  Atrial fibrillation (AF) is the most common cardiac arrhytmia, characterised
by the chaotic motion of electrical wavefronts in the atria. In clinical
practice, AF is classified under two primary categories: paroxysmal AF, short
intermittent episodes separated by periods of normal electrical activity, and
persistent AF, longer uninterrupted episodes of chaotic electrical activity.
However, the precise reasons why AF in a given patient is paroxysmal or
persistent is poorly understood. Recently, we have introduced the percolation
based Christensen-Manani-Peters (CMP) model of AF which naturally exhibits both
paroxysmal and persistent AF, but precisely how these differences emerge in the
model is unclear. In this paper, we dissect the CMP model to identify the cause
of these different AF classifications. Starting from a mean-field model where
we describe AF as a simple birth-death process, we add layers of complexity to
the model and show that persistent AF arises from re-entrant circuits which
exhibit an asymmetry in their probability of activation relative to
deactivation. As a result, different simulations generated at identical model
parameters can exhibit fibrillatory episodes spanning several orders of
magnitude from a few seconds to months. These findings demonstrate that
diverse, complex fibrillatory dynamics can emerge from very simple dynamics in
models of AF.
