Sciences & technologie. B, Sciences de l’ingénieur
Volume 0, Numéro 20, Pages 28-32
2003-12-31
Authors : Chikh M.a . Belgacem N . Meghnefi F . Bereksi-reguig F .
This paper describes the design, training and testing of an artificial neural network for classification of normal and abnormal premature ventricular contraction (PVC) beats in ECG signal. To carry out the classification task, we use the back-propagation (BP) learning algorithm. Two feature selections types were investigated with aim of generating the most appropriate input vector for the artificial neural network classifier (ANNC). The first selected information of each ECG beat is stored as 33-element vector; the second one is then reduced to a 10 dimensional vector using principal component analysis (P.C.A). The performance measures of the classifier will also be presented using as training and testing data sets from the MIT-BIH database.
Neural networks, ECG signal, PVC beats, Feature selection, MIT-BIH database.
Chouana Khaled
.
pages 49-60.
Hellal Aouatef
.
Djeddou Messaoud
.
Loukam Imed
.
A. Hameed Ibrahim
.
Al Dallal Jehad
.
Shawaqfah Moayyad
.
pages 69-83.
Bachkhaznadji A
.
Belhamri A
.
pages 19-27.
Bazizi Lydia
.
Rahmoune-aoudia Fazia
.
Lekadir Ouiza
.
pages 77-77.