A new defibrillator discrimination algorithm utilizing electrogram morphology analysis

Inappropriate therapies delivered by implantable cardioverter defibrillator s (ICDs) for supraventricular arrhythmias remain a common problem, particul arly in the event of rapidly conducted atrial fibrillation or marked sinus tachycardia. The ability to differentiate between ventricular tachycardia a nd supraventricular arrhythmias is the major goal of discrimination algorit hms. Therefore, we developed a new algorithm, SimDis, utilizing morphologic al 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 ventricula r tachycardia and atrial fibrillation. The arrhythmia complex is defined as nide if the duration is at least 30% greater than the template in sin us r hythm. For narrow complexes, four maximum and minimum values were measured to form a 4-element feature vector, which was compared with a representativ e feature vector during normal sinus rhythm. For each rhythm, any wide comp lex was classified as ventricular tachycardia. For narrow complexes, the se cond step of the algorithm compared the electrogram with the template, comp uting similarity and dissimilarity values. These values were then mapped to determine if they fell within a previously established discrimination boun dary. On the independent test set, the SimDis algorithm correctly classifie d 100% of ventricular tachycardias (27/27), 98% of sinus tachycardias (54/5 5), and 100% of episodes of atrial fibrillation (37/37). We conclude that t he SimDis algorithm yields high sensitivity (100%) and specificity (99%) fo r arrhythmia discrimination, using the computational capabilities of an ICD system.