Doctoral School Mathematics and Scientific Computing

Abstract: Image-based models of human cardiac electrophysiology (EP) are increasingly relevant as a clinical research tool. However, current clinical EP models typically lack patient-specificity as they mostly rely on generic data, or are simplified to fit within clinical time scales due to a lack of computational efficiency. This work therefore aimed to develop an efficient, clinically-compatible automated workflow for patient-specific parameterization of human cardiac EP models using non-invasive standard ECG recordings. 
This talk presents an initial left-ventricular model used to demonstrate the feasibility of generating patient-specific activation sequences based on measured QRS complexes in non-invasive ECG recordings. The left-ventricular model was then used to further facilitate data-driven clinical EP model parameterization with increasing complexity. [hide abstract]