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dc.contributor.authorNolazco Flores, Juan Arturo
dc.contributor.authorFaundez-Zanuy, Marcos
dc.contributor.authorde la Cueva, Victor
dc.contributor.authorMekyska, Jiri
dc.contributor.otherTecnoCampus. Escola Superior Politècnica (ESUPT)ca
dc.date.accessioned2023-02-23T13:33:29Z
dc.date.available2023-02-23T13:33:29Z
dc.date.issued2021-10-22
dc.identifier.citationNolazco Flores JA, Faundez-Zanuy M, de la Cueva V, Mekyska J. Exploiting spectral and cepstral handwriting features on diagnosing Parkinson’s disease. IEEE Access. 2021 Oct 22;(9):141599-141610. DOI: 10.1109/ACCESS.2021.3119035
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/20.500.12367/2196
dc.description.abstractParkinson’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 [...].ca
dc.format.extent12 p.ca
dc.language.isoengca
dc.publisherIEEEca
dc.relation.ispartofIEEE Access. 2021 Oct 22;(9):141599-141610
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.otherParkinson’s diseaseca
dc.subject.otherDysgraphia
dc.subject.otherOnline handwriting
dc.subject.otherFeature extraction
dc.subject.otherData augmentation
dc.subject.otherAutoML
dc.titleExploiting spectral and cepstral handwriting features on diagnosing Parkinson’s diseaseca
dc.typeinfo:eu-repo/semantics/articleca
dc.description.versioninfo:eu-repo/semantics/publishedVersionca
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapca
dc.identifier.doi10.1109/ACCESS.2021.3119035ca


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This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Excepto si se señala otra cosa, la licencia del ítem se describe como http://creativecommons.org/licenses/by-nc-nd/4.0/
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