Directed networks as a novel way to describe and analyze cardiac excitation: Directed Graph mapping

Networks provide a powerful methodology for a variety of biological, technological and social systems, such as analysis of brain data, social networks, internet search machines, etc. Interestingly, directed networks were not yet applied to characterize the excitation of the human heart, although this system would perfect fit for this type of analysis. This is because in clinics, cardiac activation is recorded by multiple discrete electrodes. Their successive excitation in normal sinus rhythm or during cardiac arrhythmia connects neighboring electrodes, and this naturally results in a directed network. In this work, we propose a new concept: application of directed networks to the heart to describe and characterize cardiac arrhythmia. We show that certain tools used in network theory allow us to determine the mechanism and location of cardiac arrhythmia and that the robustness of this approach could exceed the existing state-of-the-art methodologies used in clinics. We demonstrate the proof-of-principle on in-silico and clinical data. Implementing our suggested techniques in the clinic has the potential to improve the accuracy and speed of analyzing cardiac arrhythmia and to provide novel insight in arrhythmia which are still not fully understood.