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 $C{V}_{min}=0.01\frac{cm}{ms}$, 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 $C{V}_{min}$ to $0.02\frac{cm}{ms}$ had a stronger effect on the false rotors than on the true rotor. This led to a detection rate of $56.6\text{\%}$ 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 $C{V}_{min}=0.01\frac{cm}{ms}$ (14.7%). For $C{V}_{min}=0.02\frac{cm}{ms}$ 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 ($C{V}_{min}$). The true rotor is less dependent on this parameter than the false rotors, which disappear at a $C{V}_{min}=0.02\frac{cm}{ms}$. In order to increase to detection rate of the true rotor, one can decrease $C{V}_{min}$ and discard the new rotors which also appear at lower values of $C{V}_{min}$.

心房颤动（AF）是临床实践中最常见的心律失常。房颤治疗的主要问题之一是难以从临床记录中识别心律失常的来源。之所以出现这种困难，是因为目前无法验证在临床数据中确定这些来源的算法，因为无法在患者身上记录高分辨率的真实激励模式。因此，计算机建模等替代方法引起了人们的极大兴趣。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——相位映射（PM）。在右心房模拟蜿蜒的转子，并将篮式导管放置在 3 个不同的位置：上腔静脉 (SVC)、终嵴 (CT) 和冠状窦 (CS)。结果表明，虽然 PM 可以识别真实源，但由于所有三个位置的远场效应和插值误差，它也发现了几个错误源。此外，检测效率很大程度上取决于篮子的位置。

最近，开发了一种新工具来分析任何心律失常，称为定向图映射（DGM）。DGM 基于网络理论，创建激励模式的有向图，从中可以检测心律失常的位置和来源。因此，当前研究的目的是在该曲流转子的篮子数据集上比较 DGM 与 PM 的效率。DGM 工具适用于各种传导速度（最小和最大），这些传导速度是 DGM 的输入参数。

总的来说，我们发现 DGM 能够区分 SVC 和 CT 篮位置的真转子和假转子。例如，对于 SVC 位置$C{V}_{米我n}=0.01\frac{C米}{米s}$，DGM 检测到真​​正的核心，PM 的患病率为 82%，而 PM 的患病率为 94%。检测到三个错误转子，分别为 39.16% (DGM) 和 100% (PM)；22.64% (DGM) 对比 100% (PM)；0% (DGM) 与 57% (PM)。增加$C{V}_{米我n}$到$0.02\frac{C米}{米s}$对假转子的影响比对真转子的影响更大。这导致检出率$56.6\text{\%}$对于真正的转子，而所有其他假转子都消失了。CT 位置也观察到类似的趋势。对于 CS 位置，DGM 的真实转子性能已经较低$C{V}_{米我n}=0.01\frac{C米}{米s}$（14.7%）。为了$C{V}_{米我n}=0.02\frac{C米}{米s}$因此无法区分假转子和真转子。

我们可以得出结论，DGM 可以通过改变其输入参数之一来克服 PM 的一些限制（$C{V}_{米我n}$）。真转子对这个参数的依赖程度低于假转子，假转子在某个时间点就会消失。$C{V}_{米我n}=0.02\frac{C米}{米s}$。为了提高真实转子的检测率，可以减少$C{V}_{米我n}$并丢弃同样出现较低值的新转子$C{V}_{米我n}$。