Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3689
Title: Epileptic Activity Detection in EEG Signals using Linear and Non-linear Feature Extraction Methods
Authors: Fıçıcı, C. Öğretmenoğlu
Eroğul, Osman
Telatar, Ziya
Keywords: Epilepsy
Autoregressive Coefficients
Linear prediction error
Shannon entropy
Approximate entropy
Support vector machine
Issue Date: Nov-2019
Publisher: IEEE
Source: Ficici, C., Erogul, O., & Telatar, Z. (2019, November). Epileptic Activity Detection in EEG Signals using Linear and Non-linear Feature Extraction Methods. In 2019 11th International Conference on Electrical and Electronics Engineering (ELECO) (pp. 449-455). IEEE.
Abstract: The aim of this study is to obtain an automated medical diagnosis-support system about epilepsy by classifying EEG signal epochs as ictal, inter-ictal and normal. EEG signals were analyzed in their sub-bands obtained via discrete wavelet transform. Linear and non-linear methods are used for extracting features of normal, ictal and inter-ictal states. Support vector machine classification is realized by using time domain features which are autoregressive coefficients and linear prediction error energy; and information theory based features which are Shannon entropy and approximate entropy. In order to improve accuracy, linear and non-linear features are combined and then SVM trained by these features. By the proposed method, 99.0%, 96.0%, 100% accuracy, sensitivity and specificity are obtained for epileptic and non-epileptic classification, while accuracy, sensitivity and specificity of 98.2%, 95.0 and 99.0% are obtained for normal, ictal, and inter-ictal activity classification, respectively.
URI: https://hdl.handle.net/20.500.11851/3689
https://ieeexplore.ieee.org/document/8990401
ISBN: 9786050112757
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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