Title: Efficient Computational Modeling of Human Ventricular Activation and Its Electrocardiographic Representation: A Sensitivity Study

Abstract

Patient-specific models of the ventricular myocardium, combined with the computational power to run rapid simulations, are approaching the level where they could be used for personalized cardiovascular medicine. A major remaining challenge is determining model parameters from available patient data, especially for models of the Purkinje-myocardial junctions (PMJs): the sites of initial ventricular electrical activation. There are no non-invasive methods for localizing PMJs in patients, and the relationship between the standard clinical ECG and PMJ model parameters is underexplored. Thus, this study aimed to determine the sensitivity of the QRS complex of the ECG to the anatomical location and regional number of PMJs. The QRS complex was simulated using an image-based human torso and biventricular model, and cardiac electrophysiology was simulated using Cardioid. The PMJs were modeled as discrete current injection stimuli, and the location and number of stimuli were varied within initial activation regions based on published experiments. Results indicate that the QRS complex features were most sensitive to the presence or absence of four “seed” stimuli, and adjusting locations of nearby “regional” stimuli provided finer tuning. Decreasing number of regional stimuli by an order of magnitude resulted in virtually no change in the QRS complex. Thus, a minimalmore » 12-stimuli configuration was identified that resulted in physiological excitation, defined by QRS complex feature metrics and ventricular excitation pattern. In conclusion, overall, the sensitivity results suggest that parameterizing PMJ location, rather than number, be given significantly higher priority in future studies creating personalized ventricular models from patient-derived ECGs.« less

Authors:

Cranford, Jonathan P. ^[1]; O’Hara, Thomas J. ^[1]; Villongco, Christopher T. ^[2]; Hafez, Omar M. ^[3]; Blake, Robert C. ^[1]; Loscalzo, Joseph ^[4]; Fattebert, Jean-Luc  ^[1];

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Richards, David F. ^[1]; Zhang, Xiaohua ^[1]; Glosli, James N. ^[1]; McCulloch, Andrew D. ^[2]; Krummen, David E. ^[5]; Lightstone, Felice C. ^[1]; Wong, Sergio E. ^[1]

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+ Show Author Affiliations

1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2. Univ. of California, San Diego, CA (United States)
3. Univ. of California, Davis, CA (United States)
4. Harvard Medical School, Boston, MA (United States); Brigham and Women’s Hospital, Boston, MA (United States)
5. Univ. of California, San Diego, CA (United States); VA San Diego Healthcare System, San Diego, CA (United States)

Publication Date:

Fri Mar 16 00:00:00 EDT 2018

Research Org.:

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE; National Institutes of Health (NIH)

OSTI Identifier:

1463971

Grant/Contract Number:  

AC05-00OR22725; AC52-07NA27344; P41 GM103545-18; HL105242; HL111197; HL61795; HG007690; HL122199; HL126273; GM107618; FG02-97ER25308; 5 T32 HL 7444-32

Resource Type:

Accepted Manuscript

Journal Name:

Cardiovascular Engineering and Technology

Additional Journal Information:

Journal Volume: 9; Journal Issue: 3; Journal ID: ISSN 1869-408X

Publisher:

Springer Nature

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Human ventricular excitation; Sensitivity analysis; Electrocardiogram; Patient-specific modeling; Computational electrophysiology; Bundle branch block