Efficient algorithms for detecting changes in intraventricular electrogram morphology

Computing a correlation coefficient between a stored template of a normal beat and subsequent beats is a widely used indicator of changes in electrogram morphology. While effective, this method is quite computationally demanding, particularly for use in implantable devices with limited available power. We investigated methods for computing a normalized autocorrelation as efficient template matching alternatives to the correlation coefficient. These four algorithms, polarity incidence (PI), hybrid sign (HS), modified hybrid sign (MHS), and relative magnitude (RM) are compared with the correlation coefficient (CC) for detecting changes in intraventricular electrogram morphology. We sought to determine the number of instances in which 75% (or 90%) of a patient's sinus rhythm beats could be distinguished from 75% (or 90%) of the patient's ventricular tachycardia beats at the 95% confidence level in 30 patients. The relative magnitude method performs nearly as well as the correlation coefficient in nearly every case but requires only 3 multiplies per application. Hence the relative magnitude algorithm may be more appropriate for implantable devices.