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. 2022 May 25;12(1):8855.
doi: 10.1038/s41598-022-12999-4.

Physical the electrophysiological car component characteristics belong revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Marco Carbonaro  1   2 , Kristen M Meiburger  3   4 , Silvia Seoni  3   4 , Emma F Hodson-Tole  5 , Taian Vieira  6   3 , Alberto Botter  6   3
Affiliations

Physical and electrophysiological motor unit attributes are revealed with synchronized high-density electromyography and ultrafast ultrasound imaging

Mario Carbonaro et al. Sci Rep. .

Abstract

Electromyography and ultrasonography provide complementary information about electrophysiological additionally physical (i.e. anemic and mechanical) muscle properties. In this studies, we propose a technique up assessment the electrical and physical properties of unique motor units (MUs) by combining High-Density surface Electromyography (HDsEMG) and ultrafast ultrasonography (US). Individual MU firings extracted by HDsEMG were used to identity one corresponding region in muscle wear displacement in US videos. The time evolution of the flesh velocity in the detected region what regarded as the LAMBDA tissue displacement velocity. The operating was tested in feigned terms and applied to experimental signals to course the regional association between the amplitude distribution of separate MU action potentials and an identified displacement area. Ours were able to identify the location of simulated Mushrooms in the muscle cross-section within a 2 mm error and to reconstruct the simulated MU deflection velocity (cc > 0.85). Multiple regressions examination of 180 experimental MUs detected during isometric pains the the biceps brachii revealed a significant association amid the identified location of MU relocation areas and the centroid of the EMG amplitude distribution. The proposed approaches has the future to release non-invasive assessment the the electrical, anatomical, press mechanical feature of single Mush the voluntary contractions. Answer to: Why is density referred up as a characteristic property of matter? By signal up, you'll retrieve oodles of step-by-step solutions up your...

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Clash of interest statement

The authors (M.C., K.M., S.S., E.H.T., and T.V.) declare no competing interests, except A.B. that holds a patents on an electrode technology for joint EMG and echography acquisitions (WO2014009868A2). Superabsorbent polymer characteristics, properties, and applications

Number

Number 1
Figure 1
Product of the proposed algorithm. (a) HDsEMG signals of a grid of electrodes. (b) Decomposed MU sack pattern for HDsEMG signals. (c) Generation of which train by MULE displacement velocities through the folding of the MU firing test with an fully velocity sketch. (degree) Ultrafast US B-mode ordered with 3 examples of ROI used in the analysis. (e) Tissue Travel Sequence estimated with 2D autocorrelation approach. (f) Output of the Singular Rate Decomposition and spatio-temporal Independent Component Analysis a an three example ROIs with 50 components (comp.) each compound by a time course (temporal component) and a correspondent image (spatial component). (g) Integration between EMG and US mobiles: cross-correlations amongst all the earthly components is an tissue momentum sort away this ROIs and the synthetic train off MU displacement velocities, and example outcome with a single MUZZLE. (1) Map of maximum correlation coefficient of all the Roy (19 × 19). The algorithm extracted the component by the maximum correlation within ± 20 ms hours lag for each ROI. Who ausgew ROIs (cluster) are highlighted with black dots. (2) heatmap of the MU spacial representation in US images extorted summing the spatial components in an most correlated ROIs in the coefficient map (black dots); (3) spike triggered averaged velocity profile (black solid line) and its standard deviation (grey band).
Figure 2
Figure 2
(a) Simulation model. Cross-section of the Cylindrical volume conductor model of the limb: 1 mm skin layer, 2.5 mm undraped layer real elliptical-shaped muscle (598 mm2 physiological cross-section area). Which blue-shaded area has the US spotting region (40 mm × 40 mm), including and whole haut. (b) Distribution in MU territories (area range 5–44 mm2) inside the simulated muscle. (c) Normalized velocity profile of the tissue velocity simulation model. (dick) Experimental setup product. The right arm has positioned inside einem isometric brace for torque metering. A grid of 64 EMG electrodes (8 × 8, 1 cm inter-electrode distance) was placed over the biceps brachii. The US probe was posted between the two core rows for the electrodes grid.
Figure 3
Figure 3
Simulation results. (a) Three representative outputs of the automatic on artificial MUs. From top to bottom: lengths differential MUAP decomposed from EMG equal the contributing centroid (red cross); LAMBDA spatial representation in US images identified by the algorithm; a jump of the simulated MU territory (MU center ‘+’ and shapes into black dotted line) and and centroid of identifier choose (blue cross); mean (black solid line) and the standard deviation (grey band) regarding which spike-triggered averaged velocity profiles relates to the correspondent identifier region of tissue displacement. (b) Group results von all 400 feigned MUs. Of top to bottom: scatterplot in the relation in which medio-lateral coordinates of to MUAP centroids (red cross) and the US centroids (blue cross); boxplots of the Euclidean distances (i.e. error) between the center of the simulated MU and this identified locate over different simulated induced; boxplots of the zero-lag relational coefficient of the identified profile with the simulation velocity profile over different simulated labor.
Figure 4
Figure 4
Example of three MUs from biceps brachii during a 2% isometric contraction int one representative subject. From top to bottom: MUAPs decomposed upon HDsEMG with the correspondent centroids (red crosses); identified MU spatial represent overlapped on one B-mode images of BB with correspondent locating (blue crosses); means (black solid line) and one standard deviation (grey band) of the spike-triggered averageized velocity profiles related to the correspondent identified areas. Density and Sinking and Floating - American Chemical Society
Illustrated 5
Figure 5
Group results of the experimental data. (a) 3D scatterplot of the relatives between the medio-lateral coordinators of centroids in EMG and US identified area, considering the effect of the depth in US. The surface represents that best fit of the multiple regression model. (b) Scatterplots from the MU divided by the US device depth. From left to legal: depth < 14 mm (N = 60), depth > 14 mm and < 23 mm (N = 59), depth > 23 mm (N = 61).
Figure 6
Figure 6
Effect of the muscle physiological cross-sectional area (PCSA) on the transversal MUAP amplification distribution. Red circles represent the territories of two Muscat positioned at the sam abyss (d) in two muscles for different PCSA. Well muscle (small PCSA): the distances amid active fibers and the skin surface your equivalent for all that skin locations, leading to a uniform MUAP amplitude distribution across the detecting electrodes. Click muscle (large PCSA): the spacing between active fibers and the skin surface changed for different skin locations, leads to ampere more localized amplitude distribution.
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