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Atrial fibrillation (AF) is a leading cause of morbidity and mortality. AFprevalence increases with age, which is attributed to pathophysiologicalchanges that aid AF initiation and perpetuation. Current state-of-the-artmodels are only capable of simulating short periods of atrial activity at highspatial resolution, whilst the majority of clinical recordings are based oninfrequent temporal datasets of limited spatial resolution. Being able toestimate disease progression informed by both modelling and clinical data wouldbe of significant interest. In addition an analysis of the temporaldistribution of recorded fibrillation episodes AF density can provide insightsinto recurrence patterns. We present an initial analysis of the AF densitymeasure using a simplified idealised stochastic model of a binary time seriesrepresenting AF episodes. The future aim of this work is to develop robustclinical measures of progression which will be tested on models that generatelong-term synthetic data. These measures would then be of clinical interest indeciding treatment strategies.

Assessing Measures of Atrial Fibrillation Clustering via Stochastic Models of Episode Recurrence and Disease Progression