Sensitivity Analysis of ECG Features to Computational Model Input Parameters

Jenny Venton1, Karli Gillette2, Matthias Gsell3, Axel Loewe4, Claudia Nagel5, Benjamin Winkler6, Louise Wright1
1Data Science, National Physical Laboratory, 2Gottfried Schatz Research Center - Medical University of Graz, 3Medical University of Graz, 4Karlsruhe Institute of Technology (KIT), 5Institute of Biomedical Engineering - Karlsruhe Institute of Technology (KIT), 6Physikalisch-Technische Bundesanstalt (PTB)


Cardiac models of electrophysiology generating simulated electrocardiogram signals are an increasingly valuable tool for both personalised medicine and understanding cardiac pathologies. Sensitivity analysis (SA) can provide crucial insight into how simulation parameters affect ECG morphology.

Here we use two SA methods, direct numerical evaluation of integrals and polynomial chaos expansion to calculate Sobol coefficients for QRS complexes generated by a ventricular model. The importance of stimulation site parameters on output ECG features is evaluated.

SA methods can indicate parameter importance for different ECG morphology features, with physiologically sensible explanations, and different SA methods have strengths and weaknesses. Insight into parameter importance supports model development and can allow for more nuanced and patient-specific simulation changes. Conclusion: Sensitivity analysis provides valuable information about the relationship between simulated ECG morphology and cardiac model input parameters. This provides valuable insight on the quality of the simulated signals and can allow for more nuanced patient-specific simulation changes.