Mert, AhmetAkan, Aydin2021-03-202021-03-2020181863-17031863-1711http://doi.org/10.1007/s11760-018-1304-yhttps://hdl.handle.net/20.500.12885/753Akan, Aydin/0000-0001-8894-5794; Mert, Ahmt/0000-0003-4236-3646This paper explores the data-driven properties of the empirical mode decomposition (EMD) for detection of epileptic seizures. A new method in frequency domain is presented to analyze intrinsic mode functions (IMFs) decomposed by EMD. They are used to determine whether the electroencephalogram (EEG) recordings contain seizure or not. Energy levels of the IMFs are extracted as threshold level to detect the changes caused by seizure activity. A scalar value energy resulting from the energy levels is individually used as an indicator of the epileptic EEG without the requirements of multidimensional feature vector and complex machine learning algorithms. The proposed methods are tested on different EEG recordings to evaluate the effectiveness of the proposed method and yield accuracy rate up to 97.89%.eninfo:eu-repo/semantics/closedAccessEmpirical mode decompositionEpilepsySeizure detectionFilter bankArticle10.1007/s11760-018-1304-y12814891496WOS:000444312400008Q3Q2