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

dc.contributor.authorTena, Alberto
dc.contributor.authorClaria, Francec
dc.contributor.authorSolsona, Francesc
dc.contributor.authorMeister, Einar
dc.contributor.authorPovedano, Mònica
dc.date.accessioned2021-05-13T12:42:53Z
dc.date.available2021-05-13T12:42:53Z
dc.date.issued2021-03-10
dc.date.updated2021-05-13T12:31:56Z
dc.description.abstractBackground: 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.
dc.format.extent18 p.
dc.format.mimetypeapplication/pdf
dc.identifier.pmid33688838
dc.identifier.urihttps://hdl.handle.net/2445/177239
dc.language.isoeng
dc.publisherJMIR Publications Inc.
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.2196/21331
dc.relation.ispartofJMIR Medical Informatics, 2021, vol. 9, num. 3, p. e21331
dc.relation.urihttps://doi.org/10.2196/21331
dc.rightscc by (c) Tena et al., 2021
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.sourceArticles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
dc.subject.classificationEsclerosi lateral amiotròfica
dc.subject.classificationAprenentatge automàtic
dc.subject.otherAmyotrophic lateral sclerosis
dc.subject.otherMachine learning
dc.titleDetection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

Fitxers

Paquet original

Mostrant 1 - 1 de 1
Carregant...
Miniatura
Nom:
Detection of Bulbar Involvement in Patients With Amyotrophic.pdf
Mida:
569.62 KB
Format:
Adobe Portable Document Format