Combined Signal Averaging and Electrocardiographic Imaging Method to Non:Invasively Identify Atrial and Ventricular Tachycardia Mechanisms

Abstract

Current clinical electrocardiographic imaging (ECGi) aims to reconstruct epicardial signals using a single selected beat. However, minor sources of error that can be present on a beat-to-beat basis may affect the result, complicating arrhythmia mechanism identification and thus diagnosis and treatment. In this study, we applied signal averaging (SA) to ECGi on atrial and ventricular tachycardia diagnosis compared to a single beat approach. For that, a multi-lead SA algorithm was applied to QRS-complexes or atrial activity to obtain a robust template. Datasets came from patients with confirmed tachycardia obtained by invasive diagnosis. The non-invasive diagnosis was compared to the invasive with activation time maps and other clinical features in order to evaluate the contribution of SA versus the single beat approach (SB). The outcomes indicate that signal averaging improves the quality of non-invasive diagnosis for tachycardia reducing the diagnosis variability and the cycle length error (from 28% with SB to 13% with SA).