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

危及生命的心律失常，如心室颤动，是世界范围内发病率和死亡率的主要原因。心律失常期间观察到的自组织时空动力学是由涡状旋转激励波及其相互非线性相互作用决定的。由于目前使用的常规治疗方法（传递高能电除颤休克）与严重的副作用相关，因此正在研究开发替代治疗方案，特别是低能量除颤方法。数值模拟可以用于研究低能除颤的相关机制，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来开发和研究心律失常背后的混沌螺旋/涡旋波动力学的有效控制策略：A.假设驱动的终止方法，其中脉冲序列是基于潜在的（物理）机制驱动和开发的；B.人工智能驱动的终止方法，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，我们希望研究我们之前在二维和简化的数值模拟中开发的假设驱动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，我们想要研究一些特征的影响，比如非平凡的几何形状、真实的电导率各向异性和动力学的3D特性（例如涡旋波的丝张力），以及这些特征与终止率的相互作用。在项目的第二部分(B)中，将使用来自强化学习领域的算法：代理（人工神经网络）通过应用空间局部摄动与要控制的系统/动力学（混沌波动力学）相互作用。在基于扰动效果的奖励或惩罚的指导下，目标是开发有效的扰动模式/脉冲序列，这些模式/脉冲序列没有机械假设。最后，我们可以将ai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异同。在与实验伙伴的合作中，我们将讨论如何在实验装置中调查和验证获得的见解，以及如何将获得的知识纳入新型低能除颤疗法的开发中。

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.