A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography

Detalhes bibliográficos
Autor(a) principal: Antunes, Margarida
Data de Publicação: 2023
Outros Autores: Folgado, Duarte, Barandas, Marília, Carreiro, André, Quintão, Carla, de Carvalho, Mamede, Gamboa, Hugo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/146909
Resumo: This work was supported by national funds from FCT Foundation for Science and Technology, Portugal , I.P. through the protect HomeSenseALS: Home-based monitoring of functional disability in amyotrophic lateral sclerosis with mobile sensing with reference and research unit UIDB/FIS/04559/2020 (LIBPhys-UNL). Publisher Copyright: © 2022
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spelling A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyographyAmyotrophic Lateral SclerosisFeature selectionMachine learningSignal processingSurface electromyographyTime seriesSignal ProcessingBiomedical EngineeringHealth InformaticsThis work was supported by national funds from FCT Foundation for Science and Technology, Portugal , I.P. through the protect HomeSenseALS: Home-based monitoring of functional disability in amyotrophic lateral sclerosis with mobile sensing with reference and research unit UIDB/FIS/04559/2020 (LIBPhys-UNL). Publisher Copyright: © 2022Amyotrophic Lateral Sclerosis (ALS) is a fast-progressing disease with no cure. Nowadays, needle electromyography (nEMG) is the standard practice for electrodiagnosis of ALS. Surface electromyography (sEMG) is emerging as a more practical and less painful alternative to nEMG but still has analytical and technical challenges. The objective of this work was to study the feasibility of using a set of morphological features extracted from sEMG to support a machine learning pipeline for ALS diagnosis. We developed a novel feature set to characterize sEMG based on quantitative measurements to surface representation of Motor Unit Action Potentials. We conducted several experiments to study the relevance of the proposed feature set either individually or combined with conventional feature sets from temporal, statistical, spectral, and fractal domains. We validated the proposed machine learning pipeline on a dataset with sEMG upper limb muscle data from 17 ALS patients and 24 control subjects. The results support the utility of the proposed feature set, achieving an F1 score of (81.9 ± 5.7) for the onset classification approach and (83.6 ± 6.9) for the subject classification approach, solely relying on features extracted from the proposed feature set in the right first dorsal interosseous muscle. We concluded that introducing the proposed feature set is relevant for automated ALS diagnosis since it increased the classifier performance during our experiments. The proposed feature set might also help design more interpretable classifiers as the features give additional information related to the nature of the disease, being inspired by the clinical interpretation of sEMG.DF – Departamento de FísicaLIBPhys-UNLRUNAntunes, MargaridaFolgado, DuarteBarandas, MaríliaCarreiro, AndréQuintão, Carlade Carvalho, MamedeGamboa, Hugo2023-01-03T22:18:03Z2023-012023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10application/pdfhttp://hdl.handle.net/10362/146909eng1746-8094PURE: 46308147https://doi.org/10.1016/j.bspc.2022.104011info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:27:47Zoai:run.unl.pt:10362/146909Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:42.003761Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography
title A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography
spellingShingle A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography
Antunes, Margarida
Amyotrophic Lateral Sclerosis
Feature selection
Machine learning
Signal processing
Surface electromyography
Time series
Signal Processing
Biomedical Engineering
Health Informatics
title_short A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography
title_full A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography
title_fullStr A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography
title_full_unstemmed A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography
title_sort A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography
author Antunes, Margarida
author_facet Antunes, Margarida
Folgado, Duarte
Barandas, Marília
Carreiro, André
Quintão, Carla
de Carvalho, Mamede
Gamboa, Hugo
author_role author
author2 Folgado, Duarte
Barandas, Marília
Carreiro, André
Quintão, Carla
de Carvalho, Mamede
Gamboa, Hugo
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv DF – Departamento de Física
LIBPhys-UNL
RUN
dc.contributor.author.fl_str_mv Antunes, Margarida
Folgado, Duarte
Barandas, Marília
Carreiro, André
Quintão, Carla
de Carvalho, Mamede
Gamboa, Hugo
dc.subject.por.fl_str_mv Amyotrophic Lateral Sclerosis
Feature selection
Machine learning
Signal processing
Surface electromyography
Time series
Signal Processing
Biomedical Engineering
Health Informatics
topic Amyotrophic Lateral Sclerosis
Feature selection
Machine learning
Signal processing
Surface electromyography
Time series
Signal Processing
Biomedical Engineering
Health Informatics
description This work was supported by national funds from FCT Foundation for Science and Technology, Portugal , I.P. through the protect HomeSenseALS: Home-based monitoring of functional disability in amyotrophic lateral sclerosis with mobile sensing with reference and research unit UIDB/FIS/04559/2020 (LIBPhys-UNL). Publisher Copyright: © 2022
publishDate 2023
dc.date.none.fl_str_mv 2023-01-03T22:18:03Z
2023-01
2023-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/146909
url http://hdl.handle.net/10362/146909
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1746-8094
PURE: 46308147
https://doi.org/10.1016/j.bspc.2022.104011
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eu_rights_str_mv openAccess
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instacron:RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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