Decomposition of Surface EMG Signals

Abstract

This report describes an early version of a technique for decomposing surface electromyographic (sEMG) signals into the constituent motor unit (MU) action potential trains. A surface sensor array is used to collect four channels of differentially amplified EMG signals. The decomposition is achieved by a set of algorithms that uses a specially developed knowledge-based Artificial Intelligence framework. In the automatic mode the accuracy ranges from 75 to 91%. An Interactive Editor is used to increase the accuracy to > 97% in signal epochs of about 30-s duration. The accuracy was verified by comparing the firings of action potentials from the EMG signals detected simultaneously by the surface sensor array and by a needle sensor. We have decomposed up to six MU action potential trains from the sEMG signal detected from the orbicularis oculi, platysma, and tibialis anterior muscles. However, the yield is generally low, with typically < or = 5 MUs per contraction. Both the accuracy and the yield should increase as the algorithms are developed further. With this technique it is possible to investigate the behavior of MUs in muscles that are not easily studied by needle sensors. We found that the inverse relationship between the recruitment threshold and the firing rate previously reported for muscles innervated by spinal nerves is also present in the orbicularis oculi and the platysma, which are innervated by cranial nerves. However, these two muscles were found to have greater and more widespread values of firing rates than those of large limb muscles.

The detection of long-range correlations of operation force and sEMG with multifractal detrended fluctuation analysis

“…However, the physiological relevance of the present findings was addressed briefly in this paper due to the scale and limitation of this study, but such work has been planned in a further study in the near future. We will be required to identify whether the precise force-sEMG correlation function expression can be used for force signal measurement, and for the development of new models [2,3] that could account for the scale-invariant outputs for different types of feedback systems.…”

“…(1) and (2) Third, the value of the maximum correlation and its delay, called the maximum correlation delay (MCD), can be derived by using Eqs. (1) and (2). From the spectrum analysis of the cross-correlation sequence, the range of the maximum delay of in Eq.…”

Resistência ao movimento e atividade eletromiográfica dos músculos flexores e extensores de cotovelo em pacientes hemiparéticos espásticos submetidos à crioterapia e estimulação elétrica neuromuscular

“…Os dados da EMG em 150 °/s complementam esses achados, pois revelam um aumento do RMS 10, 20 e 30 minutos pós-crioterapia, que sugere maior número de unidades motoras ativadas (De Luca et al, 2006). Essa diminuição da resistência ao movimento passivo e o aumento da atividade eletromiográfica nos permite sugerir que houve uma diminuição da hipertonia espástica pós-tratamento e melhora da função muscular do antagonista ao espástico, promovendo o sinergismo muscular.…”

EMG Signal Decomposition Using Motor Unit Potential Train Validity

“…While these two basic assumptions apply to the decomposition of both surface and intramuscular EMG signals, the methods used to accomplish decomposition can be quite different for intramuscular versus surface signals. And although the decomposition of multi-channel surface EMG 13 signals is of interest and progress is being made towards being able to decompose such signals [38][39][40][41][42], this chapter focuses on the decomposition of intramuscular EMG signals because the main objective of this thesis is to improve the of intramuscular EMG signal decomposition results and facilitate using such results for clinical applications of quantitative electromyography.…”