Effects of increased buffering capacity on electromyogram\'s power spectrum density behaviour during muscle fatigue - Becman, Eric Cito (2019)
Surface electromyography (EMG) is a non-invasive technique to measure on-going changes in the electrical potentials of a muscle of interest. The EMG record is known to display alterations when assessed throughout sustained contractions. For instance, a frequency shift and compression of the signal\'s power spectrum density towards lower-frequencies. It is believed that a reduction of muscle fibre conduction velocity explains at least partially such dynamics. Additionally, the muscle acidosis observed at fatiguing contractions of some exercises seems to alter both conduction velocity and frequency properties. Within this context, the main goal of the present thesis was to explore the effects of muscle buffering capacity manipulation on the EMG power spectrum. More specifically, the mean power frequency (MNF). For manipulating buffering capacity, chronic β -alanine supplementation was used. The randomised and placebo-controlled study measured MNF throughout isometric contractions at submaximal and acidotic intensity (50% of maximal voluntary contraction force) before and after a ?-alanine or placebo supplementation period of 28 days. The β-alanine administration was found to increase muscle endurance, expected result according to the literature. Nonetheless, no visible effect was detected for MNF fatigue behaviour. Under theoretical assumptions, the fatigue behaviour of MNF should change when differences at muscle endurance are found. Thus, the lack of a statistically significant result was discussed in the face of the multifaceted nature of myoelectric signals, composed by influences of several biological and non-biological elements. Secondarily, investigation of EMG signal complexity via a1ApEn is also presented in the body of the thesis, which makes contextual sense, as it had a crucial contribution on the development of methodological aspects of the protocol employed in the main investigation and the overall Master\'s project. Additionally, EMG signal complexity is an underexplored topic and with little information known. Hence, a1ApEn provides potential to assess different EMG information not yet provided by better-documented tools, such as MNF