Employment and Comparison of Different Artificial Neural Networks for Epilepsy Diagnosis from EEG Signals
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 analyze Electroencephalography (EEG) signals by Wavelet Transform (WT) for diagnosis of epilepsy, to employ various Artificial Neural Networks (ANNs) for the signals' automatic classification. Furthermore, carrying out a performance comparison has been aimed. Three EEG signals have been decomposed into frequency sub bands by WT and the feature vectors have been extracted from these sub bands. In order to reduce the sizes of the extracted feature vectors, Principal Component Analysis (PCA) method has been applied when necessary and these feature vectors have been classified by five different ANNs as either epileptic or healthy. The performance evaluation has been carried out by conducting ROC analysis for the used ANN models that and their comparisons have also been included.
Açıklama
Anahtar Kelimeler
Epilepsy, Electroencephalography (EEG), Wavelet transform, Principal Component Analysis (PCA), Artificial Neural Networks (ANN), Roc analysis
Kaynak
JOURNAL OF MEDICAL SYSTEMS
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
36
Sayı
1
