Computational tools for analysis of repetitive atrial activation patterns during atrial fibrillation

This PhD thesis investigated atrial fibrillation (AF), the most common cardiac rhythm disorder, using advanced data analysis methods. AF is caused by irregular electrical activity in the upper chambers of the heart. Catheter ablation is a well-established and effective treatment in which a catheter is used to inactivate the tissue responsible for this abnormal activity; however, many patients do not experience long-term benefit. A key reason is that AF mechanisms differ between individuals, and some patients require more personalized ablation strategies. The computational tools developed in this thesis are designed to address this challenge. Data from patients, animal experiments, and computer simulations were systematically analyzed to generate large-scale insights, enabling the identification and visualization of patient-specific ablation targets. These data-driven frameworks provide clinicians with a clearer and more comprehensive view of the underlying electrical mechanisms of AF. Furthermore, artificial intelligence–based approaches have been developed to predict treatment response at an early stage of the disease, enabling more efficient patient prioritization and treatment planning.