Aims: To track cardiac wavefronts generated by ectopic stimuli using Bayesian filtering (particle filters).
Methods: A high dimensional tracking system based on the FithzHugh-Nagumo (FH-N) equations emulating the biological excitation and propagation dynamics of the action potential across cardiac cells is proposed. The modified FH-N model tracks the electric cardiac wavefronts on a tissue, emulating an approximated atrial fibrillation scenario.
Bayesian tracking is achieved with two particle filter (PF) schemes: a sequential Auxiliary PF (APF) and a parallelized method, Independent APF (IAPF).
Results: The numerical results of the two examples, involving both estimation errors and running times, provide numerical evidence that support the theoretical findings.
Conclusions: We were able to track random ectopic activity in a interconnected network resembling atrial tissue. We used Bayesian filtering algorithms and took advantage of new parallelized methods for speeding up computation times.