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Poincaré Plot Image and Rhythm-Specific Atlas for Atrial Bigeminy and Atrial Fibrillation Detection
IEEE Journal of Biomedical and Health Informatics ( IF 7.7 ) Pub Date : 2020-07-28 , DOI: 10.1109/jbhi.2020.3012339
G. Garcia-Isla 1 , Valentina Corino 1 , Luca Mainardi 1
Affiliation  

A detector based only on RR intervals capable of classifying other tachyarrhythmias in addition to atrial fibrillation (AF) could improve cardiac monitoring. In this paper a new classification method based in a 2D non-linear RRI dynamics representation is presented. For this aim, the concepts of Poincaré Images and Atlases are introduced. Three cardiac rhythms were targeted: Normal sinus rhythm (NSR), AF and atrial bigeminy (AB). Three Physionet open source databases were used. Poincaré Images were generated for all signals using different Poincaré plot configurations: $RR$ , $dRR$ and $RRdRR$ . The study was computed for different time window lengths and bin sizes. For each rhythm, the Poincaré Images of the 80% of that rhythm's patients were used to create a reference image, a Poincaré Atlas. The remaining 20% were used as test set and classified into one of the three rhythms using normalized mutual information and 2D correlation. The process was iterated in a tenfold cross-validation and patient-wise dataset division. Sensitivity results obtained for $RRdRR$ configuration and bin size 40 ms, for a 60 s time window were 94.35% ${}\pm{}$ 3.68, 82.07% ${}\pm{}$ 9.18 and 88.86% ${}\pm{}$ 12.79 with a specificity of 85.52% ${}\pm{}$ 7.46, 95.91% ${}\pm{}$ 3.14, 96.10% ${}\pm{}$ 2.25 for AF, NSR and AB respectively. Results suggest that a rhythms general RRI pattern may be captured using Poincaré Atlases and that these can be used to classify other signal segments using Poincaré Images. In contrast with other studies, the former method could be generalized to more cardiac rhythms and does not depend on rhythm-specific thresholds.

中文翻译:

用于心房二元法和心房颤动检测的庞加莱图图像和特定节律图谱

除了房颤 (AF) 之外,仅基于 RR 间期能够对其他快速性心律失常进行分类的检测器可以改善心脏监测。在本文中,提出了一种基于二维非线性 RRI 动态表示的新分类方法。为此,引入了庞加莱图像和图集的概念。以三种心律为目标:正常窦性心律 (NSR)、AF 和心房二重心律 (AB)。使用了三个 Physionet 开源数据库。使用不同的庞加莱图配置为所有信号生成庞加莱图像:$RR$ , $dRR$$RRdRR$ . 该研究是针对不同的时间窗口长度和 bin 大小进行计算的。对于每个节律,使用该节律患者 80% 的庞加莱图像创建参考图像,即庞加莱图谱。剩余的 20% 用作测试集,并使用归一化互信息和 2D 相关性将其分类为三种节奏之一。该过程在十倍交叉验证和患者数据集划分中迭代。获得的灵敏度结果$RRdRR$ 配置和 bin 大小 40 ms,对于 60 s 时间窗口为 94.35% ${}\pm{}$ 3.68, 82.07% ${}\pm{}$ 9.18 和 88.86% ${}\pm{}$ 12.79,特异性为 85.52% ${}\pm{}$ 7.46, 95.91% ${}\pm{}$ 3.14, 96.10% ${}\pm{}$ AF、NSR 和 AB 分别为 2.25。结果表明,可以使用 Poincaré Atlases 捕获节奏一般 RRI 模式,并且这些可以用于使用 Poincaré 图像对其他信号段进行分类。与其他研究相比,前一种方法可以推广到更多的心律,并且不依赖于特定的节律阈值。
更新日期:2020-07-28
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