Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8634
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dc.contributor.authorFıçıcı Cansel-
dc.contributor.authorTelatar, Ziya-
dc.contributor.authorEroğul, Osman-
dc.date.accessioned2022-07-30T16:43:39Z-
dc.date.available2022-07-30T16:43:39Z-
dc.date.issued2022-
dc.identifier.citationFıçıcı, C., Telatar, Z., & Eroğul, O. (2022). Automated temporal lobe epilepsy and psychogenic nonepileptic seizure patient discrimination from multichannel EEG recordings using DWT based analysis. Biomedical Signal Processing and Control, 77, 103755.en_US
dc.identifier.issn1746-8094-
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2022.103755-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8634-
dc.description.abstractPsychogenic nonepileptic seizure (PNES) and epileptic seizure resemble each other, behaviorally. This similarity causes misdiagnosis of PNES and epilepsy patients, thus patients suffering from PNES may be treated with antiepileptic drugs which can have various side effects. Furthermore, seizure is diagnosed after time consuming examination of electroencephalography (EEG) recordings realized by the expert. In this study, automated temporal lobe epilepsy (TLE) patient, PNES patient and healthy subject discrimination method from EEG signals is proposed in order to eliminate the misdiagnosis and long inspection time of EEG recordings. Also, this study provides automated approach for TLE interictal and ictal epoch classification, and TLE, PNES and healthy epoch classification. For this purpose, subbands of EEG signals are determined from discrete wavelet transform (DWT), then classification is performed using ensemble classifiers fed with energy feature extracted from the subbands. Experiments are conducted by trying two approaches for TLE, PNES and healthy epoch classification and patient discrimination. Results show that in the TLE, PNES and healthy epoch classification the highest accuracy of 97.2%, sensitivity of 97.9% and specificity of 98.1% were achieved by applying adaptive boosting method, and the highest accuracy of 87.1%, sensitivity of 86.0% and specificity of 93.6% were attained using random under sampling (RUS) boosting method in the TLE patient, PNES patients and the healthy subject discrimination. © 2022en_US
dc.description.sponsorshipAnkara Universitesien_US
dc.description.sponsorshipClinical Research Ethics Committee approval was provided by the Faculty of Medicine of the Ankara University for the retrospective study on TLE and PNES patient detection from EEG recordings.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEEGen_US
dc.subjectEpilepsy detectionen_US
dc.subjectMachine learningen_US
dc.subjectPsychogenic nonepileptic seizureen_US
dc.subjectTemporal lobe epilepsyen_US
dc.subjectAutomationen_US
dc.subjectBiomedical signal processingen_US
dc.subjectDiscrete wavelet transformsen_US
dc.subjectElectroencephalographyen_US
dc.subjectElectrophysiologyen_US
dc.subjectNeurologyen_US
dc.subjectSignal reconstructionen_US
dc.subjectDiscrete-wavelet-transformen_US
dc.subjectEpilepsy detectionen_US
dc.subjectExamination of electroencephalographyen_US
dc.subjectHealthy subjectsen_US
dc.subjectHigh-accuracyen_US
dc.subjectPsychogenic nonepileptic seizureen_US
dc.subjectSubbandsen_US
dc.subjectSubject discriminationen_US
dc.subjectTemporal lobe epilepsyen_US
dc.subjectTemporal lobe epilepsy patientsen_US
dc.subjectMachine learningen_US
dc.titleAutomated Temporal Lobe Epilepsy and Psychogenic Nonepileptic Seizure Patient Discrimination From Multichannel Eeg Recordings Using Dwt Based Analysisen_US
dc.typeArticleen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümüen_US
dc.departmentFaculties, Faculty of Engineering, Department of Biomedical Engineeringen_US
dc.identifier.volume77en_US
dc.identifier.wosWOS:000806506600008en_US
dc.identifier.scopus2-s2.0-85130079785en_US
dc.institutionauthorEroğul, Osman-
dc.identifier.doi10.1016/j.bspc.2022.103755-
dc.authorscopusid57215433394-
dc.authorscopusid6603237932-
dc.authorscopusid56247443100-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.2. Department of Biomedical Engineering-
Appears in Collections:Biyomedikal Mühendisliği Bölümü / Department of Biomedical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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