Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease associated with several motor symptoms, including alterations in handwriting, also known as PD dysgraphia. Several computerized decision support systems for PD dysgraphia have been proposed, however, the associated challe...

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Detalles Bibliográficos
Autores: Nolazco Flores, Juan Arturo, Faundez-Zanuy, Marcos, de la Cueva, Victor, Mekyska, Jiri
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:TecnoCampus
Repositorio:Repositori Digital del TecnoCampus
OAI Identifier:oai:repositori.tecnocampus.cat:20.500.12367/2196
Acceso en línea:http://hdl.handle.net/20.500.12367/2196
Access Level:acceso abierto
Palabra clave:Parkinson’s disease
Dysgraphia
Online handwriting
Feature extraction
Data augmentation
AutoML
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oai_identifier_str oai:repositori.tecnocampus.cat:20.500.12367/2196
network_acronym_str ES
network_name_str España
repository_id_str
spelling Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s diseaseNolazco Flores, Juan ArturoFaundez-Zanuy, Marcosde la Cueva, VictorMekyska, JiriParkinson’s diseaseDysgraphiaOnline handwritingFeature extractionData augmentationAutoMLParkinson’s disease (PD) is the second most frequent neurodegenerative disease associated with several motor symptoms, including alterations in handwriting, also known as PD dysgraphia. Several computerized decision support systems for PD dysgraphia have been proposed, however, the associated challenges require new approaches for more accurate diagnosis. Therefore, this work adds spectral and cepstral handwriting features to the already-used temporal, kinematic and statistics handwriting features. First, we calculate temporal and kinematic features using displacement; statistic features (SF) using displacement, and horizontal and vertical displacement; spectral(SDF) and cepstral(CDF) using displacement, horizontal and vertical displacement and pressure. Since the employed dataset (PaHaW) contains only 37 PD patients and 38 healthy control subjects (HC), then as the second step, we augment the percentage of the smaller training set to equal the larger [...].info:eu-repo/semantics/publishedVersionIEEETecnoCampus. Escola Superior Politècnica (ESUPT)202320232021info:eu-repo/semantics/article12 p.application/pdfhttp://hdl.handle.net/20.500.12367/2196reponame:Repositori Digital del TecnoCampusinstname:TecnoCampusInglésIEEE Access. 2021 Oct 22;(9):141599-141610This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositori.tecnocampus.cat:20.500.12367/21962026-06-21T13:30:27Z
dc.title.none.fl_str_mv Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease
title Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease
spellingShingle Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease
Nolazco Flores, Juan Arturo
Parkinson’s disease
Dysgraphia
Online handwriting
Feature extraction
Data augmentation
AutoML
title_short Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease
title_full Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease
title_fullStr Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease
title_full_unstemmed Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease
title_sort Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease
dc.creator.none.fl_str_mv Nolazco Flores, Juan Arturo
Faundez-Zanuy, Marcos
de la Cueva, Victor
Mekyska, Jiri
author Nolazco Flores, Juan Arturo
author_facet Nolazco Flores, Juan Arturo
Faundez-Zanuy, Marcos
de la Cueva, Victor
Mekyska, Jiri
author_role author
author2 Faundez-Zanuy, Marcos
de la Cueva, Victor
Mekyska, Jiri
author2_role author
author
author
dc.contributor.none.fl_str_mv TecnoCampus. Escola Superior Politècnica (ESUPT)
dc.subject.none.fl_str_mv Parkinson’s disease
Dysgraphia
Online handwriting
Feature extraction
Data augmentation
AutoML
topic Parkinson’s disease
Dysgraphia
Online handwriting
Feature extraction
Data augmentation
AutoML
description Parkinson’s disease (PD) is the second most frequent neurodegenerative disease associated with several motor symptoms, including alterations in handwriting, also known as PD dysgraphia. Several computerized decision support systems for PD dysgraphia have been proposed, however, the associated challenges require new approaches for more accurate diagnosis. Therefore, this work adds spectral and cepstral handwriting features to the already-used temporal, kinematic and statistics handwriting features. First, we calculate temporal and kinematic features using displacement; statistic features (SF) using displacement, and horizontal and vertical displacement; spectral(SDF) and cepstral(CDF) using displacement, horizontal and vertical displacement and pressure. Since the employed dataset (PaHaW) contains only 37 PD patients and 38 healthy control subjects (HC), then as the second step, we augment the percentage of the smaller training set to equal the larger [...].
publishDate 2021
dc.date.none.fl_str_mv 2021
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12367/2196
url http://hdl.handle.net/20.500.12367/2196
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Access. 2021 Oct 22;(9):141599-141610
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 12 p.
application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositori Digital del TecnoCampus
instname:TecnoCampus
instname_str TecnoCampus
reponame_str Repositori Digital del TecnoCampus
collection Repositori Digital del TecnoCampus
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869402758302400512
score 15.300719

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