title: Evaluation of new self-learning techniques for the generation of criteria for differentiation of wide-QRS tachycardia in supraventricular tachycardia and ventricular tachycardia.
abstract: This study presents a comparison of three different methods for differentiating between supraventricular and ventricular tachycardias with wide-QRS complex. One set of criteria, derived using classical statistical techniques, was compared with two new self-learning computer techniques: the artificial neural networks and the induction algorithm approach. By analyzing the results obtained in an independent test set, using these new techniques, the criteria defined by the classical method could be improved.
authors.affiliation: Department of Cardiology, University of Limburg, Maastricht, The Netherlands.
authors.forename: W R
authors.initials: WR
authors.lastname: Dassen
authors.forename: V L
authors.initials: VL
authors.lastname: Karthaus
authors.forename: J L
authors.initials: JL
authors.lastname: Talmon
authors.forename: R G
authors.initials: RG
authors.lastname: Mulleneers
authors.forename: J L
authors.initials: JL
authors.lastname: Smeets
authors.forename: H J
authors.initials: HJ
authors.lastname: Wellens
mesh_terms: D000465:Algorithms; D003661:Decision Support Techniques; D003663:Decision Trees; D003937:Diagnosis, Differential; D004562:Electrocardiography; D006801:Humans; D016571:Neural Networks, Computer; D013610:Tachycardia / Q000175:diagnosis*; D013617:Tachycardia, Supraventricular / Q000175:diagnosis*; D017180:Tachycardia, Ventricular / Q000175:diagnosis*