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Updated: Nov 8, 2025
High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
Published on: July 29, 2011
Directed graph mapping exceeds phase mapping in discriminating true and false rotors detected with a basket catheter
Enid Van Nieuwenhuyse1, Laura Martinez-Mateu2, Javier Saiz3
1Department of Physics and Astronomy, Ghent University, Ghent, Belgium.
Insights
Directed Graph Mapping (DGM) shows promise in identifying atrial fibrillation (AF) sources. This new tool, compared to phase mapping (PM), can better distinguish true rotors from false ones in computer models, improving AF diagnosis.
Area of Science:
- Computational electrophysiology
- Cardiac arrhythmia analysis
- Medical device technology
Background:
- Atrial fibrillation (AF) is a common arrhythmia, but identifying its sources from clinical recordings is challenging.
- Current methods like phase mapping (PM) struggle with accuracy due to limitations in clinical data resolution and algorithm verification.
- Computer modeling offers a viable approach to simulate and verify arrhythmia source detection algorithms.
Purpose of the Study:
- To compare the efficacy of Directed Graph Mapping (DGM) against phase mapping (PM) for identifying arrhythmia sources.
- To evaluate DGM's performance using a simulated meandering rotor dataset from previous studies.
- To assess DGM's ability to distinguish true arrhythmia sources from false positives generated by PM.
Main Methods:
- Simulated a meandering rotor in the right atrium.
- Utilized a basket catheter dataset recorded at Superior Vena Cava (SVC), Crista Terminalis (CT), and Coronary Sinus (CS) positions.
- Applied Directed Graph Mapping (DGM) with varying conduction velocities (CVmin) and compared results with Phase Mapping (PM).
Main Results:
- DGM successfully distinguished true rotors from false rotors at SVC and CT positions, particularly with adjusted CVmin.
- At SVC, DGM showed high true rotor detection (82%) at CVmin=0.01cmms, while significantly reducing false rotors compared to PM.
- Increasing CVmin to 0.02cmms eliminated false rotors but reduced true rotor detection; DGM's performance was poor at the CS position.
Conclusions:
- Directed Graph Mapping (DGM) offers an advantage over Phase Mapping (PM) by effectively reducing false positives.
- Adjusting the CVmin parameter in DGM allows for better discrimination between true and false arrhythmia sources.
- DGM shows potential for improving the accuracy of identifying atrial fibrillation sources, overcoming limitations of current methods.
Abstract:
Atrial fibrillation (AF) is the most frequently encountered arrhythmia in clinical practise. One of the major problems in the management of AF is the difficulty in identifying the arrhythmia sources from clinical recordings. That difficulty occurs because it is currently impossible to verify algorithms which determine these sources in clinical data, as high resolution true excitation patterns cannot be recorded in patients. Therefore, alternative approaches, like computer modelling are of great interest. In a recent published study such an approach was applied for the verification of one of the most commonly used algorithms, phase mapping (PM). A meandering rotor was simulated in the right atrium and a basket catheter was placed at 3 different locations: at the Superior Vena Cava (SVC), the Crista Terminalis (CT) and at the Coronary Sinus (CS). It was shown that although PM can identify the true source, it also finds several false sources due to the far-field effects and interpolation errors in all three positions. In addition, the detection efficiency strongly depended on the basket location. Recently, a novel tool was developed to analyse any arrhythmia called Directed Graph Mapping (DGM). DGM is based on network theory and creates a directed graph of the excitation pattern, from which the location and the source of the arrhythmia can be detected. Therefore, the objective of the current study was to compare the efficiency of DGM with PM on the basket dataset of this meandering rotor. The DGM-tool was applied for a wide variety of conduction velocities (minimal and maximal), which are input parameters of DGM. Overall we found that DGM was able to distinguish between the true rotor and false rotors for both the SVC and CT basket positions. For example, for the SVC position with a CVmin=0.01cmms, DGM detected the true core with a prevalence of 82% versus 94% for PM. Three false rotors where detected for 39.16% (DGM) versus 100% (PM); 22.64% (DGM) versus 100% (PM); and 0% (DGM) versus 57% (PM). Increasing CVmin to 0.02cmms had a stronger effect on the false rotors than on the true rotor. This led to a detection rate of 56.6% for the true rotor, while all the other false rotors disappeared. A similar trend was observed for the CT position. For the CS position, DGM already had a low performance for the true rotor for CVmin=0.01cmms (14.7%). For CVmin=0.02cmms the false and the true rotors could therefore not be distinguished. We can conclude that DGM can overcome some of the limitations of PM by varying one of its input parameters (CVmin). The true rotor is less dependent on this parameter than the false rotors, which disappear at a CVmin=0.02cmms. In order to increase to detection rate of the true rotor, one can decrease CVmin and discard the new rotors which also appear at lower values of CVmin.
