Shi Xili, Sau Arunashis, Li Xinyang, Patel Kiran, Bajaj Nikesh, Varela Marta, Wu Huiyi, Handa Balvinder, Arnold Ahran, Shun-Shin Matthew, Keene Daniel, Howard James, Whinnett Zachary, Peters Nicholas, Christensen Kim, Jensen Henrik Jeldtoft, Ng Fu Siong
National Heart and Lung Institute, Imperial College London, London, UK.
Department of Cardiology, Imperial College Healthcare NHS Trust, London, UK.
J R Soc Interface. 2023 Oct;20(207):20230443. doi: 10.1098/rsif.2023.0443. Epub 2023 Oct 11.
Understanding the mechanism sustaining cardiac fibrillation can facilitate the personalization of treatment. Granger causality analysis can be used to determine the existence of a hierarchical fibrillation mechanism that is more amenable to ablation treatment in cardiac time-series data. Conventional Granger causality based on linear predictability may fail if the assumption is not met or given sparsely sampled, high-dimensional data. More recently developed information theory-based causality measures could potentially provide a more accurate estimate of the nonlinear coupling. However, despite their successful application to linear and nonlinear physical systems, their use is not known in the clinical field. Partial mutual information from mixed embedding (PMIME) was implemented to identify the direct coupling of cardiac electrophysiology signals. We show that PMIME requires less data and is more robust to extrinsic confounding factors. The algorithms were then extended for efficient characterization of fibrillation organization and hierarchy using clinical high-dimensional data. We show that PMIME network measures correlate well with the spatio-temporal organization of fibrillation and demonstrated that hierarchical type of fibrillation and drivers could be identified in a subset of ventricular fibrillation patients, such that regions of high hierarchy are associated with high dominant frequency.
了解维持心脏颤动的机制有助于实现个性化治疗。格兰杰因果分析可用于确定在心脏时间序列数据中存在更适合消融治疗的分层颤动机制。如果假设不成立或给定的是稀疏采样的高维数据，基于线性可预测性的传统格兰杰因果分析可能会失效。最近开发的基于信息论的因果度量可能会提供对非线性耦合更准确的估计。然而，尽管它们成功应用于线性和非线性物理系统，但在临床领域的应用尚不清楚。通过混合嵌入的部分互信息（PMIME）来识别心脏电生理信号的直接耦合。我们表明，PMIME所需的数据较少，并且对外在混杂因素更具鲁棒性。然后扩展算法以使用临床高维数据有效地表征颤动组织和层次结构。我们表明，PMIME网络度量与颤动的时空组织密切相关，并证明在一部分室颤患者中可以识别出分层类型的颤动和驱动因素，使得高层次区域与高主导频率相关。
