the statistical insights these models provide can offer significant improvements when analysing data. The computational complexity of continuous models make these unsuited to generating large quantities of data [@lin2017; @clayton2011].

The remainder of this paper is organised as follows. First, we briefly introduce the CMP cellular automata model used in this research. We describe the physiological motivation behind the model and outline the process of simulating electrograms. We discuss a novel visualisation of electrograms and use insights from this method to inform statistical analysis. Random Forests, a machine learning technique with consistently strong results across a number of domains, are described briefly in section \[sec:level1B\] [@Caruana2006; @caruana2008]. These are then implemented in a search algorithm for locating AF drivers. Our results are presented and discussed in section \[sec:level1C\]. Finally, we outline potential future work and discuss the CMP model’s relevance to current clinical research.

\[sec:level2B\] The Christensen–Manani–Peters model
===================================================

The Christensen–Manani–Peters (CMP) model is a 2D cellular automata. Each atrial muscle cell in the CMP model is represented by a single square in a larger square lattice of side length $L$. All cells are connected to their nearest neighbours in one dimension (the longitudinal direction) and in the orthogonal dimension 