time steps where the unit of time is $\Delta t^{*}$ such that the action potential of a cardiomyocyte is $150 \text{ms}$ in accordance with the values seen for human atrial cardiomyocytes. In sinus rhythm the CMP model therefore beats approximately once every $660 \text{ms}$. Note that AF can easily be induced with a different set of parameters, but these are chosen as a realistic reflection of atrial activity. In the original CMP model, the fraction of dysfunctional cells is $\delta = 0.05$. This is an arbitrary choice which allows AF to spontaneously emerge. In this work we set $\delta = 0$ and instead artificially insert re-entrant circuits into the tissue. This is a reasonable concession for computational ease since this work is not concerned with the spontaneous emergence of AF but rather the diagnosis of AF after it has emerged.

\[sec:level1B\]Methods
======================

To locate AF drivers on the CMP tissue, machine learning models are utilised. These models are trained with electrogram feature data gathered from a large number of simulations of varying AF instances on CMP tissue. A benefit of this approach is that instead of relying on a single accurate measurement to locate a re-entrant circuit, a collection can 