Vila Muhamed, Rocher Sara, Rivolta Massimo W, Saiz Javier, Sassi Roberto
Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:730-733. doi: 10.1109/EMBC46164.2021.9629911.
Catheter ablation for atrial fibrillation (AF) is one of the most commonly performed electrophysiology procedures. Despite significant advances in our understanding of AF mechanisms in the last years, ablation outcomes remain suboptimal for many patients, particularly those with persistent or long-standing AF. A possible reason is that ablation techniques mainly focus on anatomic, rather than patient-specific functional targets for ablation. The identification of such ablation targets remains challenging. The purpose of this study is to investigate a novel approach based on directed networks, which allow the automatic detection of important arrhythmia mechanisms, that can be convenient for guiding the ablation strategy. The networks are generated by processing unipolar electrograms (EGMs) collected by the catheters positioned at the different regions of the atria. Network vertices represent the locations of the recordings and edges are determined using cross-covariance time-delay estimation method. The algorithm identifies rotational activity, spreading from vertex to vertex creating a cycle. This work is a simulation study and it uses a highly detailed computational 3D model of human atria in which sustained rotor activation of the atria was achieved. Virtual electrodes were placed on the endocardial surface, and EGMs were calculated at each of these electrodes. The propagation of the electric wave fronts in the atrial myocardium during AF is very complex, so in order to properly capture wave propagation patterns, we split EGMs into multiple short time frames. Then, a specific network for each of these time frames was generated, and the cycles repeating in consecutive networks point us to the stable rotor's location. The respective atrial voltage map served as reference. By detecting a cycle between the same 3 nodes in 19 out of 58 networks, where 10 of these networks were in consecutive time frames, a stable rotor was successfully located.
心房颤动（AF）的导管消融是最常施行的电生理手术之一。尽管近年来我们对AF机制的理解取得了重大进展，但对于许多患者，尤其是那些持续性或长期AF患者，消融结果仍不尽人意。一个可能的原因是消融技术主要侧重于解剖学靶点，而非针对患者的特定功能靶点进行消融。识别此类消融靶点仍然具有挑战性。本研究的目的是探讨一种基于定向网络的新方法，该方法能够自动检测重要的心律失常机制，便于指导消融策略。这些网络通过处理由置于心房不同区域的导管收集的单极电图（EGM）生成。网络顶点代表记录位置，边则使用互协方差时间延迟估计方法确定。该算法识别从一个顶点传播到另一个顶点并形成循环的旋转活动。这项工作是一项模拟研究，它使用了一个高度详细的人体心房计算三维模型，在该模型中实现了心房的持续转子激活。在心肌内膜表面放置虚拟电极，并在每个电极处计算EGM。房颤期间心房心肌中电波前的传播非常复杂，因此为了正确捕捉波传播模式，我们将EGM分割成多个短时间帧。然后，为每个时间帧生成一个特定的网络，并且在连续网络中重复出现的循环向我们指出稳定转子的位置。相应的心房电压图用作参考。通过在58个网络中的19个网络中检测到相同3个节点之间的循环，其中10个网络处于连续时间帧，成功定位了稳定转子。
