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dc.contributor.authorMporas, Iosif
dc.contributor.authorPippa, Evangelia
dc.contributor.authorZacharaki, Evangelia I.
dc.contributor.authorTsirka, Vasiliki
dc.contributor.authorRichardson, Mark P.
dc.contributor.authorKoutroumanidis, Michael
dc.contributor.authorMegalooikonomou, Vasileios
dc.date.accessioned2017-07-10T12:09:48Z
dc.date.available2017-07-10T12:09:48Z
dc.date.issued2016-01-01
dc.identifier.citationMporas , I , Pippa , E , Zacharaki , E I , Tsirka , V , Richardson , M P , Koutroumanidis , M & Megalooikonomou , V 2016 , ' Improving classification of epileptic and non-epileptic EEG events by feature selection ' , Neurocomputing , vol. 171 , pp. 576-585 . https://doi.org/10.1016/j.neucom.2015.06.071
dc.identifier.issn0925-2312
dc.identifier.otherPURE: 10687793
dc.identifier.otherPURE UUID: 5507dc51-a7bd-46f1-ad79-84b98f3a8ef2
dc.identifier.otherScopus: 84947020814
dc.identifier.urihttp://hdl.handle.net/2299/18845
dc.descriptionThis is the Accepted Manuscript version of the following article: E. Pippa, et al, “Improving classification of epileptic and non-epileptic EEG events by feature selection”, Neurocomputing, Vol. 171: 576-585, July 2015. The final published version is available at: http://www.sciencedirect.com/science/article/pii/S0925231215009509?via%3Dihub Copyright © 2015 Elsevier B.V.
dc.description.abstractCorrectly diagnosing generalized epileptic from non-epileptic episodes, such as psychogenic non epileptic seizures (PNES) and vasovagal or vasodepressor syncope (VVS), despite its importance for the administration of appropriate treatment, life improvement of the patient, and cost reduction for patient and healthcare system, is rarely tackled in the literature. Usually clinicians differentiate between generalized epileptic seizures and PNES based on clinical features and video-EEG. In this work, we investigate the use of machine learning techniques for automatic classification of generalized epileptic and non-epileptic events based only on multi-channel EEG data. For this purpose, we extract the signal patterns in the time domain and in the frequency domain and then combine all features across channels to characterize the spatio-temporal manifestation of seizures. Several classification algorithms are explored and evaluated on EEG epochs from 11 subjects in an inter-subject cross-validation setting. Due to large number of features feature ranking and selection is performed prior to classification using the ReliefF ranking algorithm within two different voting strategies. The classification models using feature subsets, achieved higher accuracy compared to the models using all features reaching 95% (Bayesian Network), 89% (Random Committee) and 87% (Random Forest) for binary classification (epileptic versus non-epileptic). The results demonstrate the competitiveness of this approach as opposed to previous methods.en
dc.language.isoeng
dc.relation.ispartofNeurocomputing
dc.rightsEmbargoed
dc.titleImproving classification of epileptic and non-epileptic EEG events by feature selectionen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionSchool of Engineering and Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.contributor.institutionBioEngineering
dc.contributor.institutionCommunications and Intelligent Systems
dc.description.statusPeer reviewed
dc.date.embargoedUntil2017-07-10
dc.identifier.urlhttp://www.sciencedirect.com/science/article/pii/S0925231215009509
dc.relation.schoolSchool of Engineering and Computer Science
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2016-01-01
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1016/j.neucom.2015.06.071
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
rioxxterms.licenseref.startdate2017-07-10
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.date.embargo2017-07-10
herts.rights.accesstypeEmbargoed


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