Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram

Surface electromyogram (EMG) finds many applications in the non-invasive characterization of muscles. Extracting information on the control of motor units (MU) is difficult when using single channels, e.g., due to the low selectivity and large phase cancellations of MU action potentials (MUAPs). In...
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Autor*in:	Luca Mesin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	motor unit firing rate motor unit synchronization surface EMG iterative reweighted least squares L deconvolution

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 10(2021), 16, p 2022
Übergeordnetes Werk:	volume:10 ; year:2021 ; number:16, p 2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics10162022

Katalog-ID:	DOAJ085557757

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language	English
source	In Electronics 10(2021), 16, p 2022 volume:10 year:2021 number:16, p 2022
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topic_facet	motor unit firing rate motor unit synchronization surface EMG iterative reweighted least squares L<sub<1</sub< optimization deconvolution Electronics
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container_title	Electronics
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Extracting information on the control of motor units (MU) is difficult when using single channels, e.g., due to the low selectivity and large phase cancellations of MU action potentials (MUAPs). In this paper, we propose a new method to face this problem in the case of a single differential channel. The signal is approximated as a sum of convolutions of different kernels (adapted to the signal) and firing patterns, whose sum is the estimation of the cumulative MU firings. Three simulators were used for testing: muscles of parallel fibres with either two innervation zones (IZs, thus, with MUAPs of different phases) or one IZ and a model with fibres inclined with respect to the skin. Simulations were prepared for different fat thicknesses, distributions of conduction velocity, maximal firing rates, synchronizations of MU discharges, and variability of the inter-spike interval. The performances were measured in terms of cross-correlations of the estimated and simulated cumulative MU firings in the range of 0–50 Hz and compared with those of a state-of-the-art single-kernel algorithm. 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callnumber-first	T - Technology
author	Luca Mesin
spellingShingle	Luca Mesin misc TK7800-8360 misc motor unit firing rate misc motor unit synchronization misc surface EMG misc iterative reweighted least squares misc L<sub<1</sub< optimization misc deconvolution misc Electronics Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram
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topic_title	TK7800-8360 Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram motor unit firing rate motor unit synchronization surface EMG iterative reweighted least squares L<sub<1</sub< optimization deconvolution
topic	misc TK7800-8360 misc motor unit firing rate misc motor unit synchronization misc surface EMG misc iterative reweighted least squares misc L<sub<1</sub< optimization misc deconvolution misc Electronics
topic_unstemmed	misc TK7800-8360 misc motor unit firing rate misc motor unit synchronization misc surface EMG misc iterative reweighted least squares misc L<sub<1</sub< optimization misc deconvolution misc Electronics
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title	Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram
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title_full	Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram
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title_sort	motor unit discharges from multi-kernel deconvolution of single channel surface electromyogram
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title_auth	Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram
abstract	Surface electromyogram (EMG) finds many applications in the non-invasive characterization of muscles. Extracting information on the control of motor units (MU) is difficult when using single channels, e.g., due to the low selectivity and large phase cancellations of MU action potentials (MUAPs). In this paper, we propose a new method to face this problem in the case of a single differential channel. The signal is approximated as a sum of convolutions of different kernels (adapted to the signal) and firing patterns, whose sum is the estimation of the cumulative MU firings. Three simulators were used for testing: muscles of parallel fibres with either two innervation zones (IZs, thus, with MUAPs of different phases) or one IZ and a model with fibres inclined with respect to the skin. Simulations were prepared for different fat thicknesses, distributions of conduction velocity, maximal firing rates, synchronizations of MU discharges, and variability of the inter-spike interval. The performances were measured in terms of cross-correlations of the estimated and simulated cumulative MU firings in the range of 0–50 Hz and compared with those of a state-of-the-art single-kernel algorithm. The median cross-correlations for multi-kernel/single-kernel approaches were 92.2%/82.4%, 98.1%/97.6%, and 95.0%/91.0% for the models with two IZs, one IZ (parallel fibres), and inclined fibres, respectively (all statistically significant differences, which were larger when the MUAP shapes were of greater difference).
abstractGer	Surface electromyogram (EMG) finds many applications in the non-invasive characterization of muscles. Extracting information on the control of motor units (MU) is difficult when using single channels, e.g., due to the low selectivity and large phase cancellations of MU action potentials (MUAPs). In this paper, we propose a new method to face this problem in the case of a single differential channel. The signal is approximated as a sum of convolutions of different kernels (adapted to the signal) and firing patterns, whose sum is the estimation of the cumulative MU firings. Three simulators were used for testing: muscles of parallel fibres with either two innervation zones (IZs, thus, with MUAPs of different phases) or one IZ and a model with fibres inclined with respect to the skin. Simulations were prepared for different fat thicknesses, distributions of conduction velocity, maximal firing rates, synchronizations of MU discharges, and variability of the inter-spike interval. The performances were measured in terms of cross-correlations of the estimated and simulated cumulative MU firings in the range of 0–50 Hz and compared with those of a state-of-the-art single-kernel algorithm. The median cross-correlations for multi-kernel/single-kernel approaches were 92.2%/82.4%, 98.1%/97.6%, and 95.0%/91.0% for the models with two IZs, one IZ (parallel fibres), and inclined fibres, respectively (all statistically significant differences, which were larger when the MUAP shapes were of greater difference).
abstract_unstemmed	Surface electromyogram (EMG) finds many applications in the non-invasive characterization of muscles. Extracting information on the control of motor units (MU) is difficult when using single channels, e.g., due to the low selectivity and large phase cancellations of MU action potentials (MUAPs). In this paper, we propose a new method to face this problem in the case of a single differential channel. The signal is approximated as a sum of convolutions of different kernels (adapted to the signal) and firing patterns, whose sum is the estimation of the cumulative MU firings. Three simulators were used for testing: muscles of parallel fibres with either two innervation zones (IZs, thus, with MUAPs of different phases) or one IZ and a model with fibres inclined with respect to the skin. Simulations were prepared for different fat thicknesses, distributions of conduction velocity, maximal firing rates, synchronizations of MU discharges, and variability of the inter-spike interval. The performances were measured in terms of cross-correlations of the estimated and simulated cumulative MU firings in the range of 0–50 Hz and compared with those of a state-of-the-art single-kernel algorithm. The median cross-correlations for multi-kernel/single-kernel approaches were 92.2%/82.4%, 98.1%/97.6%, and 95.0%/91.0% for the models with two IZs, one IZ (parallel fibres), and inclined fibres, respectively (all statistically significant differences, which were larger when the MUAP shapes were of greater difference).
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title_short	Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram
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Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram

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