Optimising low-energy defibrillation in 2D cardiac tissue with a genetic algorithm
2023 | journal article. A publication with affiliation to the University of Göttingen.
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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)
Frontiers in Network Physiology, 3 art. 1172454. DOI: https://doi.org/10.3389/fnetp.2023.1172454
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Details
- Authors
- Aron, Marcel; Lilienkamp, Thomas; Luther, Stefan; Parlitz, Ulrich
- 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%).
- Issue Date
- 2023
- Journal
- Frontiers in Network Physiology
- eISSN
- 2674-0109