Multi-scale Cardiac Modeling for ECG Forward Problem Based on Finite Element Method

The forward problem of electrocardiography is essential for understanding the mechanisms underlying cardiac electrical activity, validating and optimizing the inverse problem, and advancing personalized medicine through patient-specific modeling. Comprehensive computation of the forward problem requires multi-scale modeling and the execution of complex computational processes. The challenges associated with constructing, solving, and validating multi-scale models arise from the need to accurately represent biophysical processes by establishing precise interactions across various spatial and temporal scales. In this study, we aimed to integrate single-cell modeling, which captures the electrophysiological activity of cardiac cells, with finite element methods (FEM), which simulates the propagation of electrical signals from cardiac tissue to the body surface, to achieve a comprehensive simulation of the electrocardiogram (ECG) forward problem. We developed a multi-scale tissue structure model, incorporating it with single-cell models and FEM to compute the distribution of body surface potentials. Additionally, we simulated a 12-lead ECG by appropriately positioning electrodes. The results demonstrate that our proposed approach effectively simulates the QRS and T-wave components of the ECG and accurately captures variations in sub-cellular parameters under both normal and abnormal conditions as reflected in the ECG.