Efficient Computational Modeling of Human Ventricular Activation and Its Electrocardiographic Representation: A...

Area of Science:

• Computational Biology
• Cardiovascular Physiology
• Medical Imaging

Background:

• Personalized cardiovascular medicine relies on patient-specific cardiac models.
• Accurate modeling of Purkinje-myocardial junctions (PMJs) is essential for simulating ventricular electrical activation.
• Non-invasive methods for localizing PMJs and understanding their ECG relationship are lacking.

Purpose of the Study:

• To investigate the sensitivity of the electrocardiogram (ECG) QRS complex to the anatomical location and regional number of PMJs.
• To inform parameterization strategies for patient-specific ventricular models.

Main Methods:

• Simulated ECG QRS complex using an image-based human torso and biventricular model.
• Modeled cardiac electrophysiology with Cardioid software.
• Represented PMJs as discrete current injection stimuli, varying their location and number.

Main Results:

• ECG QRS complex features were most sensitive to the presence/absence of four 'seed' stimuli.
• Adjusting locations of nearby 'regional' stimuli allowed finer tuning of the QRS complex.
• A minimal 12-stimuli configuration yielded physiological excitation, with QRS metrics and ventricular activation patterns validated.

Conclusions:

• PMJ location is a higher priority for parameterization in personalized ventricular models than PMJ number.
• These findings guide the development of patient-specific cardiac models using ECG data.