Using Poincaré analysis to identify patients with persistent atrial fibrillation
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Atrial fibrillation (AF) contributes to about 158,000 deaths per year [1]. As a progressive disease, early detection is key. This thesis focuses on patients in two AF stages of severity: paroxysmal AF where AF episodes terminate on their own and persistent AF where AF episodes do not terminate on their own. AF Burden (AFB) is a metric that has been used to measure AF severity in the past; however, Poincaré metrics, despite having been around for a long time, have not been used to measure AF severity. This thesis demonstrates a novel approach to measuring AF severity using a Poincaré analysis by studying patients with AFB in three severity categories of Low, Medium and High AF. The results showed a correlation between Poincaré metrics and AFB metrics, indicating Poincaré metrics can be used to identify patients with persistent AF.
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University of Minnesota M.S. thesis. August 2025. Major: Biomedical Engineering. Advisor: Alena Talkachova. 1 computer file (PDF); vii, 50 pages.
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Brower, Autumn. (2025). Using Poincaré analysis to identify patients with persistent atrial fibrillation. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/278024.
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