recording locations. These results support the possibility of using statistical learning techniques for locating AF drivers on heart tissue using indirect feature measurements, derived from the accessible electrograms.\

\[sec:level1D\] Discussion
==========================

It is clear from the results in section \[sec:level1C\] that a statistical analysis of electrograms can be used to extract information as to the location of re-entrant drivers in the CMP model, an idealised mathematical model of AF focusing on the discretised structure of the atrial myocardium and the electrophysiological action of fibrosis. In recent work, we have reviewed issues concerning the limited resolution of mapping technologies used during ablation, and have found that these limitations make it hard to identify distinct mechanisms of AF in clinical practice [@Roney2017]. If re-entrant circuits are to be ablated, terminating AF, these issues must be addressed. However, the methods proposed here may mitigate some of the mapping resolution issues that arise from single localised measurements by focusing on optimising a single prediction of the driver location based on a statistical analysis of multiple lower resolution measurements. Such an approach maximises accuracy by minimising errors due to local noise.

\[sec:lims\]Future Work
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In its current form, our driver mapping approach is not refined 