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Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/7582

Title: Statistical Analysis of Atrial Fibrillation Electrograms
Authors: Haley, Charlotte

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Keywords: Signal processing
Atrial fibrillation
Issue Date: 9-Oct-2012
Abstract: Atrial fibrillation (AF) is the single most prevalent sustained cardiac rhythm disorder, arising when the normal electrochemical action potential propagating through the atria is interrupted by randomly ring foci. Current therapies rely on the analysis of electrocardiograms taken inside the atria to determine the amount of atrial activation at any given site on the endocardium. Atrial activation is measured by the appearance of peaks in an endocardial signal, detections occurring close together correspond to sites of greater activation and may be closer to the foci in which the disturbance originates. It is the purpose of this study to use signal processing techniques to determine the occurrence times of the peaks in a digitized electrocardiogram (ECG) signal and to generate from this meaningful statistics about the atrial activation of the site where the ECG was taken. Currently, mean cycle length (CL) of a signal is the most widely used statistic for atrial activation. Frequency domain methods and spectrum analysis give basis to claims that AF is not completely chaotic and that its mechanism can be explained by the substrate through which the signals propagate. Frequency domain analysis is used liberally in this paper to support the development of an algorithm for deflection detection. Little is known presently about the mechanism of AF and algorithms such as the one proposed in this paper will provide more quantitative information about the disease process.
Description: A project submitted to the department of mathematics and statistics to complete the degree requirements for the pattern II master of science. Completed and defended August 2009, submitted to Qspace 2012.
URI: http://hdl.handle.net/1974/7582
Appears in Collections:Department of Mathematics and Statistics Graduate Projects

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