Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features

The term bulbar involvement is employed in ALS to refer to deterioration of motor neurons within the corticobulbar area of the brainstem, which results in speech and swallowing dysfunctions. One of the primary symptoms is a deterioration of the voice. Early detection is crucial for improving the qua...

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Autores: Tena, Alberto, Clarià, Francesc, Solsona, Francesc, Povedano, Mònica
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/184000
Acceso en línea:https://hdl.handle.net/2445/184000
Access Level:acceso abierto
Palabra clave:Esclerosi lateral amiotròfica
Deglutició
Amyotrophic lateral sclerosis
Deglutition
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oai_identifier_str oai:diposit.ub.edu:2445/184000
network_acronym_str ES
network_name_str España
repository_id_str
spelling Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency FeaturesTena, AlbertoClarià, FrancescSolsona, FrancescPovedano, MònicaEsclerosi lateral amiotròficaDegluticióAmyotrophic lateral sclerosisDeglutitionThe term bulbar involvement is employed in ALS to refer to deterioration of motor neurons within the corticobulbar area of the brainstem, which results in speech and swallowing dysfunctions. One of the primary symptoms is a deterioration of the voice. Early detection is crucial for improving the quality of life and lifespan of ALS patients suffering from bulbar involvement. The main objective, and the principal contribution, of this research, was to design a new methodology, based on the phonatory-subsystem and time-frequency characteristics for detecting bulbar involvement automatically. This study focused on providing a set of 50 phonatory-subsystem and time-frequency features to detect this deficiency in males and females through the utterance of the five Spanish vowels. Multivariant Analysis of Variance was then used to select the statistically significant features, and the most common supervised classifications models were analyzed. A set of statistically significant features was obtained for males and females to capture this dysfunction. To date, the accuracy obtained (98.01% for females and 96.10% for males employing a random forest) outperformed the models in the literature. Adding time-frequency features to more classical phonatory-subsystem features increases the prediction capabilities of the machine-learning models for detecting bulbar involvement. Studying men and women separately gives greater success. The proposed method can be deployed in any kind of recording device (i.e., smartphone).MDPI AG2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/184000Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.3390/s22031137Sensors, 2022, vol 22, num 3https://doi.org/10.3390/s22031137cc by (c) Tena, Alberto et al, 2022http://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1840002026-05-27T06:46:51Z
dc.title.none.fl_str_mv Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features
title Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features
spellingShingle Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features
Tena, Alberto
Esclerosi lateral amiotròfica
Deglutició
Amyotrophic lateral sclerosis
Deglutition
title_short Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features
title_full Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features
title_fullStr Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features
title_full_unstemmed Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features
title_sort Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features
dc.creator.none.fl_str_mv Tena, Alberto
Clarià, Francesc
Solsona, Francesc
Povedano, Mònica
author Tena, Alberto
author_facet Tena, Alberto
Clarià, Francesc
Solsona, Francesc
Povedano, Mònica
author_role author
author2 Clarià, Francesc
Solsona, Francesc
Povedano, Mònica
author2_role author
author
author
dc.subject.none.fl_str_mv Esclerosi lateral amiotròfica
Deglutició
Amyotrophic lateral sclerosis
Deglutition
topic Esclerosi lateral amiotròfica
Deglutició
Amyotrophic lateral sclerosis
Deglutition
description The term bulbar involvement is employed in ALS to refer to deterioration of motor neurons within the corticobulbar area of the brainstem, which results in speech and swallowing dysfunctions. One of the primary symptoms is a deterioration of the voice. Early detection is crucial for improving the quality of life and lifespan of ALS patients suffering from bulbar involvement. The main objective, and the principal contribution, of this research, was to design a new methodology, based on the phonatory-subsystem and time-frequency characteristics for detecting bulbar involvement automatically. This study focused on providing a set of 50 phonatory-subsystem and time-frequency features to detect this deficiency in males and females through the utterance of the five Spanish vowels. Multivariant Analysis of Variance was then used to select the statistically significant features, and the most common supervised classifications models were analyzed. A set of statistically significant features was obtained for males and females to capture this dysfunction. To date, the accuracy obtained (98.01% for females and 96.10% for males employing a random forest) outperformed the models in the literature. Adding time-frequency features to more classical phonatory-subsystem features increases the prediction capabilities of the machine-learning models for detecting bulbar involvement. Studying men and women separately gives greater success. The proposed method can be deployed in any kind of recording device (i.e., smartphone).
publishDate 2022
dc.date.none.fl_str_mv 2022
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://hdl.handle.net/2445/184000
url https://hdl.handle.net/2445/184000
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/s22031137
Sensors, 2022, vol 22, num 3
https://doi.org/10.3390/s22031137
dc.rights.none.fl_str_mv cc by (c) Tena, Alberto et al, 2022
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc by (c) Tena, Alberto et al, 2022
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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