Automatic Quality Electrogram Assessment Improves Reentrant Activity Identification in Atrial Fibrillation

Abstract

Location of reentrant electrical activity responsible for driving atrial fibrillation (AF) is key to ablative therapies. The aim of this work is to study the effect of the quality of the electrograms (EGMs) used for 3D phase analysis on reentrant activity identification, as well as to develop an algorithm capable of automatically identifying low- quality signals.EGMs signals from 259 episodes obtained from 29 AF patients were recorded using 64-electrode basket catheters. Low-quality EGMs were manually identified. Reentrant activity was identified in 3D phase maps and provided an area under the ROC curve (AUC) of 0.69 when compared to a 2D activation-based method. Reentries located in regions with poor-quality EGMs were then removed, increasing the AUC to 0.80. The EGM classification algorithm showed a similar performance both for low-quality EGM identification (sensitivity 0.91 and specificity 0.80) and for reentrant activity location with 3D phase analysis (AUC 0.80).Discard of reentrant activity identified in regions where EGMs showed low quality significantly improved the specificity of the 3D phase analysis. Besides, EGMs classification according to their quality proved to be possible using time and spectral domain parameters.