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Rotor Tracking Using Phase of Electrograms Recorded During Atrial Fibrillation

机译:使用心房颤动期间记录的电图相位跟踪转子

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摘要

Extracellular electrograms recorded during atrial fibrillation (AF) are challenging to interpret due to the inherent beat-to-beat variability in amplitude and duration. Phase mapping represents these voltage signals in terms of relative position within the cycle, and has been widely applied to action potential and unipolar electrogram data of myocardial fibrillation. To date, however, it has not been applied to bipolar recordings, which are commonly acquired clinically. The purpose of this study is to present a novel algorithm for calculating phase from both unipolar and bipolar electrograms recorded during AF. A sequence of signal filters and processing steps are used to calculate phase from simulated, experimental, and clinical, unipolar and bipolar electrograms. The algorithm is validated against action potential phase using simulated data (trajectory centre error <0.8 mm); between experimental multi-electrode array unipolar and bipolar phase; and for wavefront identification in clinical atrial tachycardia. For clinical AF, similar rotational content (R 2 = 0.79) and propagation maps (median correlation 0.73) were measured using either unipolar or bipolar recordings. The algorithm is robust, uses standard signal processing techniques, and accurately quantifies AF wavefronts and sources. Identifying critical sources, such as rotors, in AF, may allow for more accurate targeting of ablation therapy and improved patient outcomes.Electronic supplementary materialThe online version of this article (doi:10.1007/s10439-016-1766-4) contains supplementary material, which is available to authorized users.
机译:心房颤动(AF)期间记录的细胞外电描记图由于振幅和持续时间固有的逐搏差异而难以解释。相图以周期内的相对位置表示这些电压信号,并已被广泛应用于心肌电颤动的动作电位和单极电描记图数据。然而,迄今为止,它还没有应用于临床上通常获得的双极记录。这项研究的目的是提出一种新颖的算法,用于从AF期间记录的单极和双极电描记图计算相位。一系列信号滤波器和处理步骤用于从仿真,实验和临床,单极和双极电描记图计算相位。使用模拟数据(轨迹中心误差<0.8毫米)针对动作电位相位验证了算法;在实验性多电极阵列单相和双相之间;用于临床房性心动过速的波前识别。对于临床AF,使用单极或双极记录测量了相似的旋转含量(R 2 = 0.79)和传播图(中位相关值为0.73)。该算法功能强大,使用标准信号处理技术,并且可以准确地量化AF波阵面和震源。识别房颤中的关键来源,例如转子,可以使消融治疗更准确地靶向并改善患者预后。电子补充材料本文的在线版本(doi:10.1007 / s10439-016-1766-4)包含补充材料,可供授权用户使用。

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