Mostra el registre d'ítem simple

dc.contributor.authorMartín Muñoz, Mario
dc.contributor.authorBéjar Alonso, Javier
dc.contributor.authorEspósito, Gennaro
dc.contributor.authorCatala Roig, Neus
dc.contributor.authorCortés García, Claudio Ulises
dc.contributor.authorViñas, Ferran
dc.contributor.authorTarragó Bofarull, Josep
dc.contributor.authorRojo, Emilio
dc.contributor.authorNowak, Rafal
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2017-03-01T08:01:37Z
dc.date.issued2016-08-02
dc.identifier.citationMartin, M., Bejar, J., Espósito, G., Català Roig, N., Cortes, C., Viñas, F., Tarragó, J., Rojo, E., Nowak, R. Kernel alignment for identifying objective criteria from brain MEG recordings in schizophrenia. "Pattern recognition letters", 2 Agost 2016.
dc.identifier.issn0167-8655
dc.identifier.urihttp://hdl.handle.net/2117/101758
dc.description.abstractThe current wide access to data from different neuroimaging techniques has permitted to obtain data to explore the possibility of finding objective criteria that can be used for diagnostic purposes. In order to decide which features of the data are relevant for the diagnostic task, we present in this paper a simple method for feature selection based on kernel alignment with the ideal kernel in support vector machines (SVM). The method presented shows state-of-the-art performance while being more efficient than other methods for feature selection in SVM. It is also less prone to overfitting due to the properties of the alignment measure. All these abilities are essential in neuroimaging study, where the number of features representing recordings is usually very large compared with the number of recordings. The method has been applied to a dataset in order to determine objective criteria for the diagnosis of schizophrenia. The dataset analyzed has been obtained from multichannel magnetoencephalogram (MEG) recordings, corresponding to the recordings during the performance of a mismatch negativity (MMN) auditory task by a set of schizophrenia patients and a control group. All signal frequency bands are analyzed (from d (1–4 Hz) to high frequency ¿ (60–200 Hz)) and the signal correlations among the different sensors for these frequencies are used as features.
dc.language.isoeng
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut::Salut mental
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshSchizophrenia
dc.subject.lcshMachine learning
dc.subject.lcshKernel functions
dc.subject.lcshSupport vector machines
dc.subject.lcshMagnetoencephalography
dc.subject.otherMachine learning
dc.subject.otherFeature selection
dc.subject.otherKernel methods
dc.subject.otherMEG
dc.subject.otherSchizophrenia
dc.titleKernel alignment for identifying objective criteria from brain MEG recordings in schizophrenia
dc.typeArticle
dc.subject.lemacCervell -- Processament de dades
dc.subject.lemacEsquizofrènia
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacKernel, Funcions de
dc.subject.lemacEcoencefalografia
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
dc.identifier.doi10.1016/j.patrec.2016.07.018
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0167865516301805
dc.rights.accessRestricted access - publisher's policy
drac.iddocument18815121
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/TEC2014-56256-C2-2-P
dc.date.lift2020-08
upcommons.citation.authorMartin, M., Bejar, J., Espósito, G., Català Roig, N., Cortes, C., Viñas, F., Tarragó, J., Rojo, E., Nowak, R.
upcommons.citation.publishedtrue
upcommons.citation.publicationNamePattern recognition letters


Fitxers d'aquest items

Imatge en miniatura
Thumbnail

Aquest ítem apareix a les col·leccions següents

Mostra el registre d'ítem simple

Llevat que s'hi indiqui el contrari, els continguts d'aquesta obra estan subjectes a la llicència de Creative Commons: Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya