Identification and monitoring of Parkinson’s disease dysgraphia based on fractional-order derivatives of online handwriting

Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized....

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Detalhes bibliográficos
Autores: Mucha, Ján, Mekyska, Jiri, Galaz, Zoltan, Faundez-Zanuy, Marcos, López-de-Ipiña, Karmele, Zvoncak, Vojtech, Kiska, Tomáš, Smekal, Zdenek, Brabenec, Lubos, Rektorova, Irena
Formato: artículo
Fecha de publicación:2018
País:España
Recursos:TecnoCampus
Repositorio:Repositori Digital del TecnoCampus
OAI Identifier:oai:repositori.tecnocampus.cat:20.500.12367/2522
Acesso em linha:http://hdl.handle.net/20.500.12367/2522
Access Level:acceso abierto
Palavra-chave:Parkinson’s disease dysgraphia
Micrographia
Online handwriting
Kinematic analysis
Fractional-order derivative
Fractional calculus
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spelling Identification and monitoring of Parkinson’s disease dysgraphia based on fractional-order derivatives of online handwritingMucha, JánMekyska, JiriGalaz, ZoltanFaundez-Zanuy, MarcosLópez-de-Ipiña, KarmeleZvoncak, VojtechKiska, TomášSmekal, ZdenekBrabenec, LubosRektorova, IrenaParkinson’s disease dysgraphiaMicrographiaOnline handwritingKinematic analysisFractional-order derivativeFractional calculusParkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. [...]info:eu-repo/semantics/publishedVersionMDPI202320232018info:eu-repo/semantics/article18 p.application/pdfhttp://hdl.handle.net/20.500.12367/2522reponame:Repositori Digital del TecnoCampusinstname:TecnoCampusInglésApplied Sciences. 2018;8(12):2566© 2018 by Mucha J, et al. Licensee MDPI, Basel, Switzerland.Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.tecnocampus.cat:20.500.12367/25222026-06-21T13:30:27Z
dc.title.none.fl_str_mv Identification and monitoring of Parkinson’s disease dysgraphia based on fractional-order derivatives of online handwriting
title Identification and monitoring of Parkinson’s disease dysgraphia based on fractional-order derivatives of online handwriting
spellingShingle Identification and monitoring of Parkinson’s disease dysgraphia based on fractional-order derivatives of online handwriting
Mucha, Ján
Parkinson’s disease dysgraphia
Micrographia
Online handwriting
Kinematic analysis
Fractional-order derivative
Fractional calculus
title_short Identification and monitoring of Parkinson’s disease dysgraphia based on fractional-order derivatives of online handwriting
title_full Identification and monitoring of Parkinson’s disease dysgraphia based on fractional-order derivatives of online handwriting
title_fullStr Identification and monitoring of Parkinson’s disease dysgraphia based on fractional-order derivatives of online handwriting
title_full_unstemmed Identification and monitoring of Parkinson’s disease dysgraphia based on fractional-order derivatives of online handwriting
title_sort Identification and monitoring of Parkinson’s disease dysgraphia based on fractional-order derivatives of online handwriting
dc.creator.none.fl_str_mv Mucha, Ján
Mekyska, Jiri
Galaz, Zoltan
Faundez-Zanuy, Marcos
López-de-Ipiña, Karmele
Zvoncak, Vojtech
Kiska, Tomáš
Smekal, Zdenek
Brabenec, Lubos
Rektorova, Irena
author Mucha, Ján
author_facet Mucha, Ján
Mekyska, Jiri
Galaz, Zoltan
Faundez-Zanuy, Marcos
López-de-Ipiña, Karmele
Zvoncak, Vojtech
Kiska, Tomáš
Smekal, Zdenek
Brabenec, Lubos
Rektorova, Irena
author_role author
author2 Mekyska, Jiri
Galaz, Zoltan
Faundez-Zanuy, Marcos
López-de-Ipiña, Karmele
Zvoncak, Vojtech
Kiska, Tomáš
Smekal, Zdenek
Brabenec, Lubos
Rektorova, Irena
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Parkinson’s disease dysgraphia
Micrographia
Online handwriting
Kinematic analysis
Fractional-order derivative
Fractional calculus
topic Parkinson’s disease dysgraphia
Micrographia
Online handwriting
Kinematic analysis
Fractional-order derivative
Fractional calculus
description Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. [...]
publishDate 2018
dc.date.none.fl_str_mv 2018
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/20.500.12367/2522
url http://hdl.handle.net/20.500.12367/2522
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Applied Sciences. 2018;8(12):2566
dc.rights.none.fl_str_mv © 2018 by Mucha J, et al. Licensee MDPI, Basel, Switzerland.
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2018 by Mucha J, et al. Licensee MDPI, Basel, Switzerland.
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 18 p.
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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
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score 15,301603

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