2016/12/11 by Yen Ting Lin, Eugene T. Y. Chang, Lin, Yen Ting +8
Medicine · Neuroscience · #ECG Monitoring and Analysis #FOS: Biological sciences #Functional Brain Connectivity Studies #Neural dynamics and brain function #Quantitative Methods (q-bio.QM) #Tissues and Organs (q-bio.TO)
paper · pdf · doi:10.48550/arxiv.1612.03403
openalex publication_date 2016/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Mathematical models of cardiac electrical excitation are increasingly\ncomplex, with multiscale models seeking to represent and bridge physiological\nbehaviours across temporal and spatial scales. The increasing complexity of\nthese models makes it computationally expensive to both evaluate long term (>60\nseconds) behaviour and determine sensitivity of model outputs to inputs. This\nis particularly relevant in models of atrial fibrillation (AF), where\nindividual episodes last from seconds to days, and inter-episode waiting times\ncan be minutes to months. Potential mechanisms of transition between sinus\nrhythm and AF have been identified but are not well understood, and it is\ndifficult to simulate AF for long periods of time using state-of-the-art\nmodels. In this study, we implemented a Moe-type cellular automaton on a novel,\ntopologically correct surface geometry of the left atrium. We used the model to\nsimulate stochastic initiation and spontaneous termination of AF, arising from\nbursts of spontaneous activation near pulmonary veins. The simplified\nrepresentation of atrial electrical activity reduced computational cost, and so\npermitted us to investigate AF mechanisms in a probabilistic setting. We\ncomputed large numbers (~105) of sample paths of the model, to infer\nstochastic initiation and termination rates of AF episodes using different\nmodel parameters. By generating statistical distributions of model outputs, we\ndemonstrated how to propagate uncertainties of inputs within our microscopic\nlevel model up to a macroscopic level. Lastly, we investigated spontaneous\ntermination in the model and found a complex dependence on its past AF\ntrajectory, the mechanism of which merits future investigation.\n