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dc.contributor.authorBecerra-García, Roberto Antonio
dc.contributor.authorGarcía-Bermúdez, Rodolfo
dc.contributor.authorJoya-Caparrós, Gonzalo 
dc.contributor.authorFernández-Higuera, Abel
dc.contributor.authorVelázquez-Rodríguez, Camilo
dc.contributor.authorVelazquez-Mariño, Michel
dc.contributor.authorCuevas-Beltrán, Franger
dc.contributor.authorGarcía-Lagos, Francisco 
dc.contributor.authorRodríguez-Labrada, Roberto
dc.date.accessioned2015-11-20T14:07:15Z
dc.date.available2015-11-20T14:07:15Z
dc.date.created2015
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10630/10710
dc.description.abstractIn this paper we evaluate the use of the machine learning algorithms Support Vector Machines, K-Nearest Neighbors, CART decision trees and Naive Bayes to identify non spontaneous saccades in clinical electrooculography tests. Our approach tries to solve problems like the use of manually established thresholds present in classical methods like identification by velocity threshold (I-VT) or identification by dispersion threshold (I-DT). We propose a modification to an adaptive threshold estimation algorithm for detecting signal impulses without the need of any user input. Also, a set of features were selected to take advantage of intrinsic characteristics of clinical electrooculography tests. The models were evaluated with signals recorded to subjects affected by Spinocerebellar Ataxia type 2 (SCA2). Results obtained by the algorithm shows accuracies over 97%, recalls over 97% and precisions over 91% for the four models evaluated.es_ES
dc.description.sponsorshipUniversidad de Málaga, Campus de excelencia de Andalucía Teches_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectAprendizaje automático (Inteligencia artificial)es_ES
dc.subject.otherMachine Learninges_ES
dc.subject.otherSaccadic movements identificationes_ES
dc.titleNon spontaneous saccadic movements identification in clinical electrooculography using machine learninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. de Telecomunicaciónes_ES
dc.relation.eventtitleInternational Work-Conference on Artificial Neural Networks IWANN2015es_ES
dc.relation.eventplacePalma de Mallorca (España)es_ES
dc.relation.eventdate10/06/2015es_ES
dc.identifier.orcidhttp://orcid.org/0000-0001-9256-0870es_ES
dc.cclicenseby-nc-ndes_ES
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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