Veuillez utiliser cette adresse pour citer ce document : http://dspace1.univ-tlemcen.dz/handle/112/1967
Titre: CARDIAC ARRHYTHMIA DIAGNOSIS USING A NEURO-FUZZY APPROACH
Auteur(s): Benali, R
Dib, N
Beriksi Reguig, F
Mots-clés: ECG QRS detection
neuro-fuzzy
fuzzy logic
VPC
explicit classification
MIT-BIH database
Date de publication: sep-2010
Editeur: University of Tlemcen
Résumé: The ventricular premature contractions (VPC) are cardiac arrhythmias that are widely encountered in the cardiologic field. They can be detected using the electrocardiogram (ECG) signal parameters. A novel method for detecting VPC from the ECG signal is proposed using a new algorithm (Slope) combined with a fuzzy-neural network (FNN). To achieve this objective, an algorithm for QRS detection is first implemented, and then a neuro-fuzzy classifier is developed. Its performances are evaluated by computing the percentages of sensitivity (SE), specificity (SP), and correct classification (CC). This classifier allows extraction of rules (knowledge base) to clarify the obtained results. We use the medical database (MIT-BIH) to validate our results.
Description: Journal of Mechanics in Medicine and Biology , ISSN : 0219-5194, DOI : 10.1142/S021951941000354X, Issue : 3, Volume : 10, pp. 417-429, September 2010.
URI/URL: http://dspace.univ-tlemcen.dz/handle/112/1967
ISSN: 0219-5194
Collection(s) :Articles internationaux

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