A Segmental Polynomial Model of Ventricular Electrograms as a Simple and Efficient Morphology Discriminator for Implantable Devices

Abstract

The goal of this study is to construct a polynomial model of the ventricular electrogram (EGM) that faithfully reproduces the EGM and can be implemented in current, low computational power implantable devices. Such a model of ventricular EGMs is still lacking. New Zealand White rabbits underwent chronic implantation of pacemakers through a left thoracotomy approach. Unipolar ventricular EGMs sampled at a frequency of 1 kHz were stored digitally in 1-minute segments before and after intravenous injection of isoproterenol or procainamide. Each cardiac cycle was divided into a QR and an RQ segment which were modeled separately using a 6th order polynomial equation. The 14 coefficients of each cardiac cycle were reproducible throughout the baseline recordings (r > or = 0.94, P < 0.002). Isoproterenol caused no changes in the coefficients of the QR segment but significantly altered all but one of the seven coefficients of the RQ segment (p(6)= 0.0039, p(5)= 0.017, p(4)= 0.00007, p(3)= 0.112, p(2)= 0.00016, p(1)= 0.0086, p(a)= 0.00003). Procainamide caused statistically significant changes in both QR segment (p(6)= 0.018, p(5)= 0.287, p(4)= 0.019, p(3)= 0.176, p(2)= 0.016, p(1)= 0.362, p(a)= 0.000044) and RQ segment (p(6)= 0.0028, p(5)= 0.036, p(4)= 0.002, p(3)= 0.058, p(2)= 0.022, p(1)= 0.718, p(a)= 0.0018) coefficients. Our data demonstrate the feasibility of a segmental polynomial equation that reproduces the phases of depolarization and repolarization of the rabbit EGM. This model is reproducible and demonstrates the expected changes with antiarrhythmic drug administration. If reproduced in humans, these findings can have wide applications in patients with implantable devices, ranging from morphologic discrimination of arrhythmias to early detection of metabolic derangements or drug effects.