用遗传算法优化二维心脏组织的低能除颤。

Marcel Aron, Thomas Lilienkamp, Stefan Luther, Ulrich Parlitz
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引用次数: 0

摘要

低能量电脉冲序列可以有效地终止心室颤动(VF),避免常规高能电除颤的副作用,包括组织损伤、创伤性疼痛和预后恶化。然而,低能量脉冲序列的系统优化仍然是一个主要挑战。利用均匀心脏组织的二维模拟和遗传算法,我们演示了具有非均匀脉冲能量和连续脉冲之间时间间隔的序列的优化,以实现有效的VF终止。我们进一步确定了与同等成功率(100%)的参考自适应减速起搏(ADP)方案相比,模型相关的总起搏能量减少幅度为~ 4%至~ 80%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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

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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