A new defibrillator discrimination algorithm utilizing electrogram morphology analysis

Inappropriate therapies delivered by implantable cardioverter defibrillators (ICDs) for supraventricular arrhythmias remain a common problem, particularly in the event of rapidly conducted atrial fibrillation or marked sinus tachycardia. The ability to differentiate between ventricular tachycardia and supraventricular arrhythmias is the major goal of discrimination algorithms. Therefore, we developed a new algorithm, SimDis, utilizing morphological features of the shocking electrograms. This algorithm was developed from electrogram data obtained from 36 patients undergoing ICD implantation. An independent test set was evaluated in 25 patients. Recordings were made in sinus rhythm, sinus tachycardia, and following the induction of ventricular tachycardia and atrial fibrillation. The arrhythmia complex is defined as wide if the duration is at least 30% greater than the template in sinus rhythm. For narrow complexes, four maximum and minimum values were measured to form a 4-element feature vector, which was compared with a representative feature vector during normal sinus rhythm. For each rhythm, any wide complex was classified as ventricular tachycardia. For narrow complexes, the second step of the algorithm compared the electrogram with the template, computing similarity and dissimilarity values. These values were then mapped to determine if they fell within a previously established discrimination boundary. On the independent test set, the SimDis algorithm correctly classified 100% of ventricular tachycardias (27/27), 98% of sinus tachycardias (54/55), and 100% of episodes of atrial fibrillation (37/37). We conclude that the SimDis algorithm yields high sensitivity (100%) and specificity (99%) for arrhythmia discrimination, using the computational capabilities of an ICD system.