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A hierarchical algorithm for causality discovery among atrial fibrillation electrograms

机译:心房颤动电图之间因果关系发现的分层算法

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Multi-channel intracardiac electrocardiograms (electrograms) are sequentially acquired, at the electrophysiology laboratory, in order to guide radio frequency catheter ablation during heart surgery performed on patients with sustained atrial fibrillation (AF). These electrograms are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex fractionated electrograms). In this paper, we introduce a novel hierarchical algorithm for causality discovery among these multi-output sequentially acquired electrograms. The causal model obtained provides important information about the propagation of the electrical signals inside the heart, uncovering wavefronts and activation patterns that will serve to increase our knowledge about AF and guide cardiologists towards candidate areas for catheter ablation. Numerical results on synthetic signals, generated using the FitzHugh-Nagumo model, show the good performance of the proposed approach.
机译:在电生理实验室顺序采集多通道心内心电图(电描记图),以指导对患有持续性心房颤动(AF)的患者进行的心脏手术期间射频导管消融。心脏科医师使用这些电描记图确定消融的候选区域(例如,对应于高主频率或复杂的分数电描记图的区域)。在本文中,我们介绍了一种新颖的分层算法,用于在这些多输出顺序获取的电描记图之间发现因果关系。所获得的因果模型提供了有关心脏内部电信号传播,揭示波前和激活模式的重要信息,这些信息将有助于增加我们对房颤的了解,并引导心脏病医生朝着导管消融的候选区域发展。使用FitzHugh-Nagumo模型生成的合成信号的数值结果显示了该方法的良好性能。

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