Investigating the Influence of Parameters on Electrogram Morphology Using Gaussian Process Emulators

Intracardiac electrograms (EGMs) are a valuable tool for assessing cardiac arrhythmias to identify tissue that serve as targets for radiofrequency catheter ablation. However, accurately interpreting EGMs is challenging due to the influence of various factors on the morphology of the signals. Computational modelling combined with global sensitivity analysis (GSA) can provide crucial insights into how these parameters affect the simulated EGM morphology. However, performing an accurate GSA necessitates a large number of simulations which are computationally expensive to perform. We propose a machine learning approach based on Gaussian process emulators (GPEs) of model simulations to enable rapid and robust inference of model parameters. We applied this framework to perform a computationally efficient GSA to quantify the total and first-order effects of input parameters across several features of the unipolar (uEGM) and bipolar (bEGM) EGMs. The trained GPEs demonstrated a high accuracy in predicting EGM features, achieving an average R² score and independent standard error (ISE) of 0.925 ± 0.041 and 89.7% ± 1.63%, respectively. The GSA identified fibrosis density and the electrode-wavefront angle as key parameters influencing uEGMs and bEGMs, respectively, which may lead to more informed EGM interpretation.