Automatic arrhythmia identification using analysis of the atrioventricular association. Application to a new...

Atrioventricular association is a key criterion for arrhythmia diagnosis. Its use in a defibrillator should significantly reduce the incidence of inappropriate shocks. Therefore, we evaluated the diagnostic accuracy of an algorithm that uses dual-chamber sensing and analysis of atrioventricular association to discriminate ventricular from supraventricular arrhythmias in a prototype of an implantable defibrillator.

The algorithm performed a stepwise analysis of arrhythmias. The rhythm was first classified on the basis of cycle lengths. Each episode was then classified as supraventricular or ventricular in origin on the basis of the stability of cycle lengths and atrioventricular association. This algorithm was evaluated in 156 episodes of induced sustained tachycardias. Eighty-nine tachycardias were taken from the Ann Arbor electrogram library; the others were recorded in 50 patients during electrophysiological studies. The atrial and ventricular signals were stored on an external recorder and then injected 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 episodes. 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 fibrillation and atrial flutter were 92% and 86%, respectively. For ventricular tachycardia and ventricular fibrillation, the values were 95% and 100%, respectively.

Analysis of atrioventricular association promotes reliable differentiation between ventricular and supraventricular tachycardias and should enhance the diagnostic capabilities of implantable defibrillators.