Variability and interrelationships of surface EMG parameters during local muscle fatigue

Abstract

The inter- and intraindividual variability of the frequency power density spectral and surface EMG amplitude parameters and of the muscle fiber conduction velocity (MFCV) is studied in 26 healthy volunteers during fatiguing isometric ischemic intermittent exercise of the m. biceps brachii at 80% of the maximal voluntary contraction level, with a contraction rate of 30/min. No significant age effects were found. Males were significantly stronger compared with females. The higher initial SEMG amplitude and the stronger shift of the frequency power density spectrum (PDS) to lower frequencies appear to be significantly correlated with males. Fatigue induces an almost proportional compression of the SEMG frequency content. The muscle fiber conduction velocity has the highest intraindividual reproducibility (r = 0.81). Despite the definite and strong influence of the MFCV on the PDS, the shift of the PDS can not be explained by a change of MFCV alone.

A Comparative Study in Cervical Muscle Activities during Various Resting Postures Using Electromyography

“…Rise in EMG amplitude while sustained contraction could be ascribed both muscular and neural variations induced by fatigue. Estimation of SCM's mean frequency (MNF) performs decrease over time in all situations, in agreement with a compression of the EMG spectrum while sustained contraction [34,35] . Density spectrum of surface EMG expresses the brief whole action potential distribution that is recorded by EMG.…”

The utility of interference pattern analysis

“…142 With increasing time of sustained isometric contraction, either maximal or submaximal, median and mean power of the power spectrum analysis decreases (Fig. 2), probably due to a decrease in conduction velocity along the fatigued muscle fibers and possibly due to synchronization, 2,3,10,13,16,21,28,30,42,118,122,133,134,137,151,155,156,158,171 although the latter statement is controversial. 42 The decrease in conduction velocity along the fatigued muscle fiber is not fully understood but may be due to low intracellular pH and increased extracellular potassium concentration.…”

A Muscle Fibre Conduction Velocity Tracking ASIC for Local Fatigue Monitoring

“…Algorithms using this approach rely on finding characteristic spectral frequencies of the ME signal. Lindstrom et al [8] developed a mathematical model linking MFCV with the observed EMG power spectrum. Thus, a shift in the power spectrum can account for a change in MFCV.…”