  Cardiac fibrillation is characterized by chaotic and disintegrated spiral
wave dynamics patterns, whereas sinus rhythm shows synchronized excitation
patterns. To determine functional correlations among cardiomyocytes during
complex fibrillation states, we applied a pairwise maximum entropy model (MEM)
to the 2D numerical simulation data of human atrial fibrillation. We then
constructed an energy landscape and estimated a hierarchical structure among
the different local minima (attractors) to explain the dynamic properties of
cardiac fibrillation. The MEM could describe the wave dynamics of sinus rhythm,
single stable rotor, and single rotor with wavebreak (both accuracy and
reliability>0.9), but not the multiple random wavelet case. The energy
landscapes exhibited unique profiles of local minima and energy barriers,
characterizing the spatiotemporal patterns of cardiac fibrillation dynamics.
The pairwise MEM-based energy landscape analysis provides reliable explanations
of complex nonlinear dynamics of cardiac fibrillation, which might be
determined by the presence of a 'driver' such as a sinus node or rotor.
