sEMG wavelet-based indices predicts muscle power loss during dynamic contractions |
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Authors: | M González-Izal I Rodríguez-Carreño A Malanda F Mallor-Giménez I Navarro-Amézqueta EM Gorostiaga M Izquierdo |
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Institution: | 1. Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India;2. Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada;1. Department of Physiotherapy, St. Poelten University of Applied Sciences, Austria;2. Institute for Sciences and Services in Health, St. Poelten University of Applied Sciences, Austria;3. Department of Biomechanics, Kinesiology and Applied Computer Science, ZSU, University of Vienna, Austria |
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Abstract: | The purpose of this study was to investigate the sensitivity of new surface electromyography (sEMG) indices based on the discrete wavelet transform to estimate acute exercise-induced changes on muscle power output during a dynamic fatiguing protocol. Fifteen trained subjects performed five sets consisting of 10 leg press, with 2 min rest between sets. sEMG was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were computed. These were: mean rectified voltage (MRV), median spectral frequency (Fmed), Dimitrov spectral index of muscle fatigue (FInsm5), as well as five other parameters obtained from the stationary wavelet transform (SWT) as ratios between different scales. The new wavelet indices showed better accuracy to map changes in muscle power output during the fatiguing protocol. Moreover, the new wavelet indices as a single parameter predictor accounted for 46.6% of the performance variance of changes in muscle power and the log-FInsm5 and MRV as a two-factor combination predictor accounted for 49.8%. On the other hand, the new wavelet indices proposed, showed the highest robustness in presence of additive white Gaussian noise for different signal to noise ratios (SNRs). The sEMG wavelet indices proposed may be a useful tool to map changes in muscle power output during dynamic high-loading fatiguing task. |
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