Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/3689
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Fıçıcı, C. Öğretmenoğlu | - |
dc.contributor.author | Eroğul, Osman | - |
dc.contributor.author | Telatar, Ziya | - |
dc.date.accessioned | 2020-09-17T14:43:38Z | - |
dc.date.available | 2020-09-17T14:43:38Z | - |
dc.date.issued | 2019-11 | |
dc.identifier.citation | 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. | en_US |
dc.identifier.isbn | 9786050112757 | |
dc.identifier.other | article number 8990401 | |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/3689 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/8990401 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2019 11th International Conference on Electrical and Electronics Engineering (ELECO) | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Epilepsy | en_US |
dc.subject | Autoregressive Coefficients | en_US |
dc.subject | Linear prediction error | en_US |
dc.subject | Shannon entropy | en_US |
dc.subject | Approximate entropy | en_US |
dc.subject | Support vector machine | en_US |
dc.title | Epileptic Activity Detection in Eeg Signals Using Linear and Non-Linear Feature Extraction Methods | en_US |
dc.type | Conference Object | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Biomedical Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümü | tr_TR |
dc.identifier.startpage | 449 | |
dc.identifier.endpage | 455 | |
dc.authorid | 0000-0002-4640-6570 | - |
dc.identifier.wos | WOS:000552654100087 | en_US |
dc.identifier.scopus | 2-s2.0-85080922476 | en_US |
dc.institutionauthor | Eroğul, Osman | - |
dc.identifier.doi | 10.23919/ELECO47770.2019.8990401 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
item.openairetype | Conference Object | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.2. Department of Biomedical Engineering | - |
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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