AUTOMATIC ARRHYTHMIA IDENTIFICATION USING ANALYSIS OF THE ATRIOVENTRICULAR ASSOCIATION - APPLICATION TO A NEW-GENERATION OF IMPLANTABLE DEFIBRILLATORS

Background Atrioventricular association is a key criterion for arrhyth mia diagnosis. Its use in a defibrillator should significantly reduce the incidence of inappropriate shocks. Therefore, we evaluated the dia gnostic accuracy of an algorithm that uses dual-chamber sensing and an alysis of atrioventricular association to discriminate ventricular fro m supraventricular arrhythmias in a prototype of an implantable defibr illator. Methods and Results The algorithm performed a stepwise analys is of arrhythmias. The rhythm was first classified on the basis of cyc le lengths. Each episode was then classified as supraventricular or ve ntricular in origin on the basis of the stability of cycle lengths and atrioventricular association. This algorithm was evaluated in 156 epi sodes of induced sustained tachycardias. Eighty-nine tachycardias were taken from the Ann Arbor electrogram library; the others were recorde d in 50 patients during electrophysiological studies. The atrial and v entricular signals were stored on an external recorder and then inject ed into an external prototype of a defibrillator system. The algorithm correctly diagnosed 96% of ventricular tachycardia episodes, 100% of ventricular fibrillation episodes, and 92% of double-tachycardia episo des. The mean detection time for ventricular tachycardia was 2.6+/-0.8 seconds, and for ventricular fibrillation, it was 2.1+/-0.4 seconds. The positive predictive values for the diagnoses of atrial fibrillatio n and atrial flutter were 92% and 86%, respectively. For ventricular t achycardia and ventricular fibrillation, the values were 95% and 100%, respectively. Conclusions Analysis of atrioventricular association pr omotes reliable differentiation between ventricular and supraventricul ar tachycardias and should enhance the diagnostic capabilities of impl antable defibrillators.