Abstract
We evaluated different muscle excitation estimation techniques, and their sensitivity to Motor Unit (MU) distribution in muscle tissue. For this purpose, the Convolution Kernel Compensation (CKC) method was used to identify the MU spike trains from High-Density ElectroMyoGrams (HDEMG). Afterwards, Cumulative MU Spike Train (CST) was calculated by summing up the identified MU spike trains. Muscle excitation estimation from CST was compared to the recently introduced Cumulative Motor Unit Activity Index (CAI) and classically used Root-Mean-Square (RMS) amplitude envelop of EMG. To emphasize their dependence on the MU distribution further, all three muscle excitation estimates were used to calculate the agonist-antagonist co-activation index. We showed on synthetic HDEMG that RMS envelopes are the most sensitive to MU distribution (10 % dispersion around the real value), followed by the CST (7 % dispersion) and CAI (5 % dispersion). In experimental HDEMG from wrist extensors and flexors of post-stroke subjects, RMS envelopes yielded significantly smaller excitations of antagonistic muscles than CST and CAI. As a result, RMS-based co-activation estimates differed significantly from the ones produced by CST and CAI, illuminating the problem of large diversity of muscle excitation estimates when multiple muscles are studied in pathological conditions. Similar results were also observed in experimental HDEMG of six intact young males.
Highlights
Estimation of muscle excitation from surface electromyograms (EMG) is a long-standing problem that has been tackled by many different studies [7], [9], [12], [13], [16]
The Convolution Kernel Compensation (CKC) technique cancels out the Motor Unit Action Potentials (MUAPs) in the model (1) and estimates the Motor Unit (MU) spike train as [6]: tj (n) = ctTj yC−y 1y (n) ≈ctTj tC−t 1t(n) where Cy=E y(n) yT (n) and Ct(n) =E t(n)tT (n) stand for the correlation matrix of High-Density ElectroMyoGrams (HDEMG) and MU spike trains, respectively, with E denoting mathematical expectation, and ct j y=E t j (n) yT (n) and ct j t=E t j (n)tT (n) are cross-correlation vectors
The variability of the Cumulative MU Spike Train (CST) method depended significantly on the number of identified MUs, and Standard Deviation (SD) of CoA estimates increased to 13 ± 4 %, 22 ± 13 % and 26 ± 15 % when only 20, 10 and 5 MUs were used for CST calculation in Eq (6)
Summary
Estimation of muscle excitation from surface electromyograms (EMG) is a long-standing problem that has been tackled by many different studies [7], [9], [12], [13], [16]. Most of the existing studies base the muscle excitation estimation on amplitude envelopes of EMG that are easy to calculate, but contain two different classes of information. By building on the all-or-nothing principle of motor neuron activation, CNS uses frequency modulation to govern the movements of skeletal muscles In the muscles, these binary codes get amplified electrically and filtered by the Motor Unit Action Potentials (MUAPs) [3], [4], which form the second class of information in EMG. Beneficial when determining peripheral nervous system properties like Motor Unit Conduction Velocity [15], the MUAP shapes carry no information on neural codes and should, be removed from the muscle excitation estimates. The compromise between these two factors needs to be carefully evaluated
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