Application of information theory for the classification of cardiac fibrillation dynamics
File(s)
Author(s)
Shi, Xili
Type
Thesis
Abstract
Cardiac fibrillation, including atrial fibrillation (AF) and ventricular fibrillation (VF), presents significant clinical challenges due to its complex and variable dynamics. To enable the personalized management of cardiac fibrillation disorders, this thesis applies information theory to facilitate the classification and understanding of these dynamics and their underlying mechanism. The proposed approach utilised information theoretic measures such as dispersion entropy and transfer entropy to shed light on the spatiotemporal organisation and hierarchical organisation of cardiac signals.
Using in silico and in vitro experimental datasets, the direct correlation of spatiotemporal organisation with information theoretic measures was established. The in silico study also standardized and validated the use of information theory-based directional coupling measure for characterising hierarchical organisation in high dimensional data. By applying the analysis developed to the unique datasets from non-contact mapping of persistent AF patients undergoing pulmonary vein isolation (PVI) and patients with clinically induced VF, this work demonstrated that integrating spatiotemporal and hierarchical biomarkers offered a comprehensive view of fibrillation mechanisms. The application of information theory metrics evaluated the spatiotemporal organisation and hierarchical changes induced by PVI, revealing that the role of pulmonary veins and posterior walls in sustaining persistent AF may be heterogeneous. The study further investigates VF initiation by correlating spatiotemporal features with hierarchical structures, as opposed to the persistent AF results the integrated analysis was able to identify highly organised dynamics at a group level in VF.
The findings suggest that incorporating information theory-based markers can improve the precision of personalized treatment strategies for cardiac fibrillation. However, the transition of these methods into clinical practice requires further validation through prospective studies and the refinement of data processing techniques using more sophisticated simulation techniques.
Using in silico and in vitro experimental datasets, the direct correlation of spatiotemporal organisation with information theoretic measures was established. The in silico study also standardized and validated the use of information theory-based directional coupling measure for characterising hierarchical organisation in high dimensional data. By applying the analysis developed to the unique datasets from non-contact mapping of persistent AF patients undergoing pulmonary vein isolation (PVI) and patients with clinically induced VF, this work demonstrated that integrating spatiotemporal and hierarchical biomarkers offered a comprehensive view of fibrillation mechanisms. The application of information theory metrics evaluated the spatiotemporal organisation and hierarchical changes induced by PVI, revealing that the role of pulmonary veins and posterior walls in sustaining persistent AF may be heterogeneous. The study further investigates VF initiation by correlating spatiotemporal features with hierarchical structures, as opposed to the persistent AF results the integrated analysis was able to identify highly organised dynamics at a group level in VF.
The findings suggest that incorporating information theory-based markers can improve the precision of personalized treatment strategies for cardiac fibrillation. However, the transition of these methods into clinical practice requires further validation through prospective studies and the refinement of data processing techniques using more sophisticated simulation techniques.
Version
Open Access
Date Issued
2024-07-02
Date Awarded
01/07/2025
License URL
Advisor
Knopfel, Thomas
Li, Xinyang
Ng, Fu Siong
Peters, Nicholas
Publisher Department
Department of Medicine
Department of Brain Sciences
National Heart & Lung Institute
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)