Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8634
Title: Automated temporal lobe epilepsy and psychogenic nonepileptic seizure patient discrimination from multichannel EEG recordings using DWT based analysis
Authors: Fıçıcı Cansel
Telatar, Ziya
Eroğul, Osman
Keywords: EEG
Epilepsy detection
Machine learning
Psychogenic nonepileptic seizure
Temporal lobe epilepsy
Automation
Biomedical signal processing
Discrete wavelet transforms
Electroencephalography
Electrophysiology
Neurology
Signal reconstruction
Discrete-wavelet-transform
Epilepsy detection
Examination of electroencephalography
Healthy subjects
High-accuracy
Psychogenic nonepileptic seizure
Subbands
Subject discrimination
Temporal lobe epilepsy
Temporal lobe epilepsy patients
Machine learning
Issue Date: 2022
Publisher: Elsevier Ltd
Source: Fıçıcı, 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.
Abstract: 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. © 2022
URI: https://doi.org/10.1016/j.bspc.2022.103755
https://hdl.handle.net/20.500.11851/8634
ISSN: 1746-8094
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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