Online

Benson, Bryce E.

College of Engineering and Mathematical Sciences. Bioengineering Graduate Program

2016

PhD

Atrial fibrillation (AF) is the most common cardiac arrhythmia that affects an estimated 33.5 million people worldwide. Despite its prevalence and economic burden, treatments remain relatively ineffective. Interventional treatments using catheter ablation have shown more success in cure rates than pharmacologic methods for AF. However, success rates diminish drastically in patients with more advanced forms of the disease. The focus of this research is to develop a mapping strategy to improve the success of ablation. To achieve this goal, I used a computational model of excitation in order to simulate atrial fibrillation and evaluate mapping strategies that could guide ablation. I first propose a substrate guided mapping strategy to allow patient-specific treatment rather than a one size fits all approach. Ablation guided by this method reduced AF episode durations compared to baseline durations and an equal amount of random ablation in computational simulations. Because the accuracy of electrogram mapping is dependent upon catheter-tissue contact, I then provide a method to identify the distance between the electrode recording sites and the tissue surface using only the electrogram signal. The algorithm was validated both in silico and in vivo. Finally, I develop a classification algorithm for the identification of activation patterns using simultaneous, multi-site electrode recordings to aid in the development of an appropriate ablation strategy during AF. These findings provide a framework for future mapping and ablation studies in humans and assist in the development of individualized ablation strategies for patients with higher disease burden.