Muscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking.

Neuromuscular impairments are frequently observed in patients with chronic neck pain (CNP). This study uniquely investigates whether changes in neck muscle synergies detected during gait are sensitive enough to differentiate between people with and without CNP. Surface electromyography (EMG) was rec...

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Autores principales:	David Jiménez-Grande, S Farokh Atashzar, Eduardo Martinez-Valdes, Deborah Falla
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/384306cb0f0e4482a4ff0cf6871e6910
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spelling	oai:doaj.org-article:384306cb0f0e4482a4ff0cf6871e69102021-12-02T20:10:19ZMuscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking.1932-620310.1371/journal.pone.0252657https://doaj.org/article/384306cb0f0e4482a4ff0cf6871e69102021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252657https://doaj.org/toc/1932-6203Neuromuscular impairments are frequently observed in patients with chronic neck pain (CNP). This study uniquely investigates whether changes in neck muscle synergies detected during gait are sensitive enough to differentiate between people with and without CNP. Surface electromyography (EMG) was recorded from the sternocleidomastoid, splenius capitis, and upper trapezius muscles bilaterally from 20 asymptomatic individuals and 20 people with CNP as they performed rectilinear and curvilinear gait. Intermuscular coherence was computed to generate the functional inter-muscle connectivity network, the topology of which is quantified based on a set of graph measures. Besides the functional network, spectrotemporal analysis of each EMG was used to form the feature set. With the use of Neighbourhood Component Analysis (NCA), we identified the most significant features and muscles for the classification/differentiation task conducted using K-Nearest Neighbourhood (K-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms. The NCA algorithm selected features from muscle network topology as one of the most relevant feature sets, which further emphasize the presence of major differences in muscle network topology between people with and without CNP. Curvilinear gait achieved the best classification performance through NCA-SVM based on only 16 features (accuracy: 85.00%, specificity: 81.81%, and sensitivity: 88.88%). Intermuscular muscle networks can be considered as a new sensitive tool for the classification of people with CNP. These findings further our understanding of how fundamental muscle networks are altered in people with CNP.David Jiménez-GrandeS Farokh AtashzarEduardo Martinez-ValdesDeborah FallaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252657 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David Jiménez-Grande S Farokh Atashzar Eduardo Martinez-Valdes Deborah Falla Muscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking.
description	Neuromuscular impairments are frequently observed in patients with chronic neck pain (CNP). This study uniquely investigates whether changes in neck muscle synergies detected during gait are sensitive enough to differentiate between people with and without CNP. Surface electromyography (EMG) was recorded from the sternocleidomastoid, splenius capitis, and upper trapezius muscles bilaterally from 20 asymptomatic individuals and 20 people with CNP as they performed rectilinear and curvilinear gait. Intermuscular coherence was computed to generate the functional inter-muscle connectivity network, the topology of which is quantified based on a set of graph measures. Besides the functional network, spectrotemporal analysis of each EMG was used to form the feature set. With the use of Neighbourhood Component Analysis (NCA), we identified the most significant features and muscles for the classification/differentiation task conducted using K-Nearest Neighbourhood (K-NN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms. The NCA algorithm selected features from muscle network topology as one of the most relevant feature sets, which further emphasize the presence of major differences in muscle network topology between people with and without CNP. Curvilinear gait achieved the best classification performance through NCA-SVM based on only 16 features (accuracy: 85.00%, specificity: 81.81%, and sensitivity: 88.88%). Intermuscular muscle networks can be considered as a new sensitive tool for the classification of people with CNP. These findings further our understanding of how fundamental muscle networks are altered in people with CNP.
format	article
author	David Jiménez-Grande S Farokh Atashzar Eduardo Martinez-Valdes Deborah Falla
author_facet	David Jiménez-Grande S Farokh Atashzar Eduardo Martinez-Valdes Deborah Falla
author_sort	David Jiménez-Grande
title	Muscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking.
title_short	Muscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking.
title_full	Muscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking.
title_fullStr	Muscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking.
title_full_unstemmed	Muscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking.
title_sort	muscle network topology analysis for the classification of chronic neck pain based on emg biomarkers extracted during walking.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/384306cb0f0e4482a4ff0cf6871e6910
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Muscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking.

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