Computational Modeling of Electrocardiogram Signals (2024)

Undergraduate: Preetam Tanikella

Faculty Advisor: Boyce Griffith
Department: Biomedical Engineering, Mathematics

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Novel cardiac electrophysiology models are being developed to simulate the complex electrical systems of the heart. These models combine experimental data and mathematical algorithms to help researchers investigate normal cardiac rhythms, identify abnormalities, and create new therapeutic interventions for cardiac disorders. The primary clinical tool used to understand the heart's electrical activity is the electrocardiogram (ECG), a cornerstone of clinical diagnostics. By calculating the electrocardiogram (ECG) traces for these cardiac electrophysiology models and comparing them to clinical data, we can improve the validity of these models and further our understanding of the relationship between heart function and ECG patterns used in clinical practice. Here, we utilize the extracellular potential recovery method and leverage the finite element method to compute ECG signals within a slab of myocardial tissue. We use the Ten Tusscher-Panfilov 2006 ionic model to represent the cell-membrane dynamics within the myocardial tissue and the monodomain model to compute the transmembrane potentials. By following the established stimulation protocol and tissue setup outlined in prior literature, we successfully replicate the shape of the ECG traces, thus validating our approach.