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Optimising low-energy defibrillation in 2D cardiac tissue with a genetic algorithm.
Aron, Marcel; Lilienkamp, Thomas; Luther, Stefan; Parlitz, Ulrich.
Afiliação
  • Aron M; Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Göttingen, Germany.
  • Lilienkamp T; Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.
  • Luther S; Institute for the Dynamics of Complex Systems, Georg-August-Universität Göttingen, Göttingen, Germany.
  • Parlitz U; German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany.
Front Netw Physiol ; 3: 1172454, 2023.
Article em En | MEDLINE | ID: mdl-37555132
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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Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Front Netw Physiol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Alemanha

Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Front Netw Physiol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Alemanha