from electrograms in a model of atrial fibrillation'
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\[sec:level1A\]Introduction
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Atrial Fibrillation (AF) is the most common cardiac arrhythmia in clinical practice and is getting more prevalent in the general population due to the aging demographic. However, the mechanistic origin of AF is still poorly understood. As a result, the success rates of treatment options remain limited with future improvements requiring a better understanding of how AF emerges from the underlying properties of the myocardium.

A variety of possible mechanisms have been proposed to explain the origin of AF. These include circus movement re-entry, the leading circle theory, spiral wave re-entry (otherwise known as rotors) of which micro-anatomical re-entry can be thought of as a subset, and the multiple wavelet hypothesis [@nattel2002; @schotten2011]. However, there are many contradictory findings, and no one mechanism explains all observations [@nattel2017; @lee2015]. This suggests new techniques are needed both in clinical practice and research, with numerous researchers highlighting the potential of computational simulations and machine learning [@Trayanova2017; @clayton2011; @hood2004; @winslow2012; @deo2015; @li2014]. An issue of particular importance is that of limited mapping resolution when detecting the drivers of AF. Errors in the accuracy of imaging data limit the efficacy of treatment by ablation, 