On the Analysis of Speech and Disfluencies for Automatic Detection of Mild Cognitive Impairment

Alzheimer's disease is characterized by a progressive and irreversible cognitive deterioration. In a previous stage, the so-called Mild Cognitive Impairment or cognitive loss appears. Nevertheless, this previous stage does not seem sufficiently severe to interfere in independent abilities of da...

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Autores: López de Ipiña Peña, Miren Karmele, Martínez de Lizarduy Sturtze, Unai, Calvo Salomón, Pilar María, Beitia Bengoa, Blanca, García Melero, Joseba, Fernández Gómez de Segura, Elsa, Ecay Torres, Miriam, Faúndez Zanuy, Marcos, Sanz, P.
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/50209
Acceso en línea:http://hdl.handle.net/10810/50209
Access Level:acceso abierto
Palabra clave:mild cognitive impairment
automatic speech analysis
deep learning
convolutional neural networks
nonlinear features
disfluencies
alzheimers
disease
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spelling On the Analysis of Speech and Disfluencies for Automatic Detection of Mild Cognitive ImpairmentLópez de Ipiña Peña, Miren KarmeleMartínez de Lizarduy Sturtze, UnaiCalvo Salomón, Pilar MaríaBeitia Bengoa, BlancaGarcía Melero, JosebaFernández Gómez de Segura, ElsaEcay Torres, MiriamFaúndez Zanuy, MarcosSanz, P.mild cognitive impairmentautomatic speech analysisdeep learningconvolutional neural networksnonlinear featuresdisfluenciesalzheimersdiseaseAlzheimer's disease is characterized by a progressive and irreversible cognitive deterioration. In a previous stage, the so-called Mild Cognitive Impairment or cognitive loss appears. Nevertheless, this previous stage does not seem sufficiently severe to interfere in independent abilities of daily life, so it is usually diagnosed inappropriately. Thus, its detection is a crucial challenge to be addressed by medical specialists. This paper presents a novel proposal for such early diagnosis based on automatic analysis of speech and disfluencies, and Deep Learning methodologies. The proposed tools could be useful for supporting Mild Cognitive Impairment diagnosis. The Deep Learning approach includes Convolutional Neural Networks and nonlinear multifeature modeling. Additionally, an automatic hybrid methodology is used in order to select the most relevant features by means of nonparametric Mann-Whitney U test and Support Vector Machine Attribute evaluation.This work has been supported by FEDER and MICINN, TEC2016-77,791-C4-2-R, and UPV/EHU-Basque Research Groups IT11156 and Basque Country EleKin Research GroupSpringer202120212020info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/50209reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MICINN/TEC2016-77791-C4-2-R/https://link-springer-com.ehu.idm.oclc.org/article/10.1007/s00521-018-3494-1info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0)Atribución 3.0 Españaoai:addi.ehu.eus:10810/502092026-06-18T09:23:17Z
dc.title.none.fl_str_mv On the Analysis of Speech and Disfluencies for Automatic Detection of Mild Cognitive Impairment
title On the Analysis of Speech and Disfluencies for Automatic Detection of Mild Cognitive Impairment
spellingShingle On the Analysis of Speech and Disfluencies for Automatic Detection of Mild Cognitive Impairment
López de Ipiña Peña, Miren Karmele
mild cognitive impairment
automatic speech analysis
deep learning
convolutional neural networks
nonlinear features
disfluencies
alzheimers
disease
title_short On the Analysis of Speech and Disfluencies for Automatic Detection of Mild Cognitive Impairment
title_full On the Analysis of Speech and Disfluencies for Automatic Detection of Mild Cognitive Impairment
title_fullStr On the Analysis of Speech and Disfluencies for Automatic Detection of Mild Cognitive Impairment
title_full_unstemmed On the Analysis of Speech and Disfluencies for Automatic Detection of Mild Cognitive Impairment
title_sort On the Analysis of Speech and Disfluencies for Automatic Detection of Mild Cognitive Impairment
dc.creator.none.fl_str_mv López de Ipiña Peña, Miren Karmele
Martínez de Lizarduy Sturtze, Unai
Calvo Salomón, Pilar María
Beitia Bengoa, Blanca
García Melero, Joseba
Fernández Gómez de Segura, Elsa
Ecay Torres, Miriam
Faúndez Zanuy, Marcos
Sanz, P.
author López de Ipiña Peña, Miren Karmele
author_facet López de Ipiña Peña, Miren Karmele
Martínez de Lizarduy Sturtze, Unai
Calvo Salomón, Pilar María
Beitia Bengoa, Blanca
García Melero, Joseba
Fernández Gómez de Segura, Elsa
Ecay Torres, Miriam
Faúndez Zanuy, Marcos
Sanz, P.
author_role author
author2 Martínez de Lizarduy Sturtze, Unai
Calvo Salomón, Pilar María
Beitia Bengoa, Blanca
García Melero, Joseba
Fernández Gómez de Segura, Elsa
Ecay Torres, Miriam
Faúndez Zanuy, Marcos
Sanz, P.
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv mild cognitive impairment
automatic speech analysis
deep learning
convolutional neural networks
nonlinear features
disfluencies
alzheimers
disease
topic mild cognitive impairment
automatic speech analysis
deep learning
convolutional neural networks
nonlinear features
disfluencies
alzheimers
disease
description Alzheimer's disease is characterized by a progressive and irreversible cognitive deterioration. In a previous stage, the so-called Mild Cognitive Impairment or cognitive loss appears. Nevertheless, this previous stage does not seem sufficiently severe to interfere in independent abilities of daily life, so it is usually diagnosed inappropriately. Thus, its detection is a crucial challenge to be addressed by medical specialists. This paper presents a novel proposal for such early diagnosis based on automatic analysis of speech and disfluencies, and Deep Learning methodologies. The proposed tools could be useful for supporting Mild Cognitive Impairment diagnosis. The Deep Learning approach includes Convolutional Neural Networks and nonlinear multifeature modeling. Additionally, an automatic hybrid methodology is used in order to select the most relevant features by means of nonparametric Mann-Whitney U test and Support Vector Machine Attribute evaluation.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/50209
url http://hdl.handle.net/10810/50209
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MICINN/TEC2016-77791-C4-2-R/
https://link-springer-com.ehu.idm.oclc.org/article/10.1007/s00521-018-3494-1
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
Atribución 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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