A survey on computer-assisted Parkinson's Disease diagnosis

dc.contributor.authorPereira, Clayton R.
dc.contributor.authorPereira, Danilo R.
dc.contributor.authorWeber, Silke A. T. [UNESP]
dc.contributor.authorHook, Christian
dc.contributor.authorAlbuquerque, Victor Hugo C. de
dc.contributor.authorPapa, Joao P. [UNESP]
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniv Western Sao Paulo
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionOstbayer Tech Hsch
dc.contributor.institutionUniv Fortaleza
dc.description.abstractBackground and objective: In this work, we present a systematic review concerning the recent enabling technologies as a tool to the diagnosis, treatment and better quality of life of patients diagnosed with Parkinson's Disease (PD), as well as an analysis of future trends on new approaches to this end. Methods: In this review, we compile a number of works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer and Hindawi Publishing Corporation. Each selected work has been carefully analyzed in order to identify its objective, methodology and results. Results: The review showed the majority of works make use of signal-based data, which are often acquired by means of sensors. Also, we have observed the increasing number of works that employ virtual reality and e-health monitoring systems to increase the life quality of PD patients. Despite the different approaches found in the literature, almost all of them make use of some sort of machine learning mechanism to aid the automatic PD diagnosis. Conclusions: The main focus of this survey is to consider computer-assisted diagnosis, and how effective they can be when handling the problem of PD identification. Also, the main contribution of this review is to consider very recent works only, mainly from 2015 and 2016.en
dc.description.affiliationUniv Fed Sao Carlos, Dept Comp, Sao Carlos, SP, Brazil
dc.description.affiliationUniv Western Sao Paulo, Sao Paulo, Brazil
dc.description.affiliationSao Paulo State Univ, Botucatu Med Sch, Botucatu, SP, Brazil
dc.description.affiliationOstbayer Tech Hsch, Regensburg, Germany
dc.description.affiliationUniv Fortaleza, Fortaleza, Ceara, Brazil
dc.description.affiliationSao Paulo State Univ, Sch Sci, Bauru, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Botucatu Med Sch, Botucatu, SP, Brazil
dc.description.affiliationUnespSao Paulo State Univ, Sch Sci, Bauru, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação para o Desenvolvimento da UNESP (FUNDUNESP)
dc.description.sponsorshipIdFAPESP: 2013/07375-0
dc.description.sponsorshipIdFAPESP: 2014/16250-9
dc.description.sponsorshipIdFAPESP: 2014/12236-1
dc.description.sponsorshipIdFAPESP: 2016/19403-6
dc.description.sponsorshipIdCNPq: 470571/2013-6
dc.description.sponsorshipIdCNPq: 306166/2014-3
dc.description.sponsorshipIdCNPq: 301928/2014-2
dc.description.sponsorshipIdCNPq: 304315/2017-6
dc.description.sponsorshipIdCNPq: 307066/2017-7
dc.description.sponsorshipIdFUNDUNESP: 2597.2017
dc.identifier.citationArtificial Intelligence In Medicine. Amsterdam: Elsevier, v. 95, p. 48-63, 2019.
dc.publisherElsevier B.V.
dc.relation.ispartofArtificial Intelligence In Medicine
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectParkinson's Disease
dc.subjectMachine Learning
dc.titleA survey on computer-assisted Parkinson's Disease diagnosisen
dcterms.rightsHolderElsevier B.V.