A novel method for automated fractionation detection in ventricular tachycardia

Abstract

Catheter ablation therapy has become a key intervention in treatment of recurrent Ventricular Tachycardia (VT). We propose an automated fractionation detection method for VT patients which can potentially increase the accuracy and success rate of ablation therapy. A train of pacing with three different timings, close to ventricular effective refractory period (VERP), was introduced from right ventricle (RV) apex; surface and intracardiac activations were recordedfrom different sites of the RV in 10 patients (5 tests, and 5 control). Data was processed with Teager-Kasers Energy Operator for peak detection. Features (including latency, electrogram duration, and deflections) were automatically extracted and used to detect fractionation. Performance was evaluated by comparing the results with a control cohort. Test patients showed a significantly larger mean and standard deviation for all three pace timings, and for all features (p<0.05). All patients showed a significant increase in latency as pacing approached VERP (p<0.05). We showed that this process can be automated, and that the data obtained is correlative with arrhythmia. Furthermore, we showed that this data might be useful in isolating arrhythmogenic tissue for better targeted ablation.