The time-sequenced adaptive filter for analysis of cardiac arrhythmias in intraventricular electrograms.

Abstract

Implantable antitachycardia devices rely upon schemes for detecting cardiac arrhythmias which utilize rate and its variations; yet rate parameters often identify nonpathologic tachycardias as potentially dangerous and deliver unwarranted therapy. I have developed a predictive filter based upon the time-sequenced adaptive algorithm to be used as a supplement to rate criteria for detecting and identifying serious arrhythmias. The method does not require a fixed template and is independent of a priori patient information. The algorithm also provides arrhythmia diagnosis immediately at the change in rhythm. Algorithmic parameters were determined based upon a training set of patient data, and performance of the technique was evaluated with a completely new test set of 20 arrhythmia passages. The new algorithm yielded a sensitivity and specificity for ventricular tachycardia of 91% and 82% and for ventricular fibrillation of 71% and 93%. Correlation waveform analysis was used to diagnose the same test set of arrhythmias. It yielded a sensitivity and specificity for ventricular tachycardia of 100% and 67% and for ventricular fibrillation of 50% and 100%.