In silico Evaluation of Wearable Cardiac Defibrillator: Personalized Therapy Planning to Prevent Sudden Cardiac Death

In this paper, we propose a computational model to predict and optimize the defibrillation mechanism of Wearable Cardiac Defibrillator (WCD). The computational model is developed from high resolution torso cardiac MRI followed by biophysical simulation to assess the efficacy of defibrillation by det...

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Bibliographic Details
Published in2021 29th European Signal Processing Conference (EUSIPCO) pp. 1201 - 1205
Main Authors Mazumder, Oishee, Sinha, Aniruddha
Format Conference Proceeding
LanguageEnglish
Published EURASIP 23.08.2021
Subjects
Biological system modeling
Computational modeling
Defibrillator
Discrete Fourier transforms
Electrodes
Finite Element model
MRI
Myocardial damage
Myocardium
Torso
Wearable computers
Weighted Kullback Leibler divergence
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Summary:In this paper, we propose a computational model to predict and optimize the defibrillation mechanism of Wearable Cardiac Defibrillator (WCD). The computational model is developed from high resolution torso cardiac MRI followed by biophysical simulation to assess the efficacy of defibrillation by determining defibrillation thresholds (DFT) and extent of myocardial damage. A measure for quantifying such efficacy is proposed by calculating the divergence in the distribution of myocardial potential gradient obtained in silico, with respect to an ideal probabilistic distribution, defined for defibrillator success. Variations in defibrillation efficacy is simulated for using different shocking electrode configurations to assess the best defibrillator outcome with minimal myocardial damage. The developed model can be used for designing personalized WCD vests depending on subject specific anatomy and pathology.
ISSN:2076-1465
DOI:10.23919/EUSIPCO54536.2021.9616251