Short-Time Estimation of Fractionation in Atrial Fibrillation with Coarse-Grained Correlation Dimension for Mapping the Atrial Substrate

Abstract

Atrial fibrillation (AF) is currently the most common cardiac arrhythmia, with catheter ablation (CA) of the pulmonary veins (PV) being its first line therapy. Ablation of complex fractionated atrial electrograms (CFAEs) outside the PVs has demonstrated improved long-term results, but their identification requires a reliable electrogram (EGM) fractionation estimator. This study proposes a technique aimed to assist CA procedures under real-time settings. The method has been tested on three groups of recordings: Group 1 consisted of 24 highly representative EGMs, eight of each belonging to a different AF Type. Group 2 contained the entire dataset of 119 EGMs, whereas Group 3 contained 20 pseudo-real EGMs of the special Type IV AF. Coarse-grained correlation dimension (CGCD) was computed at epochs of 1 s duration, obtaining a classification accuracy of 100% in Group 1 and 84.0–85.7% in Group 2, using 10-fold cross-validation. The receiver operating characteristics (ROC) analysis for highly fractionated EGMs, showed 100% specificity and sensitivity in Group 1 and 87.5% specificity and 93.6% sensitivity in Group 2. In addition, 100% of the pseudo-real EGMs were correctly identified as Type IV AF. This method can consistently express the fractionation level of AF EGMs and provides better performance than previous works. Its ability to compute fractionation in short-time can agilely detect sudden changes of AF Types and could be used for mapping the atrial substrate, thus assisting CA procedures under real-time settings for atrial substrate modification.

The Relevance of Heart Rate Fluctuation When Evaluating Atrial Substrate Electrical Features in Catheter Ablation of Paroxysmal Atrial Fibrillation

“…Invasive EGM analysis is at the frontline of atrial substrate evaluation, assisting the detection of areas with structural remodeling [ 7 , 10 ]. During AF, these areas are specified as areas with a high dominant frequency and low-voltage EGMs or areas with highly complex EGMs, and various techniques have been developed to facilitate their detection as possible non-PV ablation targets [ 7 , 67 , 68 ]. During SR, the detection of areas sustaining the AF activity is achieved with the help of specific atrial sites that are used for pacing [ 10 , 14 ].…”

The Dissimilar Impact in Atrial Substrate Modificationof Left and Right Pulmonary Veins Isolation after Catheter Ablation of Paroxysmal Atrial Fibrillation

“…Many techniques exist to localize non-PV triggers, with complex fractionated atrial electrograms (CFAEs) during AF [ 4 , 13 , 14 ] or low voltage electrograms (EGMs) during SR [ 4 , 15 , 16 ] being two of the most established ones. A combination of both techniques along with highly proportioned EGM fractionation has recently indicated sites showing fibrosis, with a high correlation between these areas in AF and SR [ 6 ].…”

“…Evidence shows that additional ablation of these sites offers little or no improved results with respect to single PVs ablation [ 17 , 18 , 19 ]. It remains unclear, however, whether failure of additional CA applications to provide significant improvement in termination of AF stems from the incapacity of CA on sites other than PVs to terminate AF or from a vague and unclear definition of areas in need of ablation due to highly complex EGMs, thus highlighting the need for more reliable algorithms able to properly evaluate the atrial substrate [ 6 , 14 ].…”

OpenEP: A Cross-Platform Electroanatomic Mapping Data Format and Analysis Platform for Electrophysiology Research

“…Such data is interpreted conventionally within electroanatomic mapping platforms through the creation of local activation time maps and their derivatives (Williams et al, 2018), voltage maps (Kistler et al, 2004;Pak et al, 2011;Al-Kaisey et al, 2020;Pappone et al, 2020), and maps representing electrogram morphological features during arrhythmia or pacing (Chang et al, 2013;Jadidi et al, 2016). Within research settings, the same data has also been extensively post-processed to analyze complex electrogram features (Almeida et al, 2020;Vraka et al, 2020), activation patterns (El Haddad et al, 2014), conduction velocities (Cantwell et al, 2015;Aronis et al, 2020) and identify phase singularities through multiple mapping techniques (Child et al, 2018;Ríos-Muñoz et al, 2018).…”