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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MDPI AG
2021-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/16/2022 |
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| author | Luca Mesin |
| author_facet | Luca Mesin |
| author_sort | Luca Mesin |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-03-10T08:51:59Z |
| format | Article |
| id | doaj.art-c16849de67e449849ee188a792a2be13 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T08:51:59Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c16849de67e449849ee188a792a2be132023-11-22T07:26:03ZengMDPI AGElectronics2079-92922021-08-011016202210.3390/electronics10162022Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface ElectromyogramLuca Mesin0Mathematical Biology and Physiology, Department of Electronic and Telecommunication, Politecnico di Torino, 10129 Turin, ItalySurface 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).https://www.mdpi.com/2079-9292/10/16/2022motor unit firing ratemotor unit synchronizationsurface EMGiterative reweighted least squaresL<sub>1</sub> optimizationdeconvolution |
| spellingShingle | Luca Mesin Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram Electronics motor unit firing rate motor unit synchronization surface EMG iterative reweighted least squares L<sub>1</sub> optimization deconvolution |
| title | Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram |
| title_full | Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram |
| title_fullStr | Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram |
| title_full_unstemmed | Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram |
| title_short | Motor Unit Discharges from Multi-Kernel Deconvolution of Single Channel Surface Electromyogram |
| title_sort | motor unit discharges from multi kernel deconvolution of single channel surface electromyogram |
| topic | motor unit firing rate motor unit synchronization surface EMG iterative reweighted least squares L<sub>1</sub> optimization deconvolution |
| url | https://www.mdpi.com/2079-9292/10/16/2022 |
| work_keys_str_mv | AT lucamesin motorunitdischargesfrommultikerneldeconvolutionofsinglechannelsurfaceelectromyogram |