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Updated: Apr 21, 2026
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
Published on: January 8, 2013
Patient-specific modelling of cardiac electrophysiology in heart-failure patients
Mark Potse1, Dorian Krause2, Wilco Kroon2
1Center for Computational Medicine in Cardiology, Faculty of Informatics, Università della Svizzera italiana, Via Giuseppe Buffi 13, 6904 Lugano, Switzerland Inria Bordeaux Sud-Ouest, 33405 Talence CEDEX, France mark@potse.nl.
Insights
Numerical simulations using patient-specific heart models can reveal causes of left bundle-branch block (LBBB) in heart failure patients. This approach aids in understanding individual disease characteristics for better patient care.
Area of Science:
- Cardiovascular Electrophysiology
- Computational Biology
- Medical Simulation
Background:
- Left-ventricular (LV) conduction disturbances, particularly left bundle-branch block (LBBB), are prevalent in heart failure.
- The exact mechanisms underlying LBBB in heart failure patients are not fully understood and likely vary individually.
Observation:
- This study utilized patient-specific anatomical models and reaction-diffusion equations to simulate ventricular electrical activity.
- Electrocardiograms (ECGs) and cardiac electrograms were computed by solving the bidomain equation to analyze electrical signal propagation.
Findings:
- Simulations successfully reproduced measured ECG morphology and LV endocardial activation order in heart failure patients with LBBB.
- Model parameters were tuned to match simulated and measured signals, though discrepancies in S-wave depth were noted.
Implications:
- Developing patient-tailored computational models is feasible for reproducing cardiac electrical signals.
- This method offers a novel way to gain insights into individual disease characteristics of LBBB in heart failure that are not accessible through conventional methods.
Aims:
Left-ventricular (LV) conduction disturbances are common in heart-failure patients and a left bundle-branch block (LBBB) electrocardiogram (ECG) type is often seen. The precise cause of this pattern is uncertain and is probably variable between patients, ranging from proximal interruption of the left bundle branch to diffuse distal conduction disease in the working myocardium. Using realistic numerical simulation methods and patient-tailored model anatomies, we investigated different hypotheses to explain the observed activation order on the LV endocardium, electrogram morphologies, and ECG features in two patients with heart failure and LBBB ECG.
Methods And Results:
Ventricular electrical activity was simulated using reaction-diffusion models with patient-specific anatomies. From the simulated action potentials, ECGs and cardiac electrograms were computed by solving the bidomain equation. Model parameters such as earliest activation sites, tissue conductivity, and densities of ionic currents were tuned to reproduce the measured signals. Electrocardiogram morphology and activation order could be matched simultaneously. Local electrograms matched well at some sites, but overall the measured waveforms had deeper S-waves than the simulated waveforms.
Conclusion:
Tuning a reaction-diffusion model of the human heart to reproduce measured ECGs and electrograms is feasible and may provide insights in individual disease characteristics that cannot be obtained by other means.
