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...

Descripción completa

Detalles Bibliográficos
Autores principales: Huang, Chengjun, Lu, Zhiyuan, Chen, Maoqi, Klein, Cliff S., Zhang, Yingchun, Li, Sheng, Zhou, Ping
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