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用于碎裂电图分析的新型频谱估计器的优化有助于辨别房颤类型。

Optimization of novel spectral estimator for fractionated electrogram analysis is helpful to discern atrial fibrillation type.

作者信息

Ciaccio Edward J, Biviano Angelo B, Garan Hasan

机构信息

Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, USA.

Department of Medicine, Division of Cardiology, Columbia University Medical Center, New York, USA.

出版信息

Comput Methods Programs Biomed. 2014 Nov;117(2):343-50. doi: 10.1016/j.cmpb.2014.06.006. Epub 2014 Jun 20.

Abstract

INTRODUCTION

Paroxysmal versus persistent atrial fibrillation (AF) can be distinguished based on differences in the spectral parameters of fractionated atrial electrograms. Maximization of these differences would improve characterization of the arrhythmogenic substrate. A novel spectral estimator (NSE) has been shown previously to provide greater distinction in AF spectral parameters as compared with the Fourier transform estimator. Herein, it is described how the differences in NSE spectral parameters can be further improved.

METHOD

In 10 persistent and 9 paroxysmal AF patients undergoing electrophysiologic study, fractionated electrograms were acquired from the distal bipolar ablation electrode. A total of 204 electrograms were recorded from the pulmonary vein (PV) antra and from the anterior and posterior left atrial free wall. The following spectral parameters were measured: the dominant frequency (DF), which reflects local activation rate, the DF amplitude (DA), and the mean spectral profile (MP), which represents background electrical activity. To optimize differences in parameters between paroxysmal versus persistent AF patients, the NSE was varied by selectively removing subharmonics, using a threshold. The threshold was altered in steps to determine the optimal subharmonics removal.

RESULTS

At the optimal threshold level, mean differences in persistent versus paroxysmal AF spectral parameters were: ΔDA=+0.371 mV, ΔDF=+0.737 Hz, and ΔMP=-0.096 mV. When subharmonics were not removed, the differences were substantially less: ΔDA=+0.301 mV, ΔDF=+0.699 Hz, and ΔMP=-0.063 mV.

CONCLUSIONS

NSE optimization produces greater spectral parameter difference between persistent versus paroxysmal AF data. Quantifying spectral parameter differences can be assistive in characterizing the arrhythmogenic substrate.

摘要

引言

阵发性房颤与持续性房颤可根据碎裂心房电图的频谱参数差异进行区分。最大化这些差异将有助于更好地表征心律失常基质。先前的研究表明,一种新型频谱估计器(NSE)与傅里叶变换估计器相比,能在房颤频谱参数上提供更显著的区分度。本文描述了如何进一步提高NSE频谱参数的差异。

方法

对10例持续性房颤患者和9例阵发性房颤患者进行电生理研究,从远端双极消融电极获取碎裂电图。共记录了204份来自肺静脉前庭以及左心房前壁和后壁的电图。测量了以下频谱参数:反映局部激动速率的主导频率(DF)、DF幅度(DA)以及代表背景电活动的平均频谱轮廓(MP)。为优化阵发性房颤患者与持续性房颤患者之间参数的差异,通过使用阈值选择性去除次谐波来改变NSE。逐步改变阈值以确定最佳的次谐波去除量。

结果

在最佳阈值水平下,持续性房颤与阵发性房颤频谱参数的平均差异为:ΔDA = +0.371 mV,ΔDF = +0.737 Hz,ΔMP = -0.096 mV。当未去除次谐波时,差异显著减小:ΔDA = +0.301 mV,ΔDF = +0.699 Hz,ΔMP = -0.063 mV。

结论

NSE优化可使持续性房颤与阵发性房颤数据之间的频谱参数差异更大。量化频谱参数差异有助于表征心律失常基质。

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