Homomorphic deconvolution for MUAP estimation from surface EMG signals

G Biagetti, P Crippa, S Orcioni… - IEEE journal of …, 2016 - ieeexplore.ieee.org
IEEE journal of biomedical and health informatics, 2016ieeexplore.ieee.org
This paper presents a technique for parametric model estimation of the motor unit action
potential (MUAP) from the surface electromyography (sEMG) signal by using homomorphic
deconvolution. The cepstrum-based deconvolution removes the effect of the stochastic
impulse train, which originates the sEMG signal, from the power spectrum of sEMG signal
itself. In this way, only information on MUAP shape and amplitude were maintained, and
then, used to estimate the parameters of a time-domain model of the MUAP itself. In order to …
This paper presents a technique for parametric model estimation of the motor unit action potential (MUAP) from the surface electromyography (sEMG) signal by using homomorphic deconvolution. The cepstrum-based deconvolution removes the effect of the stochastic impulse train, which originates the sEMG signal, from the power spectrum of sEMG signal itself. In this way, only information on MUAP shape and amplitude were maintained, and then, used to estimate the parameters of a time-domain model of the MUAP itself. In order to validate the effectiveness of this technique, sEMG signals recorded during several biceps curl exercises have been used for MUAP amplitude and time scale estimation. The parameters so extracted as functions of time were used to evaluate muscle fatigue showing a good agreement with previously published results.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果