and it cause disagreement when interpreted by the research community [@Roney2017; @hansen2015; @cherry2008].

In this paper, we present a method whereby electrograms are extracted from a number of independent locations in the atria and these are cross-referenced to triangulate the position of a re-entrant circuit. By using multiple measurements, noise and local fluctuations are minimised and a prediction can be reached without being overly reliant on the imaging resolution at any one given location. The procedure applies machine learning methods to maximise the prediction accuracy of the algorithm. The work here should be considered a proof of concept and has been carried out without some of the detail that would be necessary in a realistic clinical implementation, but nevertheless, it presents a clear path towards a potential improvement in ablation success rates based on locating drivers from electrograms only.

The Christensen, Manani and Peters model (CMP) is a 2D cellular automata on a simplified architecture of the atria [@christensen2015]. While the model architecture is simplified, it preserves the key features of discrete cardiomyocyte activation at the microscopic level, while ensuring macroscopic conduction appears continuous. The CMP model has a particular focus on demonstrating the role of fibrosis in initiating and 