I'm David Vidmar

During a normal human heartbeat, an electrical wave of excitation spreads out evenly across cardiac tissue. This electrical signal causes contraction of the heart muscle, resulting in the pumping of blood throughout the body. If this electrical wave becomes mangled, the resulting contraction can become substantially altered, leading to an arrhythmia. The most serious type of arrhythmia, known as fibrillation, arises when the heart's contraction becomes entirely uncoordinated and ineffective, with its motion reduced to chaotic quivering. My PhD research focused on understanding the complex dynamical state underlying human fibrillation by exploring the relationship between theoretical principles, computational simulations, and clinical data.

Images by wikipedia user JHeuser, distributed under CC BY-SA 3.0

Though fibrillation in humans can exist indefinitely, it is most commonly seen in episodes which last from minutes to days, eventually terminating back to normal sinus rhythm. An obvious question arises: what is the mechanism allowing for the chaos inherent to fibrillation to persist for extended periods of time? Put another way, what exactly is perpuating this arrhythmia and how can we stop it?

Recent findings point to the importance of consistent rotational patterns of conduction as playing an important role in the maintanence of this arrhythmia. These rotational patterns are grounded in extensive theoretical and computational studies of the heart and are hypothesized to act as the drivers of disorganization. A sample simulation of this hypothesis is shown to the right, where a central rotational pattern exists with wavefronts breaking up far from the core.

Simulation of rotational pattern surrounded by breakup, red is excited tissue and blue is resting tissue.

To better understand the role that various conduction patterns play in human fibrillation, it becomes necessary to collect and analyze clinical data from patients. This is achieved through use of a basket catheter, which records electrical signals at various points on the surface of the heart during an episode of arrhythmia. These signals are then processed and used to create patient-specific videos of the underyling electrical activity.

One focus of my work was to design and implement a methodology to make the interpretation of these complex activation videos easier for physicians and scientists, as well as providing quantiative measures of any conduction patterns present. To do so, we designed a novel algorithm which accurately identifies and visualizes the vector flow field of electrical activity in these videos. This is outlined in our publiction "Determing Conduction Patterns on a Sparse Electrode Grid" (see below) as well as in our patent application WO2017165846A1. With this tool we can conduct systematic studies on the dynamical evolution of arrhythmia episodes.

Activation video during clinical fibrillation, with the computed flow field in green and activation in white. Reproduced from Vidmar et al (2016), Physical Review E. Copyright APS.