Patient prognosis based on feature extraction, selection and classification of EEG periodic activity

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dc.contributor.authorSánchez González, Alain
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorMaestro Saiz, Iratxe
dc.contributor.authorYurrebaso Santamaria, Izaskun
dc.date.accessioned2026-02-26T13:38:25Z
dc.date.available2026-02-26T13:38:25Z
dc.date.issued2015-02-01
dc.date.updated2026-02-26T13:38:24Z
dc.description.abstractPeriodic activity in electroencephalography (PA-EEG) is shown as comprising a series of repetitive wave patterns that may appear in different cerebral regions and are due to many different pathologies. The diagnosis based on PA-EEG is an arduous task for experts in Clinical Neurophysiology, being mainly based on other clinical features of patients. Considering this difficulty in the diagnosis it is also very complicated to establish the prognosis of patients who present PA-EEG. The goal of this paper is to propose a method capable of determining patient prognosis based on characteristics of the PA-EEG activity. The approach, based on a parallel classification architecture and a majority vote system has proven successful by obtaining a success rate of 81.94% in the classification of patient prognosis of our database.en
dc.description.sponsorshipThis publication has been funded by the eVIDA research group grant from the Education and Research Department of the Basque Country, by Deiker from the University of Deusto and by the Basque Government SAIOTEK programen
dc.identifier.citationSánchez-González, A., García-Zapirain, B., Maestro Saiz, I., & Santamaría, I. Y. (2015). Patient prognosis based on feature extraction, selection and classification of EEG periodic activity. Bio-Medical Materials and Engineering, 26, S1569-S1578. https://doi.org/10.3233/BME-151456
dc.identifier.doi10.3233/BME-151456
dc.identifier.eissn1878-3619
dc.identifier.issn0959-2989
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5264
dc.language.isoeng
dc.publisherIOS Press
dc.rights© 2015 – IOS Press and the authors
dc.subject.otherBioinformatics
dc.subject.otherEEG periodic activity
dc.subject.otherFeature selection
dc.subject.otherMedical classification
dc.titlePatient prognosis based on feature extraction, selection and classification of EEG periodic activityen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPageS1578
oaire.citation.startPageS1569
oaire.citation.titleBio-Medical Materials and Engineering
oaire.citation.volume26
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc/4.0/
oaire.versionVoR
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