Diagnosis of Epilepsy from Electroencephalography Signals Using Multilayer Perceptron and Elman Artificial Neural Networks and Wavelet Transform
Küçük Resim Yok
Tarih
2012
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, it has been intended to perform an automatic classification of Electroencephalography (EEG) signals via Artificial Neural Networks (ANN) and to investigate these signals using Wavelet Transform (WT) for diagnosing epilepsy syndrome. EEG signals have been decomposed into frequency sub-bands using WT and a set of feature vectors which were extracted from the sub-bands. Dimensions of these feature vectors have been reduced via Principal Component Analysis (PCA) method and then classified as epileptic or healthy using Multilayer Perceptron (MLP) and ELMAN ANN. Performance evaluation of the used ANN models have been carried out by performing Receiver Operation Characteristic (ROC) analysis.
Açıklama
Anahtar Kelimeler
Epilepsy, Electroencephalography, Signal processing, Wavelet transform, Artificial neural networks, Receiver operation characteristic analysis, Multilayer perceptron, Elman network, Principal component analysis
Kaynak
JOURNAL OF MEDICAL SYSTEMS
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
36
Sayı
1
