High throughput electrophysiology data analysis using EP Workbench

Cardiac electrophysiology describes the study and treatment of heart rhythm disorders (arrhythmias). Data collected during routine treatment of arrhythmias describe, in detail, cardiac anatomy, motion, structure and function and could therefore be used to enhance prognostication for arrhythmia patients. Exposing these data for research has been a key objective of our efforts, delivered through the OpenEP project. EP Workbench, our graphical interface platform extends our prior work to large scale manipulation of data by providing point-and-click access to ARCHER2 compute resources, integration with a well-established open-source cardiac simulation engine (openCARP) and the ability to integrate custom plug-and-play algorithms into analysis pipelines. The ongoing work of our group is using these features of EP Workbench to study arrhythmia mechanisms and treatments, in three key areas. Firstly, we are using a digital twin approach to study heart rhythm disorder treatments including modelling the application of anti-arrhythmic drugs in patients with atrial fibrillation and studying ablation strategies in patients with atrial septal defects, a common form of congenital heart disease. Parameters to enable these experiments are configured through EP Workbench and simulations are run on ARCHER2 using the OpenCARP solver. Secondly, we have developed algorithms for quantifying cardiac electrical activation during atrial fibrillation (AF). The use of ARCHER2 in this setting will enable rapid turn-around for data analysis, critical for testing multiple treatment strategies on high fidelity 3D models of the heart. Finally, we are developing a new pipeline strategy for processing cardiac electrophysiology data at the population scale using EP Workbench, ARCHER2 and our established compact data storage format (OpenEP). Taken together, this work demonstrates the ability of large-scale data analysis using EP Workbench and ARCHER2, unlocking previously inaccessible insights from electrophysiology data which may lead to future innovations in arrhythmia patient care.