Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation.
Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation.
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DOI：
10.1098/rsos.172434
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发表时间：
2018-04
影响因子：
3.5
通讯作者：
Christensen K
中科院分区：
文献类型：
--
作者：
McGillivray MF;Cheng W;Peters NS;Christensen K
标测分辨率最近已被确定为成功定位房颤（AF）驱动因素的关键限制。使用一个简单的细胞自动机模型的AF，我们展示了一种方法，通过该方法可以快速，准确地使用间接电描记图测量的集合来定位折返驱动程序。所提出的方法采用简单的开箱即用的机器学习算法来将特征电描记图梯度与来自再入驱动器的电描记图记录的位移相关联。这种方法对电活动的局部波动不太敏感。其结果是，该方法成功地定位95.4%的驱动程序在组织中包含一个驱动程序，和95.1%（92.6%）的第一（第二）驱动程序在组织中包含两个驱动程序的AF。此外，我们演示了如何将该技术可以应用于组织与任意数量的驱动程序。在其目前的形式，所提出的技术是不够完善的临床设置。然而，所提出的方法为旨在改善AF靶向消融的未来研究提供了一条有希望的途径。
Mapping resolution has recently been identified as a key limitation in successfully locating the drivers of atrial fibrillation (AF). Using a simple cellular automata model of AF, we demonstrate a method by which re-entrant drivers can be located quickly and accurately using a collection of indirect electrogram measurements. The method proposed employs simple, out-of-the-box machine learning algorithms to correlate characteristic electrogram gradients with the displacement of an electrogram recording from a re-entrant driver. Such a method is less sensitive to local fluctuations in electrical activity. As a result, the method successfully locates 95.4% of drivers in tissues containing a single driver, and 95.1% (92.6%) for the first (second) driver in tissues containing two drivers of AF. Additionally, we demonstrate how the technique can be applied to tissues with an arbitrary number of drivers. In its current form, the techniques presented are not refined enough for a clinical setting. However, the methods proposed offer a promising path for future investigations aimed at improving targeted ablation for AF.
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DOI：
10.1098/rsif.2016.0968
发表时间：
2017-03
期刊：
Journal of the Royal Society, Interface
影响因子：
--
作者：
Lin YT;Chang ET;Eatock J;Galla T;Clayton RH
通讯作者：
Clayton RH
影响因子：
8.6
作者：
Bub, G;Shrier, A;Glass, L
通讯作者：
Glass, L
影响因子：
3.6
作者：
Lord, J.;Willis, S.;Ruiz, F.
通讯作者：
Ruiz, F.
影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
通讯作者：
Thrun S
影响因子：
39.3
作者：
Hansen, Brian J.;Zhao, Jichao;Fedorov, Vadim V.
通讯作者：
Fedorov, Vadim V.