Cura O.K.Yilmaz G.C.Türe H.S.Akan A.2023-06-162023-06-1620219.79E+122219-5491https://doi.org/10.23919/EUSIPCO54536.2021.9615988https://hdl.handle.net/20.500.14365/373129th European Signal Processing Conference, EUSIPCO 2021 -- 23 August 2021 through 27 August 2021 -- 175283Psychogenic non-epileptic seizures (PNES) are mostly associated with psychogenic factors, where the symptoms are often confused with epilepsy. Since electroencephalography (EEG) signals maintain their normal state in PNES cases, it is not possible to diagnose using the EEG recordings alone. Therefore, long-term video EEG records and detailed patient history are needed for reliable diagnosis and correct treatment. However, the video EEG recording method is more expensive than the classical EEG. Therefore, it has great importance to distinguish PNES signals from normal epileptic seizure (ES) signals using only the EEG recordings. In the proposed study, using the Synchrosqueezed Transform (SST) that gives high-resolution time-frequency representations (TFR), inter-PNES, PNES, and Epileptic seizure EEG classification is introduced. 17 joint TF features are calculated from the TFRs, and various classifiers are used for classification processes. Classification problems with three classes (inter-PNES, PNES, and ES) and two classes (inter-PNES and PNES) are considered. Experimental results indicated that both three-class and two-class classification approaches achieved encouraging validation performances (three-class problem: 95.8% ACC, 86.9% SEN, 91.4% PRE, and 8.6% FDR; two-class problem: 96.4% ACC, 96.8% SEN, 97.3% PRE, and FDR lower than 10%). © 2021 European Signal Processing Conference. All rights reserved.eninfo:eu-repo/semantics/closedAccessEEGPNESSSTTime-frequency analysisBiomedical signal processingClassification (of information)ElectroencephalographyElectrophysiologyNeurodegenerative diseasesPatient treatmentEpileptic seizuresHigh resolutionNormal statePatient historyPsychogenic non-epileptic seizureResolution timeSynchrosqueezed transformSynchrosqueezingTime-frequency AnalysisTime-frequency representationsNeurophysiologyConference Object10.23919/EUSIPCO54536.2021.96159882-s2.0-85123210204