Polat, HasanOzerdem, Mehmet Sirac2024-04-242024-04-242016978-617-607-913-2https://hdl.handle.net/11468/2084712th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH) -- APR 20-24, 2016 -- Lviv, UKRAINEIn this study, EEG signals recorded from healthy individuals and EEG signals recorded from epileptic patients during epileptic seizures were classified. In the classification process, the Hilbert and wavelet transform were applied separately for the extraction of features from the EEG signals. The same statistical parameters were used in order to reduce the size of the feature vectors obtained via both approaches. K-nearest neighborhood (kNN) was used as classification algorithm. The obtained feature vector based on wavelet and Hilbert transform were classified separately via the kNN algorithm.eninfo:eu-repo/semantics/closedAccessEegEpilepsyClassificationWavelet TransfomK-Nearest NeighborhoodConference Object6669WOS:0003892712000172-s2.0-84981278229N/AN/A