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dc.contributor.authorBecerra, Roberto
dc.contributor.authorJoya-Caparrós, Gonzalo 
dc.contributor.authorGarcía, Rodolfo
dc.contributor.authorVelázque, Luis
dc.contributor.authorRodríguez, Roberto
dc.contributor.authorPino, Carmen
dc.date.accessioned2013-10-23T12:47:17Z
dc.date.available2013-10-23T12:47:17Z
dc.date.issued2013-10-23
dc.identifier.urihttp://hdl.handle.net/10630/6153
dc.description.abstractIn this paper, we compare the performance of two different methods for the task of electrooculogram saccadic points classification in Patients with Ataxia SCA2: Multilayer Perceptrons (MLP) and Random Forest. First we segment the recordings of 6 subjects into ranges of saccadic and non-saccadic points as the basis of supervised learning. Then, we randomly select a set of cases based on the velocity profile near each selected point for training and validation purposes using percent split scheme. Obtained results show that both methods have similar performance in classification matter, and seem to be suitable to solve theproblem of saccadic point classification in electrooculographic records from subjects with Ataxia SCA2.es_ES
dc.description.sponsorshipAECID: Agencia Española de Cooperación Internacional para el Desarrollo. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectOjos - Movimientoes_ES
dc.subjectTecnologíaes_ES
dc.subject.otherEOG signalses_ES
dc.subject.otherMultilayer perceptrones_ES
dc.subject.otherRandom forestes_ES
dc.subject.otherSaccadeses_ES
dc.subject.otherAtaxia sca2es_ES
dc.titleSaccadic points classification using Multilayer Perceptron and Randon Forest classifiers in EOG recording of patients with Ataxia SCA2es_ES
dc.typeinfo:eu-repo/semantics/preprintes_ES
dc.centroE.T.S.I. de Telecomunicaciónes_ES
dc.relation.eventtitleInternational Work-Conference on Artificial Neural Networks (IWAMM)es_ES
dc.relation.eventplacePuesto de la Cruz, Tenerife, Españaes_ES
dc.relation.eventdate12-14 de junio de 2013es_ES


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