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

• Published in: 2021 29th European Signal Processing Conference (EUSIPCO) pp. 1201 - 1205
• Main Authors: Mazumder, Oishee, Sinha, Aniruddha
• Format: Conference Proceeding
• Language: English
• 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
• ISSN: 2076-1465
• DOI: 10.23919/EUSIPCO54536.2021.9616251

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.