Directed Graph Mapping exceeds Phase Mapping for the detection of simulated 2D meandering rotors in fibrotic tissue with added noise.

Cardiac arrhythmias such as atrial fibrillation (AF) are recognised to be associated with re-entry or rotors. A rotor is a wave of excitation in the cardiac tissue that wraps around its refractory tail, causing faster-than-normal periodic excitation. The detection of rotor centres is of crucial importance in guiding ablation strategies for the treatment of arrhythmia. The most popular technique for detecting rotor centres is Phase Mapping (PM), which detects phase singularities derived from the phase of a signal. This method has been proven to be prone to errors, especially in regimes of fibrotic tissue and temporal noise. Recently, a novel technique called Directed Graph Mapping (DGM) was developed to detect rotational activity such as rotors by creating a network of excitation. This research aims to compare the performance of advanced PM techniques versus DGM for the detection of rotors using 64 simulated 2D meandering rotors in the presence of various levels of fibrotic tissue and temporal noise. Four strategies were employed to compare the performances of PM and DGM. These included a visual analysis, a comparison of F2-scores and distance distributions, and calculating p-values using the mid-p McNemar test. Results indicate that in the case of low meandering, fibrosis and noise, PM and DGM yield excellent results and are comparable. However, in the case of high meandering, fibrosis and noise, PM is undeniably prone to errors, mainly in the form of an excess of false positives, resulting in low precision. In contrast, DGM is more robust against these factors as F2-scores remain high, yielding F2≥0.931 as opposed to the best PM F2≥0.635 across all 64 simulations.

Directed graph mapping shows rotors maintain non-terminating and focal sources maintain self-terminating Torsade de Pointes in canine model.

Torsade de Pointes is a polymorphic ventricular tachycardia which is as yet incompletely understood. While the onset of a TdP episode is generally accepted to be caused by triggered activity, the mechanisms for the perpetuation is still under debate. In this study, we analysed data from 54 TdP episodes divided over 5 dogs (4 female, 1 male) with chronic atrioventricular block. Previous research on this dataset showed both reentry and triggered activity to perpetuate the arrhythmia. 13 of those TdP episodes showed reentry as part of the driving mechanism of perpetuating the episode. The remaining 41 episodes were purely ectopic. Reentry was the main mechanism in long-lasting episodes (>14 beats), while focal sources were responsible for maintaining shorter episodes. Building on these results, we re-analysed the data using directed graph mapping This program uses principles from network theory and a combination of positional data and local activation times to identify reentry loops and focal sources within the data. The results of this study are twofold. First, concerning reentry loops, we found that on average non-terminating (NT) episodes (≥10 s) show significantly more simultaneous reentry loops than self-terminating (ST) TdP (<10 s). Non-terminating episodes have on average 2.72 ± 1.48 simultaneous loops, compared to an average of 1.33 ± 0.66 for self-terminating episodes. In addition, each NT episode showed a presence of (bi-)ventricular loops between 10.10% and 69.62% of their total reentry duration. Compared to the ST episodes, only 1 in 4 episodes (25%) showed (bi-)ventricular reentry, lasting only 7.12% of its total reentry duration. This suggests that while focal beats trigger TdP, macro-reentry and multiple simultaneous localized reentries are the major drivers of long-lasting episodes. Second, using heatmaps, we found focal sources to occur in preferred locations, instead of being distributed randomly. This may have implications on treatment if such focal origins can be disabled reliably.

Directed Graph Mapping for Ventricular Tachycardia: A Comparison to Established Mapping Techniques.

BACKGROUND: Understanding underlying mechanism(s) and identifying critical circuit components are fundamental to successful ventricular tachycardia (VT) ablation. Directed graph mapping (DGM) offers a novel technique to identify the mechanism and critical components of a VT circuit. OBJECTIVES: This study sought to evaluate the accuracy of DGM in VT ablation compared with traditional mapping techniques and a commercially available automated conduction velocity mapping (ACVM) tool. METHODS: Patients with structural heart disease who had undergone a VT ablation with entrainment-proven critical isthmus and a high-density electroanatomical activation map were included. Traditional mapping (TM) consisted of a combination of local activation time and entrainment mapping and was considered the gold standard for determining the VT mechanism, circuit, and isthmus location. The same local activation time values were then processed using DGM and a commercially available ACVM (Coherent Mapping, Biosense Webster) tool. The aim of this study was to compare TM vs DGM and ACVM in their ability to identify the VT mechanism, characterize the VT circuit, and locate the critical isthmus. RESULTS: Thirty-five cases were identified. TM classified the VT mechanism as focal in 7 patients and re-entrant in 28 patients. TM classified 11 VTs as single-loop re-entry, 15 as dual-loop re-entry, 1 as complex, and 1 case was indeterminant. The overall agreement between DGM and TM for determining VT mechanism and circuit type was strong (kappa value = 0.79; P < 0.01), as was the agreement between ACVM and TM (kappa value = 0.66; P < 0.01). Both DGM and ACVM identified the putative VT isthmus in 25 (89%) of the re-entrant cases. Focal activation was correctly identified by both techniques in all cases. CONCLUSIONS: DGM is a rapid automated algorithm that has a strong level of agreement with TM for manually re-annotated VT maps.

DG-Mapping: a novel software package for the analysis of any type of reentry and focal activation of simulated, experimental or clinical data of cardiac arrhythmia.

In this work, we present the release of a novel easy to use software package called DGM or Directed-Graph-Mapping. DGM can automatically analyze any type of arrhythmia to find reentry or focal sources if the measurements are synchronized in time. Currently, DGM requires the local activation times (LAT) and the spatial coordinates of the measured electrodes. However, there is no requirement for any spatial organization of the electrodes, allowing to analyze clinical, experimental or computational data. DGM creates directed networks of the activation, which are analyzed with fast algorithms to search for reentry (cycles in the network) and focal sources (nodes with outgoing arrows). DGM has been mainly optimized to analyze atrial tachycardia, but we also discuss other applications of DGM demonstrating its wide applicability. The goal is to release a free software package which can allow researchers to save time in the analysis of cardiac data. An academic license is attached to the software, allowing only non-commercial use of the software. All updates of the software, user and installation guide will be published on a dedicated website www.dgmapping.com . Graphical Abstract Direct-Graph-Mapping is a method to automatically analyze a given arrhythmia by converting measured data of the electrodes in a directed network. DGM requires the local activation times (LAT) and the spatial coordinates of the measured electrodes. There is no requirement for any spatial organization of the electrodes, allowing to analyze clinical, experimental or computational data (see left). An example could be the LATs and coordinates from a CARTO file. DGM creates a directed network of the activation by (1) determining the neighbors of each node, 2 (2) allowing a directed arrow between two neighbors if propagation of the electrical wave is possible, (3) repeating this process for all nodes, (4) if necessary, redistributing the nodes more uniformly and repeating step (1)-(3). Two possible steps are (5) to visualize the wavefront by creating an average graph or (6) find the cycles in the network which represent the reentry loops. Focal sources are nodes with only outgoing arrows.