Browsing by Author "Türe, Hatice Sabiha"

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Citation - WoS: 5
Citation - Scopus: 5
Classification of Epileptic and Psychogenic Nonepileptic Seizures Via Time-Frequency Features of Eeg Data
(World Scientific Publ Co Pte Ltd, 2023) Karabiber Cura, Ozlem; Akan, Aydın; Türe, Hatice Sabiha; Sabiha Ture, Hatice
The majority of psychogenic nonepileptic seizures (PNESs) are brought on by psychogenic causes, but because their symptoms resemble those of epilepsy, they are frequently misdiagnosed. Although EEG signals are normal in PNES cases, electroencephalography (EEG) recordings alone are not sufficient to identify the illness. Hence, accurate diagnosis and effective treatment depend on long-term video EEG data and a complete patient history. Video EEG setup, however, is more expensive than using standard EEG equipment. To distinguish PNES signals from conventional epileptic seizure (ES) signals, it is crucial to develop methods solely based on EEG recordings. The proposed study presents a technique utilizing short-term EEG data for the classification of inter-PNES, PNES, and ES segments using time-frequency methods such as the Continuous Wavelet transform (CWT), Short-Time Fourier transform (STFT), CWT-based synchrosqueezed transform (WSST), and STFT-based SST (FSST), which provide high-resolution time-frequency representations (TFRs). TFRs of EEG segments are utilized to generate 13 joint TF (J-TF)-based features, four gray-level co-occurrence matrix (GLCM)-based features, and 16 higher-order joint TF moment (HOJ-Mom)-based features. These features are then employed in the classification procedure. Both three-class (inter-PNES versus PNES versus ES: ACC: 80.9%, SEN: 81.8%, and PRE: 84.7%) and two-class (Inter-PNES versus PNES: ACC: 88.2%, SEN: 87.2%, and PRE: 86.1%; PNES versus ES: ACC: 98.5%, SEN: 99.3%, and PRE: 98.9%) classification algorithms performed well, according to the experimental results. The STFT and FSST strategies surpass the CWT and WSST strategies in terms of classification accuracy, sensitivity, and precision. Moreover, the J-TF-based feature sets often perform better than the other two.
Citation - WoS: 3
Citation - Scopus: 4
Classification of Psychogenic Non-Epileptic Seizures Using Synchrosqueezing Transform of Eeg Signals
(European Signal Processing Conference, EUSIPCO, 2021) Cura O.K.; Yilmaz G.C.; Türe H.S.; Akan A.; Yilmaz, Gülce Cosku; Türe, Hatice Sabiha; Cura, Ozlem Karabiber; Akan, Aydin
Psychogenic 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.