Predicting Cardiothoracic Voltages During High Energy Shocks: Methodology and Comparison of Experimental to Finite Element Model Data

Dawn Blilie Jorgenson, Paul H. Schimpf, Irving Shen, George Johnson, Gust H. Bardy, David R. Haynor, Yongmin Kim

Research output: Contribution to journalArticle

46 Scopus citations

Abstract

Finite element modeling has been used as a method to investigate the voltage distribution within the thorax during high energy shocks. However, there have been few quantitative methods developed to assess how well the calculations derived from the models correspond to measured voltages. In this paper, we present a methodology for recording thoracic voltages and the results of comparisons of these voltages to those predicted by finite element models. We constructed detailed 3-D subject-specific thorax models of six pigs based on their individual CT images. The models were correlated with the results of experiments conducted on the animals to measure the voltage distribution in the thorax at 52 locations during synchronized high energy shocks. One transthoracic and two transvenous electrode configurations were used in the study. The measured voltage values were compared to the model predictions resulting in a correlation coefficient of 0.927 ±b 0.036 (average ± standard deviation) and a relative rms error of 22.13 ± 5.99%. The model predictions of voltage gradient within the myocardium were also examined revealing differences in the percent of the myocardium above a threshold value for various electrode configurations and variability between individual animals. This variability reinforces the potential benefit of patient-specific modeling.

Original languageEnglish (US)
Pages (from-to)559-571
Number of pages13
JournalIEEE Transactions on Biomedical Engineering
Volume42
Issue number6
DOIs
StatePublished - Jun 1995

ASJC Scopus subject areas

  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Predicting Cardiothoracic Voltages During High Energy Shocks: Methodology and Comparison of Experimental to Finite Element Model Data'. Together they form a unique fingerprint.

  • Cite this