Spatial Projection of Tachycardia Electrograms for Morphology Discrimination

Discrimination of Ventricular Tachycardia (VT) from Supra-Ventricular Tachycardia (SVT) remains a major challenge for appropriate therapy delivery in Implantable Cardioverter Defibrillators (ICDs), especially in single chamber devices. We propose here a new discrimina-tion algorithm that analyzes, with a machine learning ap-proach, the morphology of a two-dimensional representa-tion of both a far-field and a near-field ventricular sensing channel. Features extracted from this representation allow comparisons between curves. Thus, arrhythmia discrimi-nation is performed by comparing an arrhythmia curve to a reference curve. A statistical classifier was trained on a private database and tested on the standard Ann Arbor Electrogram Li-braries. Our discrimination algorithm demonstrated high sensitivity and specificity for VT/SVT discrimination. The requirements of this algorithm make it appropriate for im-plementation in the simplest ICD system. 1