  Atrial fibrillation is a heart rhythm disorder that affects tens of millions
people worldwide. The most effective treatment is catheter ablation. This
involves irreversible heating of abnormal cardiac tissue facilitated by
electroanatomical mapping. However, it is difficult to consistently identify
the triggers and sources that may initiate or perpetuate atrial fibrillation
due to its chaotic behavior. We developed a patient-specific computational
heart model that can accurately reproduce the activation patterns to help in
localizing these triggers and sources. Our model has high spatial resolution,
with whole-atrium temporal synchronous activity, and has patient-specific
accurate electrophysiological activation patterns. A total of 15 patients data
were processed: 8 in sinus rhythm, 6 in atrial flutter and 1 in atrial
tachycardia. For resolution, the average simulation geometry voxel is a cube of
2.47 mm length. For synchrony, the model takes in about 1,500 local electrogram
recordings, optimally fits parameters to the individual's atrium geometry and
then generates whole-atrium activation patterns. For accuracy, the average
local activation time error is 5.47 ms for sinus rhythm, 10.97 ms for flutter
and tachycardia; and the average correlation is 0.95 for sinus rhythm, 0.81 for
flutter and tachycardia. This promising result demonstrates our model is an
effective building block in capturing more complex rhythms such as atrial
fibrillation to guide physicians for effective ablation therapy.
