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Cardiac Electrical Modeling for Closed-Loop Validation of Implantable Devices

Authors :
Avinash Malik
Mark L. Trew
Partha S. Roop
Nitish Patel
Weiwei Ai
Source :
IEEE Transactions on Biomedical Engineering. 67:536-544
Publication Year :
2020
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2020.

Abstract

Objective: Evaluating and testing cardiac electrical devices in a closed-physiologic-loop can help design safety, but this is rarely practical or comprehensive. Furthermore, in silico closed-loop testing with biophysical computer models cannot meet the requirements of time-critical cardiac device systems, while simplified models meeting time-critical requirements may not have the necessary dynamic features. We propose a new high-level (abstracted) physiologically-based computational heart model that is time-critical and dynamic. Methods: The model comprises cardiac regional cellular-electrophysiology types connected by a path model along a conduction network. The regional electrophysiology and paths are modeled with hybrid automata that capture non-linear dynamics, such as action potential and conduction velocity restitution and overdrive suppression. The hierarchy of pacemaker functions is incorporated to generate sinus rhythms, while abnormal automaticity can be introduced to form a variety of arrhythmias such as escape ectopic rhythms. Model parameters are calibrated using experimental data and prior model simulations. Conclusion: Regional electrophysiology and paths in the model match human action potentials, dynamic behavior, and cardiac activation sequences. Connected in closed loop with a pacing device in DDD mode, the model generates complex arrhythmia such as atrioventricular nodal reentry tachycardia. Such device-induced outcomes have been observed clinically and we can establish the key physiological features of the heart model that influence the device operation. Significance: These findings demonstrate how an abstract heart model can be used for device validation and to design personalized treatment.

Details

ISSN :
15582531 and 00189294
Volume :
67
Database :
OpenAIRE
Journal :
IEEE Transactions on Biomedical Engineering
Accession number :
edsair.doi.dedup.....dca7927a61e86db0dd8a13214ebaf67c
Full Text :
https://doi.org/10.1109/tbme.2019.2917212