Early warning studies in an atrial model to prevent fibrillation

A cellular automata model is used to simulate an atrial tissue. We were able to obtain and study signals of the heart that resemble the electrocardiograms for different topological cases. Considering that the heart is a dynamic system in a critical state, we used new techniques known as early warnings, based in the statistical behavior of the signals. We found that it is possible to determine how healthy is an atrial tissue and how far it is from suffering an atrial fibrillation (AF) episode. Another analysis related with the memory of the system (Lag-1 and power spectrum analysis) was performed and we obtained that the atrial tissue goes through a phase transition from a healthy state to a deteriorated one. This can help us to understand the dynamics of the AF and possibly apply this to prevent them with non-invasive methods and with a high degree of confidence.