Please use this identifier to cite or link to this item:
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.
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

Show full item record

CORE Recommender


checked on Sep 23, 2022

Page view(s)

checked on Dec 26, 2022

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.