A channel selection method for epilepsy seizure prediction

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dc.authorscopusid56236872500
dc.authorscopusid36793379200
dc.authorscopusid55666247200
dc.contributor.authorCoşgun, Erman
dc.contributor.authorÇelebi, A.
dc.contributor.authorGüllü, Mehmet Kemal
dc.date.accessioned2022-02-15T16:57:28Z
dc.date.available2022-02-15T16:57:28Z
dc.date.issued2021
dc.departmentBakırçay Üniversitesien_US
dc.descriptionKocaeli University;Kocaeli University Technoparken_US
dc.description2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 -- 25 August 2021 through 27 August 2021 -- -- 172175en_US
dc.description.abstractThe development of systems that can predict epilepsy seizures in real time offers great hope for epilepsy patients. These systems aim to prevent accidents that patients may experience due to loss of consciousness during seizures. Therefore, systems that can predict epileptic seizures should both work in real time and be designed to maintain the daily activities of the patient. In this case, a system with as few electrodes as possible should be developed. In this study, it is aimed to choose the most appropriate electrode in predicting epileptic seizures. Channel selection is made according to two parameters and its effect on seizure prediction is examined. The first parameter is the difference in variance between preictal and interictal; The other parameter is the weighted average sensitivity (WAS). The Rusboosted Tree ensemble classification is used to calculate WAS. The prediction process is carried out with the method we proposed in the previous study. For performance evaluation, prediction accuracy, sensitivity (SEN) and false alarm rates per hour (FPR) are calculated. The prediction performance for the channel selected according to the variance difference results are 69%, 70.9% and 0.054 respectively and the for the channel selected according to WAS results are 69%, 71.8% and 0.031 respectively. © 2021 IEEE.en_US
dc.description.sponsorshipKocaeli Üniversitesi: :2018/063en_US
dc.description.sponsorshipACKNOWLEDGMENT This work was supported by Kocaeli University, Scientific Research Projects Coordination Unit, under project number:2018/063.en_US
dc.identifier.doi10.1109/INISTA52262.2021.9548583
dc.identifier.isbn9781665436038
dc.identifier.scopus2-s2.0-85116615882en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/INISTA52262.2021.9548583
dc.identifier.urihttps://hdl.handle.net/20.500.14034/173
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.journal2021 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2021 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectChannel selectionen_US
dc.subjectEEG.en_US
dc.subjectEpilepsy predictionen_US
dc.subjectRusBoosted Tree classificationen_US
dc.subjectElectrodesen_US
dc.subjectForestryen_US
dc.subjectNeurophysiologyen_US
dc.subjectReal time systemsen_US
dc.subjectAverage sensitivitiesen_US
dc.subjectChannel selectionen_US
dc.subjectEEG.en_US
dc.subjectEpilepsy predictionen_US
dc.subjectLoss of consciousnessen_US
dc.subjectReal- timeen_US
dc.subjectRusboosted tree classificationen_US
dc.subjectSeizure predictionen_US
dc.subjectSelection methodsen_US
dc.subjectWeighted averagesen_US
dc.subjectForecastingen_US
dc.titleA channel selection method for epilepsy seizure predictionen_US
dc.typeConference Objecten_US

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