Open AccessEvaluation of new self‐learning techniques for the generation of criteria for differentiation of wide‐QRS tachycardia in supraventricular tachycardia and ventricular tachycardia
Author(s) -
Dassen Willem R.M.,
Mulleneers Rob G.A.,
Smeets Joep L.R.M.,
Wellens Hein J.J.,
Karthaus Vincent L.J.,
Talmon Jan L.
Publication year - 1995
Publication title -
clinical cardiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.263
H-Index - 72
eISSN - 1932-8737
pISSN - 0160-9289
DOI - 10.1002/clc.4960180213
Subject(s) - medicine , supraventricular tachycardia , ventricular tachycardia , qrs complex , tachycardia , cardiology , set (abstract data type) , electrocardiography , computer science , programming language
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.