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Multifractal Desynchronization of the Cardiac Excitable Cell Network During Atrial Fibrillation. I. Multifractal Analysis of Clinical Data

机译:心房颤动期间心脏兴奋性细胞网络的多重分形失步。一临床数据的多重分形分析

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摘要

Atrial fibrillation (AF) is a cardiac arrhythmia characterized by rapid and irregular atrial electrical activity with a high clinical impact on stroke incidence. Best available therapeutic strategies combine pharmacological and surgical means. But when successful, they do not always prevent long-term relapses. Initial success becomes all the more tricky to achieve as the arrhythmia maintains itself and the pathology evolves into sustained or chronic AF. This raises the open crucial issue of deciphering the mechanisms that govern the onset of AF as well as its perpetuation. In this study, we develop a wavelet-based multi-scale strategy to analyze the electrical activity of human hearts recorded by catheter electrodes, positioned in the coronary sinus (CS), during episodes of AF. We compute the so-called multifractal spectra using two variants of the wavelet transform modulus maxima method, the moment (partition function) method and the magnitude cumulant method. Application of these methods to long time series recorded in a patient with chronic AF provides quantitative evidence of the multifractal intermittent nature of the electric energy of passing cardiac impulses at low frequencies, i.e., for times (≳0.5 s) longer than the mean interbeat (≃ 10−1 s). We also report the results of a two-point magnitude correlation analysis which infers the absence of a multiplicative time-scale structure underlying multifractal scaling. The electric energy dynamics looks like a “multifractal white noise” with quadratic (log-normal) multifractal spectra. These observations challenge concepts of functional reentrant circuits in mechanistic theories of AF, still leaving open the role of the autonomic nervous system (ANS). A transition is indeed observed in the computed multifractal spectra which group according to two distinct areas, consistently with the anatomical substrate binding to the CS, namely the left atrial posterior wall, and the ligament of Marshall which is innervated by the ANS. In a companion paper (II. Modeling), we propose a mathematical model of a denervated heart where the kinetics of gap junction conductance alone induces a desynchronization of the myocardial excitable cells, accounting for the multifractal spectra found experimentally in the left atrial posterior wall area.
机译:心房颤动(AF)是一种心律失常,其特征是快速和不规则的心房电活动,对中风发生率有很高的临床影响。最佳的可用治疗策略结合了药理学和手术学手段。但是,一旦成功,它们并不能总是防止长期复发。随着心律失常的维持和病理发展为持续性或慢性房颤,最初的成功变得更加棘手。这就提出了一个公开的关键问题,那就是破译控制房颤发作及其永久性的机制。在这项研究中,我们开发了一种基于小波的多尺度策略,以分析在AF发作期间由位于冠状窦(CS)中的导管电极记录的人心脏的电活动。我们使用小波变换模量最大值方法,矩(分配函数)方法和幅度累积量方法的两个变体来计算所谓的多形谱。这些方法在患有慢性AF的患者中记录的长时间序列中的应用提供了定量的证据,证明低频传递心脏脉冲的电能具有多重分形间歇性,即比平均心跳时间长(≳0.5s)( ≃10 -1 s)。我们还报告了两点幅度相关性分析的结果,该分析推断出不存在基于多重分形标度的乘法时间标度结构。电能动力学看起来像具有二次(对数正态)多重分形光谱的“多重分形白噪声”。这些发现挑战了房颤力学理论中功能性折返回路的概念,但仍保留了自主神经系统(ANS)的作用。实际上,在计算出的多重分形光谱中观察到过渡,该过渡根据两个不同的区域进行分组,这与解剖学底物与CS的结合一致,即左心房后壁和被ANS支配的马歇尔韧带。在随附的论文(II。建模)中,我们提出了失神经的心脏的数学模型,其中间隙连接电导的动力学单独导致心肌兴奋性细胞失同步,这是通过实验在左心房后壁区域发现的多重分形光谱。

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