor unit recruitment and subsequent de-recruitment of later-recruited faster motor units. The significantly higher θ values in the last endurance time window reflect the relatively large low frequency content, which is associated with a higher proportion of slow motor units. Slow motor units are more fatigue resistant and can provide sufficient force a longer duration. The fatiguing phase correlated with a decrease in MPF and an increasing degree of fatigue of faster motor units, while the mechanical endurance time reflected the output level of mainly slower motor units (Gerdle et al., 1989; Minning et al., 2007).
As shown in the Fig.8.2, the EMG intensity lines become progressively steeper in the fast speed condition than in slow speed condition as a function of % endurance time,
size of recruited motor units increased for the higher mechanical requirement within individual muscles. On the other hand, the increase in θ and decline in MPF were greater for the fast speed than for the slow speed, which may be due to early de- recruitment of more fatigable faster MUs, possibly compensated by a firing rate increase (Freund, 1983) or by synchronization of MUs (Krogh-Lund and Jorgensen, 1993; Holtermann et al., 2009). Faster MUs fatigue more rapidly and so would not be able to sustain force production over a prolonged period of time. The relation
between size of motor unit and fatigability thus makes functional sense. Slow MUs, which develop relatively lower tension, are resistant to fatigue, while the fast MUs, which develop large tension, are fatigued quickly and may be activated for brief duration.
MPF and θ, determined by time-frequency analysis and PCA in the present study, showed sensitive and consistent changes in terms of muscle fatigue at low-moderate levels of wheelchair propulsion. Particularly, changes in θ are associated with
recruitment of different types and size of motor units, suggesting that the θ. has potential as a fatigue index.