Parkinson's disease identification through Optimum-Path Forest

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorSpadoto, André A.
dc.creatorGuido, Rodrigo C.
dc.creatorPapa, João Paulo
dc.creatorFalcão, Alexandre X.
dc.date2014-05-27T11:25:19Z
dc.date2016-10-25T18:32:53Z
dc.date2014-05-27T11:25:19Z
dc.date2016-10-25T18:32:53Z
dc.date2010-12-01
dc.date.accessioned2017-04-06T01:47:17Z
dc.date.available2017-04-06T01:47:17Z
dc.identifier2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, p. 6087-6090.
dc.identifierhttp://hdl.handle.net/11449/72041
dc.identifierhttp://acervodigital.unesp.br/handle/11449/72041
dc.identifier10.1109/IEMBS.2010.5627634
dc.identifier2-s2.0-78650818582
dc.identifierhttp://dx.doi.org/10.1109/IEMBS.2010.5627634
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/892949
dc.descriptionArtificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification. © 2010 IEEE.
dc.languageeng
dc.relation2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial intelligence techniques
dc.subjectArtificial Neural Network
dc.subjectAutomatic recognition
dc.subjectCommonly used
dc.subjectFeature space
dc.subjectKernel mapping
dc.subjectParkinson's disease
dc.subjectPattern recognition techniques
dc.subjectPD identification
dc.subjectSupervised classification
dc.subjectDiseases
dc.subjectPattern recognition
dc.subjectNeural networks
dc.titleParkinson's disease identification through Optimum-Path Forest
dc.typeOtro


Este ítem pertenece a la siguiente institución