Forests model has one of the highest average performances of any machine learning method across a wide range of different problems. Preliminary testing on our data showed considerably higher success rates for Random Forests than for other simple machine learning algorithms. The mathematical background for Random Forests is described in appendix \[sec:ap3\].

The training data for the Random Forests was gathered from 5000 randomly generated CMP tissues with one randomly placed AF circuit. The fraction of transverse connections was chosen to be $\nu = 0.2$ as this is the critical point where instances of paroxysmal AF emerge in the CMP model [@christensen2015]. Each tissue had 64 multiprobe electrodes uniformly placed giving in total of 2,880,000 electrogram recordings in total.

\[sec:level2E\] Algorithm for locating re-entrant circuits
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The goal of our algorithm is to demonstrate a proof of concept where re-entrant circuits driving AF can be located using solely electrogram information. The aim is not to create a perfect model which could be directly transfered to more complicated scenarios, but rather, the aim is to show the feasibility of these methods for electrical mapping in a system with large local fluctuations. As part of this approach, a small number of simplifications 