Nonlinear measures characterize atrial fibrillatory dynamics generated using fractional diffusion

Computational simulations are used as tool to study atrial fibrillation and its maintaining mechanisms. Phase analysis has been used to elucidate the mechanisms by which a reentry is generated. However, clinical application of phase mapping requires a signal preprocessing stage that could affect the activation sequences. In this work we use the fractional diffusion equation to generate fibrillatory dynamics, including stable and meandering rotors, and multiple wavelets, by varying the order of the spatial fractional derivatives obtaining different complexity levels of propagation in a 2D domain. We applied nonlinear measures to characterize the propagation patterns from electrograms. Our results show that electroanatomical maps constructed using approximate entropy and multifractal analysis, are able to detect the tip of stable and meandering rotors, and to mark the occurrence of collisions and wave breaks. Application of these signal processing techniques to clinical practice is feasible and could improve atrial fibrillation ablation procedures.