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dc.contributor.authorRibeiro, Luiz C.F. [UNESP]
dc.contributor.authorAfonso, Luis C.S.
dc.contributor.authorPapa, João P. [UNESP]
dc.identifier.citationComputers in Biology and Medicine, v. 115.
dc.description.abstractParkinson's Disease (PD) is a clinical syndrome that affects millions of people worldwide. Although considered as a non-lethal disease, PD shortens the life expectancy of the patients. Many studies have been dedicated to evaluating methods for early-stage PD detection, which includes machine learning techniques that employ, in most cases, motor dysfunctions, such as tremor. This work explores the time dependency in tremor signals collected from handwriting exams. To learn such temporal information, we propose a model based on Bidirectional Gated Recurrent Units along with an attention mechanism. We also introduce the concept of “Bag of Samplings” that computes multiple compact representations of the signals. Experimental results have shown the proposed model is a promising technique with results comparable to some state-of-the-art approaches in the literature.en
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.relation.ispartofComputers in Biology and Medicine
dc.subjectBag of samplings
dc.subjectHandwritten dynamics
dc.subjectParkinson's disease
dc.subjectRecurrent Neural Networks
dc.titleBag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networksen
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.description.affiliationUNESP - São Paulo State University School of Sciences
dc.description.affiliationUFSCar - Federal University of São Carlos Department of Computing
dc.description.affiliationUnespUNESP - São Paulo State University School of Sciences
dc.description.sponsorshipIdFAPESP: 2013/07375-0
dc.description.sponsorshipIdFAPESP: 2014/12236-1
dc.description.sponsorshipIdCNPq: 307066/2017-7
dc.description.sponsorshipIdCNPq: 427968/2018-6
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