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dc.contributor.authorSantiago Rodrigo, Luis de 
dc.contributor.authorSánchez Morla, Eva María 
dc.contributor.authorOrtiz del Castillo, Miguel 
dc.contributor.authorLópez Guillén, María Elena 
dc.contributor.authorAmo Usanos, Carlos 
dc.contributor.authorAlonso Rodríguez, María Concepción 
dc.contributor.authorBarea Navarro, Rafael 
dc.contributor.authorCavaliere Ballesta, Carlo 
dc.contributor.authorFernández Rodríguez, Alfredo José 
dc.contributor.authorBoquete Vázquez, Luciano 
dc.date.accessioned2019-05-09T13:50:40Z
dc.date.available2019-05-09T13:50:40Z
dc.date.issued2019-04-04
dc.identifier.bibliographicCitationSantiago L., Sánchez Morla E.M., Ortiz M., López E., Amo Usanos C., Alonso-Rodríguez M.C., et al. 2019, "A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings", PLoS ONE vol. 14, no. 4, e0214662.
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/10017/37429
dc.description.abstractIntroduction: The aim of this study is to develop a computer-aided diagnosis system to identify subjects at differing stages of development of multiple sclerosis (MS) using multifocal visual-evoked potentials (mfVEPs). Using an automatic classifier, diagnosis is performed first on the eyes and then on the subjects. Patients: MfVEP signals were obtained from patients with Radiologically Isolated Syndrome (RIS) (n = 30 eyes), patients with Clinically Isolated Syndrome (CIS) (n = 62 eyes), patients with definite MS (n = 56 eyes) and 22 control subjects (n = 44 eyes). The CIS and MS groups were divided into two subgroups: those with eyes affected by optic neuritis (ON) and those without (non-ON). Methods: For individual eye diagnosis, a feature vector was formed with information about the intensity, latency and singular values of the mfVEP signals. A flat multiclass classifier (FMC) and a hierarchical classifier (HC) were tested and both were implemented using the k-Nearest Neighbour (k-NN) algorithm. The output of the best eye classifier was used to classify the subjects. In the event of divergence, the eye with the best mfVEP recording was selected. Results: In the eye classifier, the HC performed better than the FMC (accuracy = 0.74 and extended Matthew Correlation Coefficient (MCC) = 0.68). In the subject classification, accuracy = 0.95 and MCC = 0.93, confirming that it may be a promising tool for MS diagnosis. Chirped-pulse φOTDR provides distributed strain measurement via a time-delay estimation process. We propose a lower bound for performance, after reducing sampling error and compensating phase-noise. We attempt to reach the limit, attaining unprecedented pε/√Hz sensitivities. Conclusion: In addition to amplitude (axonal loss) and latency (demyelination), it has shown that the singular values of the mfVEP signals provide discriminatory information that may be used to identify subjects with differing degrees of the disease.en
dc.description.sponsorshipAgencia Estatal de Investigaciónes_ES
dc.description.sponsorshipMinisterio de Economía y Competitividades_ES
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherPublic Library of Science
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleA computer-aided diagnosis of multiple sclerosis based on mfVEP recordingsen
dc.typeinfo:eu-repo/semantics/articleen
dc.subject.ecienciaElectrónicaes_ES
dc.subject.ecienciaElectronicsen
dc.subject.ecienciaMedicinaes_ES
dc.subject.ecienciaMedicineen
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Electrónicaes_ES
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Cirugía, Ciencias Médicas y Socialeses_ES
dc.date.updated2019-05-09T13:46:47Z
dc.relation.publisherversionhttps://doi.org/10.1371/journal.pone.0214662
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.1371/journal.pone.0214662
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-88438-R/ES/INVESTIGACION DE LA TECNICA DE POTENCIALES EVOCADOS VISUALES MULTIFOCALES. APLICACION EN ESTUDIOS DE EVOLUCION DE ESCLEROSIS MULTIPLE Y EVALUACION DE MEDICAMENTOS/
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//RD16%2F0008%2F0020
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.uxxiAR/0000030608
dc.identifier.publicationtitlePLoS ONE
dc.identifier.publicationvolume14
dc.identifier.publicationissue4


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