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，如室颤，是全世界发病率和死亡率的主要原因。在心律失常过程中观察到的自组织时空动力学由涡旋状旋转激发波及其相互作用的非线性决定。由于目前使用的常规治疗方法(提供高能电除颤电击)与严重的副作用有关，因此正在进行研究，以开发替代治疗方案，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，从而有助于将临床前动物研究的数量降至最低。在这个项目中，我们想要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)机制来激励和开发的；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(A)的第一部分，我们想研究假设驱动的终止方法，这些方法是我们之前在二维和简化的数值模拟中开发的，在三维和现实的心脏几何形状中是如何执行的。具体地说，我们希望研究诸如非平凡几何、真实的电导率各向异性和动力学的3D属性(例如，涡旋波的细丝张力)等特征的影响，以及这些特征对于终止率的交互作用。在项目(B)的第二部分，将使用强化学习领域的算法：通过应用空间局部扰动，智能体(人工神经网络)与要控制的系统/动力学(混沌波动力学)相互作用。在基于扰动影响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，而不是机械假设。最后，我们可以比较人工智能驱动的控制策略和当前的假设驱动的方法，以调查这两个概念之间的异同。与我们的实验合作伙伴合作，我们将讨论如何在实验设置中调查和验证所获得的见解，以及如何将所获得的知识融入到新型低能量除颤疗法的开发中。

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.