Analytical approaches for myocardial fibrillation signals. (1st November 2018)

• Record Type: Journal Article
• Authors: Handa, Balvinder S.; Roney, Caroline H.; Houston, Charles; Qureshi, Norman A.; Li, Xinyang; Pitcher, David S.; Chowdhury, Rasheda A.; Lim, Phang Boon; Dupont, Emmanuel; Niederer, Steven A.; Cantwell, Chris D.; Peters, Nicholas S.; Ng, Fu Siong
• Is Part Of: Computers in biology and medicine. Volume 102(2018)
• Page Start: 315
• Page End: 326
• Publication Date: 2018-11-01
• Subjects: Atrial fibrillation -- Ventricular fibrillation -- Analysis -- Voltage mapping -- Rotors -- Phase analysis -- Shannon entropy -- Dominant frequency -- Organizational index -- Complex fractionated electrograms; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.07.008
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Atrial and ventricular fibrillation are complex arrhythmias, and their underlying mechanisms remain widely debated and incompletely understood. This is partly because the electrical signals recorded during myocardial fibrillation are themselves complex and difficult to interpret with simple analytical tools. There are currently a number of analytical approaches to handle fibrillation data. Some of these techniques focus on mapping putative drivers of myocardial fibrillation, such as dominant frequency, organizational index, Shannon entropy and phase mapping. Other techniques focus on mapping the underlying myocardial substrate sustaining fibrillation, such as voltage mapping and complex fractionated electrogram mapping. In this review, we discuss these techniques, their application and their limitations, with reference to our experimental and clinical data. We also describe novel tools including a new algorithm to map microreentrant circuits sustaining fibrillation. Highlights: Mechanism of atrial and ventricular fibrillation remains poorly understood. Rotational drivers of fibrillation can be mapped using phase analysis. Voltage mapping can identify fibrotic substrate of fibrillation. Conduction delay algorithm identifies lines of conduction block at reentry core. Substrate and driver mapping is limited by resolution of clinical data sets.