H. Welle, C. Nagel, A. Loewe, R. Mikut, and O. Dössel. Classification of Bundle Branch Blocks with QRS Templates Extracted from 12-lead ECGs. In Current Directions in Biomedical Engineering, vol. 7(2) , pp. 582-585, 2021
Student Theses (1)
H. Welle. Parameter Optimisation for Eikonal Simulations of Ventricular Activation Based on 12-lead Electrocardiograms from a Clinical Cohort. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Masterarbeit. 2022
Abstract:
In silico studies of ventricular electrophysiology bear the potential to produce a vast amount of synthetic electrocardiograms (ECGs) required for big data and machine learning approaches to identify cardiovascular pathologies. For large-scale simulations of ventricular ECGs, simulation parameters applicable to a variety of anatomical models need to be found such that the synthetic data resemble clinically observed recordings with high fidelity.In the thesis at hand, parameters for simulating ventricular activation by solving the Eikonal equation were optimised. For this purpose, 11,705 clinical 12-lead ECGs were filtered, annotated and cut, to generate representative QRS complex templates for each lead and observation. Those were then aligned, keeping inter-lead signal shifts, before reducing the dataset to its principal components (PCs). The suitability of the PCs to represent signal char- acteristics was validated through a classification distinguishing the healthy and pathological subjects of the dataset.Subsequently, ventricular activation parameters for a simplified representation of the His- Purkinje network were optimised. Those parameters include conduction velocities of the subendocardium and the myocardium, anisotropy ratio of the myocardial fibres as well as number, location, size and activation delay of initially activated regions (IARs) on the suben- docardium. The optimisation was conducted on the mean shape geometry of a ventricular shape model employing ventricular coordinates to define IARs geometry independent. ECGs were derived from the body surface potentials using the boundary-element method (BEM). The optimised objective function was based on the L2 error norm between the original signal and the reconstructed signal after projection to 20 PCs of the healthy clinical cohort. An evolutionary algorithm (EA) and a Bayesian optimisation algorithm were developed to opti- mise the parameters. Robustness was assessed by investigating the influence of ventricular geometry variations....