Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/111876
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dc.contributor.authorDíaz Cabrera, Moisésen_US
dc.contributor.authorMomina Moetesumen_US
dc.contributor.authorImran Siddiqien_US
dc.contributor.authorGennaro Vessioen_US
dc.date.accessioned2021-09-22T11:02:56Z-
dc.date.available2021-09-22T11:02:56Z-
dc.date.issued2021en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttp://hdl.handle.net/10553/111876-
dc.description.abstractParkinson's disease (PD) is commonly characterized by several motor symptoms, such as bradykinesia, akinesia, rigidity, and tremor. The analysis of patients' fine motor control, particularly handwriting, is a powerful tool to support PD assessment. Over the years, various dynamic attributes of handwriting, such as pen pressure, stroke speed, in-air time, etc., which can be captured with the help of online handwriting acquisition tools, have been evaluated for the identification of PD. Motion events, and their associated spatio-temporal properties captured in online handwriting, enable effective classification of PD patients through the identification of unique sequential patterns. This paper proposes a novel classification model based on one-dimensional convolutions and Bidirectional Gated Recurrent Units (BiGRUs) to assess the potential of sequential information of handwriting in identifying Parkinsonian symptoms. One-dimensional convolutions are applied to raw sequences as well as derived features; the resulting sequences are then fed to BiGRU layers to achieve the final classification. The proposed method outperformed state-of-the-art approaches on the PaHaW dataset and achieved competitive results on the NewHandPD dataset.en_US
dc.languageengen_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.sourceExpert Systems with Applications [ISSN 0957-4174], v. 168, 114405, (Abril 2021)en_US
dc.subject3304 Tecnología de los ordenadoresen_US
dc.subject320507 Neurologíaen_US
dc.subject.otherComputer-aided diagnosisen_US
dc.subject.otherDynamic handwriting analysisen_US
dc.subject.otherParkinson's diseaseen_US
dc.subject.otherRecurrent neural networksen_US
dc.titleSequence-based Dynamic Handwriting Analysis for Parkinson's Disease Detection with One-dimensional Convolutions and BiGRUsen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typearticleen_US
dc.identifier.doi10.1016/j.eswa.2020.114405en_US
dc.identifier.scopus2-s2.0-85097459861-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.investigacionCiencias de la Saluden_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.identifier.ulpgcNoen_US
dc.contributor.buulpgcBU-BASen_US
dc.description.sjr2,07
dc.description.jcr8,665
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
dc.description.miaricds11,0
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Física-
crisitem.author.orcid0000-0003-3878-3867-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameDíaz Cabrera, Moisés-
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