Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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2015 - 201912020 - 20221

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Author: search.filters.author.Telatar, Ziyasearch.filters.applied.f.rights: search.filters.rights.info:eu-repo/semantics/closedAccess

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    Comparison of Non - Parametric PSD Detection Methods in the Anaylsis of EEG Signals in Sleep Apnea
    (2015) Kocak, Onur; Beytar, Faruk; Firat, Hikmet; Telatar, Ziya; Erogul, Osman
    Sleep apnea is characterized by complete cessation of airflowin the mouth and nose for at least 10 seconds and it is a disease that causes significant disruption of sleep patterns. In the absence of treatment, it can lead to serious health problems such as heart attack and stroke. Polysomnography is the gold standard examination methods used in the diagnosis of the disease. In this study, EEG signals obtained from the polysomnography recording are divided into sub-bands and their epochs in pre apnea, intra apnea and post apnea were analyzed. Non-parametric power spectral density (PSD) detection methods (Periodogram, Welch and Multi Taper) applied to the EEG signals were compared.
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    Automated Temporal Lobe Epilepsy And Psychogenic Nonepileptic Seizure Patient Discrimination From Multichannel EEG Recordings Using DWT Based Analysis
    (2022) Ficici, Cansel; Telatar, Ziya; Erogul, Osman
    Psychogenic 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.