Wavelet Neural Network for Classification of Bundle Branch Blocks
Küçük Resim Yok
Tarih
2011
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
INT ASSOC ENGINEERS-IAENG
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Bundle branch blocks are very important for the heart treatment immediately. Left and right bundle branch blocks represent an independent predictor in which underlying cardiac disease that needs to be treated. In this study, we presented a model of wavelet neural network for classification of bundle branch blocks. The proposed wavelet neural network was implemented using Morlet and Mexican hat wavelet functions as activation function in hidden layer. ECG data in this study were formed by taken from MIT-BIH ECG Arrhythmia Database. Training and test data consist of three different beat types, which are belong to ECG signal classes of normal, right bundle branch block and left bundle branch block. The performed experimental studies were demonstrated that wavelet neural network designed by Mexican hat wavelet was successful than other network which designed by Morlet wavelet.
Açıklama
World Congress on Engineering (WCE 2011) -- JUL 06-08, 2011 -- Imperial Coll, London, UNITED KINGDOM
Anahtar Kelimeler
Wavelet neural network, ECG, classification, QRS detection
Kaynak
WORLD CONGRESS ON ENGINEERING, WCE 2011, VOL II
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
