Henriksson Mikael, Martin-Yebra Alba, Butkuviene Monika, Rasmussen Jakob Gulddahl, Marozas Vaidotas, Petrenas Andrius, Savelev Aleksei, Platonov Pyotr G, Sornmo Leif
IEEE Trans Biomed Eng. 2021 Jan;68(1):319-329. doi: 10.1109/TBME.2020.2995563. Epub 2020 Dec 21.
The present study proposes a model-based, statistical approach to characterizing episode patterns in paroxysmal atrial fibrillation (AF). Thanks to the rapid advancement of noninvasive monitoring technology, the proposed approach should become increasingly relevant in clinical practice.
History-dependent point process modeling is employed to characterize AF episode patterns, using a novel alternating, bivariate Hawkes self-exciting model. In addition, a modified version of a recently proposed statistical model to simulate AF progression throughout a lifetime is considered, involving non-Markovian rhythm switching and survival functions. For each model, the maximum likelihood estimator is derived and used to find the model parameters from observed data.
Using three databases with a total of 59 long-term ECG recordings, the goodness-of-fit analysis demonstrates that the proposed alternating, bivariate Hawkes model fits SR-to-AF transitions in 40 recordings and AF-to-SR transitions in 51; the corresponding numbers for the AF model with non-Markovian rhythm switching are 40 and 11, respectively. Moreover, the results indicate that the model parameters related to AF episode clustering, i.e., aggregation of temporal AF episodes, provide information complementary to the well-known clinical parameter AF burden.
Point process modeling provides a detailed characterization of the occurrence pattern of AF episodes that may improve the understanding of arrhythmia progression.
本研究提出一种基于模型的统计方法来表征阵发性心房颤动（AF）的发作模式。由于无创监测技术的快速发展，该方法在临床实践中应会变得越来越重要。
采用依赖历史的点过程建模来表征AF发作模式，使用一种新颖的交替双变量霍克斯自激模型。此外，考虑了最近提出的一种统计模型的改进版本，用于模拟一生中AF的进展，该模型涉及非马尔可夫节律转换和生存函数。对于每个模型，推导了最大似然估计器，并用于从观测数据中找到模型参数。
使用三个数据库，共59份长期心电图记录，拟合优度分析表明，所提出的交替双变量霍克斯模型在40份记录中拟合了窦性心律到AF的转换，在51份记录中拟合了AF到窦性心律的转换；具有非马尔可夫节律转换的AF模型的相应数字分别为40和11。此外，结果表明，与AF发作聚类相关的模型参数，即AF发作的时间聚集性，提供了与著名的临床参数AF负荷互补的信息。
点过程建模提供了AF发作发生模式的详细表征，这可能会增进对心律失常进展的理解。
