Patient specific simulation of body surface ECG using the finite element method.

Area of Science:

• Computational biology
• Biomedical engineering
• Cardiac electrophysiology

Background:

• Advances in computer science enable realistic heart electrophysiology simulations.
• Previous studies focused on cell-level excitation and repolarization models.

Purpose of the Study:

• To extend existing cardiac simulation techniques to personalized body surface electrocardiogram (ECG) prediction.
• To validate patient-specific heart models for clinical applications.

Main Methods:

• Created patient-specific finite element models of the heart and torso using clinical data (CT scans).
• Employed a parallel multi-grid method to solve the dynamic bi-domain equations.
• Personalized models by defining non-excitable tissue, incorporating failing myocyte models, and adjusting excitation sequences.

Main Results:

• Achieved reasonable agreement between simulated and actual ECGs for four patients with diverse heart conditions.
• Successfully reproduced ECG morphologies for three patients under bi-ventricular pacing without further model adjustments.
• Demonstrated the validity of the personalized heart models through accurate simulation of pacing-induced ECG changes.

Conclusions:

• The study enhances understanding of the cellular underpinnings of the body surface ECG.
• Personalized cardiac simulation holds promise for future clinical applications in cardiology.