Cargando…
Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform
This study examined methods for estimating the innervation zone (IZ) of a muscle using recorded monopolar high density M waves. Two IZ estimation methods based on principal component analysis (PCA) and Radon transform (RT) were examined. Experimental M waves, acquired from the biceps brachii muscles...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050562/ https://www.ncbi.nlm.nih.gov/pubmed/37008017 http://dx.doi.org/10.3389/fphys.2023.1137146 |
_version_ | 1785014666829758464 |
---|---|
author | Huang, Chengjun Lu, Zhiyuan Chen, Maoqi Klein, Cliff S. Zhang, Yingchun Li, Sheng Zhou, Ping |
author_facet | Huang, Chengjun Lu, Zhiyuan Chen, Maoqi Klein, Cliff S. Zhang, Yingchun Li, Sheng Zhou, Ping |
author_sort | Huang, Chengjun |
collection | PubMed |
description | This study examined methods for estimating the innervation zone (IZ) of a muscle using recorded monopolar high density M waves. Two IZ estimation methods based on principal component analysis (PCA) and Radon transform (RT) were examined. Experimental M waves, acquired from the biceps brachii muscles of nine healthy subjects were used as testing data sets. The performance of the two methods was evaluated by comparing their IZ estimations with manual IZ detection by experienced human operators. Compared with manual detection, the agreement rate of the estimated IZs was 83% and 63% for PCA and RT based methods, respectively, both using monopolar high density M waves. In contrast, the agreement rate was 56% for cross correlation analysis using bipolar high density M waves. The mean difference in estimated IZ location between manual detection and the tested method was 0.12 ± 0.28 inter-electrode-distance (IED) for PCA, 0.33 ± 0.41 IED for RT and 0.39 ± 0.74 IED for cross correlation-based methods. The results indicate that the PCA based method was able to automatically detect muscle IZs from monopolar M waves. Thus, PCA provides an alternative approach to estimate IZ location of voluntary or electrically-evoked muscle contractions, and may have particular value for IZ detection in patients with impaired voluntary muscle activation. |
format | Online Article Text |
id | pubmed-10050562 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100505622023-03-30 Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform Huang, Chengjun Lu, Zhiyuan Chen, Maoqi Klein, Cliff S. Zhang, Yingchun Li, Sheng Zhou, Ping Front Physiol Physiology This study examined methods for estimating the innervation zone (IZ) of a muscle using recorded monopolar high density M waves. Two IZ estimation methods based on principal component analysis (PCA) and Radon transform (RT) were examined. Experimental M waves, acquired from the biceps brachii muscles of nine healthy subjects were used as testing data sets. The performance of the two methods was evaluated by comparing their IZ estimations with manual IZ detection by experienced human operators. Compared with manual detection, the agreement rate of the estimated IZs was 83% and 63% for PCA and RT based methods, respectively, both using monopolar high density M waves. In contrast, the agreement rate was 56% for cross correlation analysis using bipolar high density M waves. The mean difference in estimated IZ location between manual detection and the tested method was 0.12 ± 0.28 inter-electrode-distance (IED) for PCA, 0.33 ± 0.41 IED for RT and 0.39 ± 0.74 IED for cross correlation-based methods. The results indicate that the PCA based method was able to automatically detect muscle IZs from monopolar M waves. Thus, PCA provides an alternative approach to estimate IZ location of voluntary or electrically-evoked muscle contractions, and may have particular value for IZ detection in patients with impaired voluntary muscle activation. Frontiers Media S.A. 2023-03-15 /pmc/articles/PMC10050562/ /pubmed/37008017 http://dx.doi.org/10.3389/fphys.2023.1137146 Text en Copyright © 2023 Huang, Lu, Chen, Klein, Zhang, Li and Zhou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Huang, Chengjun Lu, Zhiyuan Chen, Maoqi Klein, Cliff S. Zhang, Yingchun Li, Sheng Zhou, Ping Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform |
title | Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform |
title_full | Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform |
title_fullStr | Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform |
title_full_unstemmed | Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform |
title_short | Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform |
title_sort | muscle innervation zone estimation from monopolar high-density m-waves using principal component analysis and radon transform |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050562/ https://www.ncbi.nlm.nih.gov/pubmed/37008017 http://dx.doi.org/10.3389/fphys.2023.1137146 |
work_keys_str_mv | AT huangchengjun muscleinnervationzoneestimationfrommonopolarhighdensitymwavesusingprincipalcomponentanalysisandradontransform AT luzhiyuan muscleinnervationzoneestimationfrommonopolarhighdensitymwavesusingprincipalcomponentanalysisandradontransform AT chenmaoqi muscleinnervationzoneestimationfrommonopolarhighdensitymwavesusingprincipalcomponentanalysisandradontransform AT kleincliffs muscleinnervationzoneestimationfrommonopolarhighdensitymwavesusingprincipalcomponentanalysisandradontransform AT zhangyingchun muscleinnervationzoneestimationfrommonopolarhighdensitymwavesusingprincipalcomponentanalysisandradontransform AT lisheng muscleinnervationzoneestimationfrommonopolarhighdensitymwavesusingprincipalcomponentanalysisandradontransform AT zhouping muscleinnervationzoneestimationfrommonopolarhighdensitymwavesusingprincipalcomponentanalysisandradontransform |