Frequency Domain Analysis of the Surface Electrocardiogram and Intra-cardiac Recordings: Insights into the Mechanisms of Atrial Fibrillation

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Atrial fibrillation (AF) is the most common supraventricular tachyarrhythmia and its prevalence is expected to increase with the aging population. However, the mechanisms AF initiation, maintenance and termination are not completely understood. The surface electrocardiogram (ECG) characteristics are a direct reflection of pathophysiologic events in the atria and can be used in studying AF. The main goal of this thesis is to use time and frequency domain methods to investigate the surface ECG and its relationship to intra-cardiac electrograms to investigate the mechanisms of AF. In this research the possibility of quantifying the AF-induced electrophysiological remodeling and its reversal by analyzing the surface ECG during paroxysmal AF was explored. The change of fibrillatory wave dynamics during the spontaneous onset and termination of AF was investigated. It is not well understood how activity from different parts of the atria contributes to the surface ECG. Lead V1 is dominated by right atrial activity due to its proximal location to the right atrium and is often used for analysis. However, it has been established that AF mostly initiates in the pulmonary veins and the left atrium. Therefore, part of the work investigates whether left atrial events are reflected in the surface ECG, and whether additional surface ECG leads should be used in patients with AF. Misdiagnosis of AF can result in potentially harmful or unnecessary treatment with antiarrhythmic drugs or cardioversion. Many patients with AF are implanted with cardiac pacemakers or defibrillators. However, automated computer interpretation algorithms have been shown to misdiagnose and completely misclassify pacemaker pulses. In this research a new high-resolution system optimized for recording outputs from electronic pacemakers was evaluated. The surface ECG and intra-cardiac electrograms may be best used simultaneously in the study of AF mechanisms, as they provide complementary information to each other. The information they provide can be useful in targeting therapy to treat and terminate AF.

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  • 09/10/2018
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