Attuel Guillaume, Gerasimova-Chechkina Evgeniya, Argoul Francoise, Yahia Hussein, Arneodo Alain
Geometry and Statistics in Acquisition Data, Centre de Recherche INRIA, Talence, France.
Laboratory of Physical Foundation of Strength, Institute of Continuous Media Mechanics UB RAS, Perm, Russia.
Front Physiol. 2018 Mar 26;8:1139. doi: 10.3389/fphys.2017.01139. eCollection 2017.
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 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.
心房颤动（AF）是一种心律失常，其特征是心房电活动快速且不规则，对中风发生率具有很高的临床影响。现有的最佳治疗策略结合了药物和手术方法。但即便成功，这些方法也并非总能预防长期复发。随着心律失常持续存在且病情发展为持续性或慢性房颤，要取得初步成功变得愈发棘手。这就引出了一个关键的开放性问题，即破解引发房颤及其持续存在的机制。在本研究中，我们开发了一种基于小波的多尺度策略，用于分析在房颤发作期间通过置于冠状窦（CS）的导管电极记录的人体心脏电活动。我们使用小波变换模极大值方法的两种变体、矩（配分函数）方法和幅度累积量方法来计算所谓的多重分形谱。将这些方法应用于一名慢性房颤患者记录的长时间序列，为低频下通过心脏冲动的电能的多重分形间歇性特征提供了定量证据，即对于时间（≳0.5秒）长于平均心跳间隔（≃10秒）的情况。我们还报告了两点幅度相关性分析的结果，该分析推断多重分形标度背后不存在乘法时间尺度结构。电能动态看起来像具有二次（对数正态）多重分形谱的“多重分形白噪声”。这些观察结果对房颤机制理论中的功能性折返电路概念提出了挑战，自主神经系统（ANS）的作用仍不明确。在计算出的多重分形谱中确实观察到了一种转变，这些谱根据两个不同区域分组，这与与冠状窦相连的解剖学基质一致，即左心房后壁和受自主神经系统支配的马歇尔韧带。在一篇配套论文（II. 建模）中，我们提出了一个去神经心脏的数学模型，其中仅缝隙连接电导的动力学就会导致心肌可兴奋细胞的去同步化，这与在左心房后壁区域实验中发现的多重分形谱相符。
