A morphology-based feature set for automated Amyotrophic Lateral Sclerosis diagnosis on surface electromyography
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , |
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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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 |
format |
article |
status_str |
publishedVersion |
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 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
10 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138118274646016 |