Yayık, ApdullahYıldırım, EsenKutlu, YakupYıldırım, Serdar2019-07-162019-07-1620152147-6799https://trdizin.gov.tr/publication/paper/detail/TVRreU56Y3dNQT09https://hdl.handle.net/20.500.12483/2326Epileptic seizure detection and prediction from electroencephalography (EEG) is a vital area of research. In this study, SecondOrder Difference Plot (SODP) is used to extract features based on consecutive difference of time domain values from three states of EEG (pre-ictal, ictal and inter-ictal), and Multi-Layer Neural Network classifier is used to classify these three classes. The proposed technique is tested on a publicly available EEG database and classified with Naive Bayes and k-nearest neighbor classifiers. As a result, it is shown that overall accuracy of 98.70% can be achieved by using the proposed system with Neural Network classifier.Epileptic seizure detection and prediction from electroencephalography (EEG) is a vital area of research. In this study, SecondOrder Difference Plot (SODP) is used to extract features based on consecutive difference of time domain values from three states of EEG (pre-ictal, ictal and inter-ictal), and Multi-Layer Neural Network classifier is used to classify these three classes. The proposed technique is tested on a publicly available EEG database and classified with Naive Bayes and k-nearest neighbor classifiers. As a result, it is shown that overall accuracy of 98.70% can be achieved by using the proposed system with Neural Network classifier.eninfo:eu-repo/semantics/openAccessBilgisayar BilimleriYapay ZekaArticle311418