Veuillez utiliser cette adresse pour citer ce document : http://dspace1.univ-tlemcen.dz/handle/112/839
Titre: The Use of Artificial Neural Network to Detect the Premature Ventricular Contraction (PVC) Beats
Auteur(s): Chikh, MA
Belgacem, N
Chikh, AZ
Bereksi Reguig, F
Mots-clés: ECG signal
linear prediction coding
principal component analysis
Fourier transform
neural networks
premature ventricular contraction
MIT-BIH arrhythmia database
Date de publication: 27-sep-2003
Editeur: University of Tlemcen
Résumé: Premature 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.
Description: Conférence Internationale sur les Systèmes de Télécommunication , d’Electronique Médicale et d’Automatique, CISTEMA’2003
URI/URL: http://dspace.univ-tlemcen.dz/handle/112/839
Collection(s) :Articles internationaux

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