Chikh, MaBelgacem, NChikh, AzBereksi Reguig, F2012-05-232012-05-232003-09-27https://dspace.univ-tlemcen.dz/handle/112/839Conférence Internationale sur les Systèmes de Télécommunication , d’Electronique Médicale et d’Automatique, CISTEMA’2003Premature ventricular contraction (PVC) is a cardiac arrhythmia that can result in sudden death. Understanding and treatment of this disorder would be improved if patterns of electrical activation could be accurately identified and studied during its occurrence. In this paper, we shall review three feature extractions algorithms of the electrocardiogram (ECG) signal, fourier transform, linear prediction coding (LPC) technique and principal component analysis (PCA) method, with aim of generating the most appropriate input vector for a neural classifier. The performance measures of the classifier rate, sensitivity and specificity of these algorithms will also be presented using as training and testing data sets from the MIT-BIH database.enECG signallinear prediction codingprincipal component analysisFourier transformneural networkspremature ventricular contractionMIT-BIH arrhythmia databaseArticle