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非线性方法在区分阵发性与持续性心房颤动中的分段电图中的应用。

Application of nonlinear methods to discriminate fractionated electrograms in paroxysmal versus persistent atrial fibrillation.

机构信息

Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.

Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield, UK.

出版信息

Comput Methods Programs Biomed. 2019 Jul;175:163-178. doi: 10.1016/j.cmpb.2019.04.018. Epub 2019 Apr 18.

Abstract

BACKGROUND AND OBJECTIVE

Complex fractionated atrial electrograms (CFAE) may contain information concerning the electrophysiological substrate of atrial fibrillation (AF); therefore they are of interest to guide catheter ablation treatment of AF. Electrogram signals are shaped by activation events, which are dynamical in nature. This makes it difficult to establish those signal properties that can provide insight into the ablation site location. Nonlinear measures may improve information. To test this hypothesis, we used nonlinear measures to analyze CFAE.

METHODS

CFAE from several atrial sites, recorded for a duration of 16 s, were acquired from 10 patients with persistent and 9 patients with paroxysmal AF. These signals were appraised using non-overlapping windows of 1-, 2- and 4-s durations. The resulting data sets were analyzed with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA). The data was also quantified via entropy measures.

RESULTS

RQA exhibited unique plots for persistent versus paroxysmal AF. Similar patterns were observed to be repeated throughout the RPs. Trends were consistent for signal segments of 1 and 2 s as well as 4 s in duration. This was suggestive that the underlying signal generation process is also repetitive, and that repetitiveness can be detected even in 1-s sequences. The results also showed that most entropy metrics exhibited higher measurement values (closer to equilibrium) for persistent AF data. It was also found that Determinism (DET), Trapping Time (TT), and Modified Multiscale Entropy (MMSE), extracted from signals that were acquired from locations at the posterior atrial free wall, are highly discriminative of persistent versus paroxysmal AF data.

CONCLUSIONS

Short data sequences are sufficient to provide information to discern persistent versus paroxysmal AF data with a significant difference, and can be useful to detect repeating patterns of atrial activation.

摘要

背景和目的

复杂碎裂心房电图(CFAE)可能包含与心房颤动(AF)电生理基质有关的信息;因此,它们对于指导导管消融治疗 AF 具有重要意义。电图信号由激活事件形成,其本质上是动态的。这使得确定那些可以深入了解消融部位位置的信号特性变得困难。非线性测量可能会改善信息。为了验证这一假设,我们使用非线性测量来分析 CFAE。

方法

从 10 例持续性 AF 和 9 例阵发性 AF 患者中获取了持续时间为 16 秒的多个心房部位的 CFAE。使用 1、2 和 4 秒时长的非重叠窗口来评估这些信号。将得到的数据集进行递归图(RP)和递归量化分析(RQA)分析。还通过熵测量对数据进行了量化。

结果

RQA 对持续性与阵发性 AF 显示出独特的图谱。在 RP 中观察到相似的模式重复出现。在 1 和 2 秒以及 4 秒的信号段中,趋势都是一致的。这表明潜在的信号产生过程也是重复的,即使在 1 秒的序列中也可以检测到重复。结果还表明,大多数熵度量指标在持续性 AF 数据中表现出更高的测量值(更接近平衡)。还发现,从后房游离壁部位获得的信号中提取的确定性(DET)、捕获时间(TT)和修正多尺度熵(MMSE)指标,对持续性与阵发性 AF 数据具有高度区分能力。

结论

短数据序列足以提供信息来区分持续性与阵发性 AF 数据,并且可以用于检测心房激活的重复模式。

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