Epileptic Seizure Detection from EEG Signals by Using Wavelet and Hilbert Transform
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Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In 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.
Açıklama
12th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH) -- APR 20-24, 2016 -- Lviv, UKRAINE
Anahtar Kelimeler
Eeg, Epilepsy, Classification, Wavelet Transfom, K-Nearest Neighborhood
Kaynak
2016 Xii International Conference on Perspective Technologies and Methods in Mems Design (Memstech)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
