Rotor pivot point identification with intrinsic mode function complexity index using empirical mode decomposition

S. P. Arunachalam, S. Mulpuru, P. Friedman, E. Tolkacheva
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引用次数: 3

Abstract

Atrial Fibrillation (AF), a most common cardiac arrhythmia affects more than 2.3 million people in the US and associated with increased risk of stroke, heart failure and death. Cather ablation to treat paroxysmal AF patients is somewhat successful with challenges remaining to accurately identify the active sites for persistent AF patients which may occur outside the pulmonary vein (PV) region due to inadequate cardiac mapping systems. In this work, the authors propose an Empirical Mode Decomposition (EMD) approach using multi-scale entropy estimates of the intrinsic mode functions as a complexity measure to accurately identify pivot point of the rotor that were induced in ex-vivo isolated rabbit heart with Ventricular Tachycardia (VT). The new approach using EMD demonstrated successful identification of the rotor core region providing better contrast relative to the periphery region. Validation of the EMD approach using intra-atrial electrograms from paroxysmal and persistent AF patients with rotors is required to accurately identify the rotor pivot point to guide AF ablation.
基于经验模态分解的固有模态函数复杂度指数转子枢轴点识别
房颤(AF)是一种最常见的心律失常,在美国影响着230多万人,并与中风、心力衰竭和死亡的风险增加有关。导管消融治疗阵发性房颤患者在一定程度上是成功的,但由于心脏测绘系统不完善,难以准确识别可能发生在肺静脉(PV)区域外的持续性房颤患者的活动部位。在这项工作中,作者提出了一种经验模态分解(EMD)方法,利用本征模态函数的多尺度熵估计作为复杂性度量来准确识别离体兔室性心动过速(VT)诱导的转子枢轴点。使用EMD的新方法证明了转子核心区域的成功识别,相对于外围区域提供了更好的对比度。需要使用阵发性和持续性房颤患者的心房电图验证EMD方法,以准确识别转子枢轴点以指导房颤消融。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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