Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study

Background: Bulbar involvement is a term used in amyotrophic lateral sclerosis (ALS) that refers to motor neuron impairment in the corticobulbar area of the brainstem, which produces a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar involvement is voice deterioration cha...

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Autores: Tena del Pozo, Alberto, Clarià Sancho, Francisco, Solsona Tehàs, Francesc, Meister, Einar, Povedano, Mònica
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/70887
Acceso en línea:https://doi.org/10.2196/21331
http://hdl.handle.net/10459.1/70887
Access Level:acceso abierto
Palabra clave:Amyotrophic lateral sclerosis
Bulbar involvement
Voice
Diagnosis
Machine learning
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spelling Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development StudyTena del Pozo, AlbertoClarià Sancho, FranciscoSolsona Tehàs, FrancescMeister, EinarPovedano, MònicaAmyotrophic lateral sclerosisBulbar involvementVoiceDiagnosisMachine learningBackground: Bulbar involvement is a term used in amyotrophic lateral sclerosis (ALS) that refers to motor neuron impairment in the corticobulbar area of the brainstem, which produces a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar involvement is voice deterioration characterized by grossly defective articulation; extremely slow, laborious speech; marked hypernasality; and severe harshness. Bulbar involvement requires well-timed and carefully coordinated interventions. Therefore, early detection is crucial to improving the quality of life and lengthening the life expectancy of patients with ALS who present with this dysfunction. Recent research efforts have focused on voice analysis to capture bulbar involvement. Objective: The main objective of this paper was (1) to design a methodology for diagnosing bulbar involvement efficiently through the acoustic parameters of uttered vowels in Spanish, and (2) to demonstrate that the performance of the automated diagnosis of bulbar involvement is superior to human diagnosis. Methods: The study focused on the extraction of features from the phonatory subsystem—jitter, shimmer, harmonics-to-noise ratio, and pitch—from the utterance of the five Spanish vowels. Then, we used various supervised classification algorithms, preceded by principal component analysis of the features obtained. Results: To date, support vector machines have performed better (accuracy 95.8%) than the models analyzed in the related work. We also show how the model can improve human diagnosis, which can often misdiagnose bulbar involvement. Conclusions: The results obtained are very encouraging and demonstrate the efficiency and applicability of the automated model presented in this paper. It may be an appropriate tool to help in the diagnosis of ALS by multidisciplinary clinical teams, in particular to improve the diagnosis of bulbar involvement.This work was supported by the Ministerio de Economía y Competitividad under contract TIN2017-84553-C2-2-R. Einar Meister’s research has been supported by the European Regional Development Fund through the Centre of Excellence in Estonian Studies. The Neurology Department of the Bellvitge University Hospital in Barcelona allowed the recording of the voices of the participants in its facilities. The clinical records were illustrated by Carlos Augusto Salazar Talavera. Dr Marta Fulla and Maria Carmen Majos Bellmunt advised about the process of eliciting the sounds.JMIR Publications2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.2196/21331http://hdl.handle.net/10459.1/70887reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84553-C2-2-RReproducció del document publicat a https://doi.org/10.2196/21331JMIR Medical Informatics, 2021, vol. 9, núm. 3, e21331cc-by (c) Tena, et al., 2021info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/708872026-06-24T12:42:17Z
dc.title.none.fl_str_mv Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study
title Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study
spellingShingle Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study
Tena del Pozo, Alberto
Amyotrophic lateral sclerosis
Bulbar involvement
Voice
Diagnosis
Machine learning
title_short Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study
title_full Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study
title_fullStr Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study
title_full_unstemmed Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study
title_sort Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study
dc.creator.none.fl_str_mv Tena del Pozo, Alberto
Clarià Sancho, Francisco
Solsona Tehàs, Francesc
Meister, Einar
Povedano, Mònica
author Tena del Pozo, Alberto
author_facet Tena del Pozo, Alberto
Clarià Sancho, Francisco
Solsona Tehàs, Francesc
Meister, Einar
Povedano, Mònica
author_role author
author2 Clarià Sancho, Francisco
Solsona Tehàs, Francesc
Meister, Einar
Povedano, Mònica
author2_role author
author
author
author
dc.subject.none.fl_str_mv Amyotrophic lateral sclerosis
Bulbar involvement
Voice
Diagnosis
Machine learning
topic Amyotrophic lateral sclerosis
Bulbar involvement
Voice
Diagnosis
Machine learning
description Background: Bulbar involvement is a term used in amyotrophic lateral sclerosis (ALS) that refers to motor neuron impairment in the corticobulbar area of the brainstem, which produces a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar involvement is voice deterioration characterized by grossly defective articulation; extremely slow, laborious speech; marked hypernasality; and severe harshness. Bulbar involvement requires well-timed and carefully coordinated interventions. Therefore, early detection is crucial to improving the quality of life and lengthening the life expectancy of patients with ALS who present with this dysfunction. Recent research efforts have focused on voice analysis to capture bulbar involvement. Objective: The main objective of this paper was (1) to design a methodology for diagnosing bulbar involvement efficiently through the acoustic parameters of uttered vowels in Spanish, and (2) to demonstrate that the performance of the automated diagnosis of bulbar involvement is superior to human diagnosis. Methods: The study focused on the extraction of features from the phonatory subsystem—jitter, shimmer, harmonics-to-noise ratio, and pitch—from the utterance of the five Spanish vowels. Then, we used various supervised classification algorithms, preceded by principal component analysis of the features obtained. Results: To date, support vector machines have performed better (accuracy 95.8%) than the models analyzed in the related work. We also show how the model can improve human diagnosis, which can often misdiagnose bulbar involvement. Conclusions: The results obtained are very encouraging and demonstrate the efficiency and applicability of the automated model presented in this paper. It may be an appropriate tool to help in the diagnosis of ALS by multidisciplinary clinical teams, in particular to improve the diagnosis of bulbar involvement.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.2196/21331
http://hdl.handle.net/10459.1/70887
url https://doi.org/10.2196/21331
http://hdl.handle.net/10459.1/70887
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84553-C2-2-R
Reproducció del document publicat a https://doi.org/10.2196/21331
JMIR Medical Informatics, 2021, vol. 9, núm. 3, e21331
dc.rights.none.fl_str_mv cc-by (c) Tena, et al., 2021
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Tena, et al., 2021
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv JMIR Publications
publisher.none.fl_str_mv JMIR Publications
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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