Signal processing approaches for the spatiotemporal analysis of cardiac arrhythmias using intracardiac electrograms
2022-02
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Signal processing approaches for the spatiotemporal analysis of cardiac arrhythmias using intracardiac electrograms
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2022-02
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Abstract
Each heartbeat is controlled by an electrical wave of excitation that propagates throughthe heart and initiates cardiac contraction. The normal heartbeat is initiated by pacemaker
cells in the sinus node located in the right atrium, propagate throughout the atria, and then
enters the ventricles via the atrioventricular junction and finally ends in the Purkinje fibers.
The rate and regularity of these cardiac rhythms are determined by the intrinsic firing rate
(automaticity) of the pacemaker cells and the influence of extrinsic factors, including
various ionic mechanisms and drugs. Abnormal regimes of wave initiation and
propagation result in cardiac arrhythmias. Various mechanisms, including local ectopic
activity, focal triggers, wave breaks, and functional reentry, drive the arrhythmic activity
in the heart. The spatiotemporal complexity of each of these underlying mechanisms is
different, with more complexity seen in tachyarrhythmias and less complexity for
bradyarrhythmias. Understanding the spatiotemporal complexity of the different
arrhythmias is of great interest to electrophysiologists. In recent years, catheter ablation therapy (non-pharmacological approach) has had anincreasingly important role in curing many arrhythmias. The underlying spatiotemporal
complexity of each arrhythmia plays an important role in deciding the target sites for
ablation in this therapy. Currently, existing signal analysis techniques are not robust for
all types of arrhythmias. Therefore it is essential to develop new approaches that can fully
capture the intrinsic dynamics and the spatiotemporal complexity of both atrial and
ventricular arrhythmias using intracardiac electrogram signals.
Some novel approaches, namely multiscale frequency, multiscale entropy, kurtosis, and
Shannon entropy was developed using the ex-vivo optical mapping of rabbit hearts. But,
the nature of signals obtained during optical mapping is very different from the
intracardiac electrograms obtained during the catheter ablation procedure. Also, the
clinical recordings suffer from limitations such as sparse spatial data availability and
sequential mapping. Therefore it is essential to enhance the above techniques to work on
the intracardiac electrograms and also identify the spatial sites in the heart that maintain
these arrhythmic activities. For my study, the intracardiac analysis was performed under two different types ofcardiac arrhythmic rhythms, namely Atrial Fibrillation (AF) and Ventricular Fibrillation
(VF). Atrial Fibrillation (AF) is an arrhythmia in the upper two chambers (atria) of the
heart. AF is responsible for significant impairment in quality of life and contributes to
substantial morbidity and health care expenditure. AF is the most common arrhythmia in
humans and, as such, is heterogeneous in its mechanism, presentation, and clinical course
and therefore requires individualized treatment. Ventricular fibrillation (VF) is a type of
lethal heart rhythm. During ventricular fibrillation, disorganized heart signals cause the
lower heart chambers (ventricles) to quiver, and the heart does not pump blood to the rest
of the body. Ventricular fibrillation is an emergency that requires immediate medical
attention. It's the most frequent cause of sudden cardiac death. Although both these rhythms originate at different locations of the heart and havedifferent types of rhythms and morphology, the underlying spatiotemporal organizations
and intracardiac electrogram analysis approaches are similar. Therefore, my thesis
consists of the following three objectives:
1. Clinical implementation and validation of novel approaches using intracardiac
electrograms to characterize the spatiotemporal dynamics of the AF arrhythmic activities.
2. Development of a similarity score using a combination of various iEGMs analysis
techniques to more accurately identify the spatial location of active sites in AF patients.
3. Development of an analytical approach to characterize the organization (organized or
disorganized) of VF electrical activities using clinical intracardiac electrograms.
Description
University of Minnesota Ph.D. dissertation. February 2022. Major: Electrical Engineering. Advisor: Alena Talkachova. 1 computer file (PDF); xiv, 99 pages + 1 compressed folder of supplementary files.
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Ravikumar, Vasanth. (2022). Signal processing approaches for the spatiotemporal analysis of cardiac arrhythmias using intracardiac electrograms. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/250407.
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