Investigating termination mechanisms of chaotic spiral and scroll wave dynamicsunderlying cardiac by using hypothesis-driven and AI-driven terminationapproaches

使用假设驱动和人工智能驱动的终止方法研究心脏的混沌螺旋和滚动波动力学的终止机制

• 批准号: 538874043
• 负责人: Professor Dr. Thomas Lilienkamp
• 金额: --
• 依托单位: Fakultät Angewandte Mathematik, Physik und Allgemeinwissenschaften
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/538874043?language=en
• 关键词: Investigating; termination; mechanisms; chaotic; spiral

Life-threatening cardiac arrhythmias, such as ventricular fibrillation, are a major cause of morbidity and mortality worldwide. The self-organized spatio-temporal dynamics observed during arrhythmias is determined by vortex-like rotating excitation waves and their nonlinear interaction with each other. Since the currently used conventional treatment method (the delivery of a high-energy electrical defibrillation shock) is associated with severe side effects, research is being conducted into the development of alternative treatment options, particularly low-energy defibrillation methods. Numerical simulations can be used to investigate relevant mechanisms of low-energy defibrillation and to develop new effective pulse sequences in detail, and thus contributing in this way to minimize the number of preclinical animal studies. In this project, we want to follow two approaches on how to develop and investigate efficient control strategies for chaotic spiral/scroll wave dynamics underlying cardiac arrhythmias: A. Hypothesis-driven termination approaches, where pulse sequences are motivated and developed based on underlying (physical) mechanisms and B. AI-driven termination approaches, where concepts and algorithms from the field of Reinforcement Learning (RL) will be used. In the first part of the project (A) we want to investigate how hypothesis-driven termination approaches which we previously developed in two-dimensional and simplified numerical simulations perform in three-dimensional and realistic heart geometries. In detail, we want to study the influence of features like a non-trivial geometry, a realistic conductivity anisotropy and 3D properties of the dynamics (e.g. filament tension of scroll waves) as well as the interaction of these features with regard to the termination rate. In the second part of the project (B), algorithms from the field of reinforcement learning will be used: An agent (an artificial neural network) interacts with the system/the dynamics to be controlled (chaotic wave dynamics) by the application of spatially localized perturbations. Guided by rewards or punishments based on the effects of the perturbations, the goal is to develop effective perturbation patterns/pulse sequences that are free of mechanistic assumptions. Finally, we can compare AI-driven control strategies with the current hypothesis-driven approaches, in order to investigate what the similarities and differences between both concepts are. In collaboration with our experimental partners we will discuss how the obtained insights can be investigated and verified in experimental setups and how to incorporate the gained knowledge into the development of novel low-energy defibrillation therapies.

危及生命的心律失常，如心室纤颤，是世界范围内发病率和死亡率的主要原因。在心律失常期间观察到的自组织时空动力学是由涡旋状旋转激励波及其彼此之间的非线性相互作用决定的。由于目前使用的常规治疗方法（提供高能量电除颤电击）与严重的副作用有关，因此正在研究开发替代治疗方案，特别是低能量除颤方法。数值模拟可用于研究低能量除颤的相关机制，并详细开发新的有效脉冲序列，从而有助于减少临床前动物研究的数量。在这个项目中，我们希望遵循两种方法，如何开发和研究有效的控制策略，混沌螺旋/涡卷波动力学的基础心律失常：A。假设驱动的终止方法，其中脉冲序列基于潜在（物理）机制和B被激发和开发。AI驱动的终止方法，其中将使用强化学习（RL）领域的概念和算法。在项目的第一部分（A）中，我们希望研究我们以前在二维和简化的数值模拟中开发的假设驱动的终止方法如何在三维和现实的心脏几何形状中执行。详细地说，我们要研究的影响功能，如一个非平凡的几何形状，一个现实的电导率各向异性和3D性能的动态（如丝张力的滚动波），以及这些功能的相互作用方面的终止率。在项目的第二部分（B）中，将使用强化学习领域的算法：代理（人工神经网络）通过应用空间局部扰动与系统/待控制的动态（混沌波动动力学）进行交互。通过基于扰动影响的奖励或惩罚的指导，目标是开发不受机械假设影响的有效扰动模式/脉冲序列。最后，我们可以将人工智能驱动的控制策略与当前的假设驱动方法进行比较，以研究这两个概念之间的相似之处和差异。在与我们的实验合作伙伴的合作中，我们将讨论如何在实验设置中研究和验证所获得的见解，以及如何将所获得的知识纳入新型低能量除颤疗法的开发中。