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  Optimising low-energy defibrillation in 2D cardiac tissue with a genetic algorithm

Aron, M., Lilienkamp, T., Luther, S., & Parlitz, U. (2023). Optimising low-energy defibrillation in 2D cardiac tissue with a genetic algorithm. Frontiers in Network Physiology, 3: 1172454. doi:10.3389/fnetp.2023.1172454.

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 Creators:
Aron, Marcel1, Author           
Lilienkamp, Thomas1, Author           
Luther, Stefan1, Author           
Parlitz, Ulrich1, Author           
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1Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063288              

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 Abstract: Sequences of low-energy electrical pulses can effectively terminate ventricular fibrillation (VF) and avoid the side effects of conventional high-energy electrical defibrillation shocks, including tissue damage, traumatic pain, and worsening of prognosis. However, the systematic optimisation of sequences of low-energy pulses remains a major challenge. Using 2D simulations of homogeneous cardiac tissue and a genetic algorithm, we demonstrate the optimisation of sequences with non-uniform pulse energies and time intervals between consecutive pulses for efficient VF termination. We further identify model-dependent reductions of total pacing energy ranging from ∼4% to ∼80% compared to reference adaptive-deceleration pacing (ADP) protocols of equal success rate (100%).

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Language(s): eng - English
 Dates: 2023-07-242023
 Publication Status: Issued
 Pages: -
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 Rev. Type: Peer
 Identifiers: DOI: 10.3389/fnetp.2023.1172454
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Title: Frontiers in Network Physiology
  Abbreviation : Front. Netw. Physiol.
Source Genre: Journal
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Publ. Info: Frontiers Media
Pages: - Volume / Issue: 3 Sequence Number: 1172454 Start / End Page: - Identifier: ISSN: 2674-0109
CoNE: https://pure.mpg.de/cone/journals/resource/2674-0109