Department of Physics and Astronomy, Ghent University, Ghent, Belgium.
Departamento de Teoría de La Señal y Las Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, Madrid, Spain.
Comput Biol Med. 2021 Jun;133:104381. doi: 10.1016/j.compbiomed.2021.104381. Epub 2021 Apr 15.
Atrial fibrillation (AF) is the most frequently encountered arrhythmia in clinical practise. One of the major problems in the management of AF is the difficulty in identifying the arrhythmia sources from clinical recordings. That difficulty occurs because it is currently impossible to verify algorithms which determine these sources in clinical data, as high resolution true excitation patterns cannot be recorded in patients. Therefore, alternative approaches, like computer modelling are of great interest. In a recent published study such an approach was applied for the verification of one of the most commonly used algorithms, phase mapping (PM). A meandering rotor was simulated in the right atrium and a basket catheter was placed at 3 different locations: at the Superior Vena Cava (SVC), the Crista Terminalis (CT) and at the Coronary Sinus (CS). It was shown that although PM can identify the true source, it also finds several false sources due to the far-field effects and interpolation errors in all three positions. In addition, the detection efficiency strongly depended on the basket location. Recently, a novel tool was developed to analyse any arrhythmia called Directed Graph Mapping (DGM). DGM is based on network theory and creates a directed graph of the excitation pattern, from which the location and the source of the arrhythmia can be detected. Therefore, the objective of the current study was to compare the efficiency of DGM with PM on the basket dataset of this meandering rotor. The DGM-tool was applied for a wide variety of conduction velocities (minimal and maximal), which are input parameters of DGM. Overall we found that DGM was able to distinguish between the true rotor and false rotors for both the SVC and CT basket positions. For example, for the SVC position with a CV=0.01cmms, DGM detected the true core with a prevalence of 82% versus 94% for PM. Three false rotors where detected for 39.16% (DGM) versus 100% (PM); 22.64% (DGM) versus 100% (PM); and 0% (DGM) versus 57% (PM). Increasing CV to 0.02cmms had a stronger effect on the false rotors than on the true rotor. This led to a detection rate of 56.6% for the true rotor, while all the other false rotors disappeared. A similar trend was observed for the CT position. For the CS position, DGM already had a low performance for the true rotor for CV=0.01cmms (14.7%). For CV=0.02cmms the false and the true rotors could therefore not be distinguished. We can conclude that DGM can overcome some of the limitations of PM by varying one of its input parameters (CV). The true rotor is less dependent on this parameter than the false rotors, which disappear at a CV=0.02cmms. In order to increase to detection rate of the true rotor, one can decrease CV and discard the new rotors which also appear at lower values of CV.
心房颤动（AF）是临床实践中最常遇到的心律失常。AF 管理中的一个主要问题是难以从临床记录中识别心律失常源。这种困难的出现是因为目前不可能验证确定这些源的算法，因为在患者中无法记录高分辨率的真实激发模式。因此，计算机建模等替代方法非常有意义。在最近发表的一项研究中，这种方法被应用于验证最常用的算法之一，即相位映射（PM）。在右心房中模拟了蜿蜒的转子，并在三个不同位置放置了篮子导管：上腔静脉（SVC）、冠状窦（CS）和末端嵴（CT）。结果表明，尽管 PM 可以识别真实的源，但由于远场效应和所有三个位置的插值误差，它也会找到几个虚假的源。此外，检测效率强烈依赖于篮子的位置。最近，开发了一种名为有向图映射（DGM）的新工具来分析任何心律失常。DGM 基于网络理论，从激励模式创建有向图，从中可以检测心律失常的位置和源。因此，本研究的目的是比较 DGM 与 PM 在该蜿蜒转子的篮子数据集上的效率。DGM 工具适用于各种传导速度（最小和最大），这是 DGM 的输入参数。总的来说，我们发现 DGM 能够区分 SVC 和 CT 篮子位置的真实转子和虚假转子。例如，对于 CV=0.01cmms 的 SVC 位置，DGM 检测到真实核心的患病率为 82%，而 PM 为 94%。对于 CV=0.01cmms，DGM 检测到 3 个虚假转子的比例为 39.16%（DGM）对 100%（PM）；22.64%（DGM）对 100%（PM）；而 0%（DGM）对 57%（PM）。将 CV 增加到 0.02cmms 对真实转子的影响大于对虚假转子的影响。这导致真实转子的检测率为 56.6%，而所有其他虚假转子都消失了。在 CT 位置观察到类似的趋势。对于 CS 位置，对于 CV=0.01cmms，DGM 对真实转子的性能已经很低（14.7%）。对于 CV=0.02cmms，因此无法区分虚假和真实转子。我们可以得出结论，DGM 可以通过改变其输入参数之一（CV）来克服 PM 的一些限制。真实转子对该参数的依赖性小于虚假转子，后者在 CV=0.02cmms 时消失。为了提高真实转子的检测率，可以降低 CV 值，并丢弃在较低 CV 值下也出现的新转子。
