Assessment of the dynamics of atrial signals and local atrial period series during atrial fibrillation: effects of isoproterenol administration

BACKGROUND: The autonomic nervous system (ANS) plays an important role in the genesis and maintenance of atrial fibrillation (AF), but quantification of its electrophysiologic effects is extremely complex and difficult. Aim of the study was to evaluate the capability of linear and non-linear indexes to capture the fine changing dynamics of atrial signals and local atrial period (LAP) series during adrenergic activation induced by isoproterenol (a sympathomimetic drug) infusion. METHODS: Nine patients with paroxysmal or persistent AF (aged 60 ± 6) underwent electrophysiological study in which isoproterenol was administered to patients. Atrial electrograms were acquired during i) sinus rhythm (SR); ii) sinus rhythm during isoproterenol (SRISO) administration; iii) atrial fibrillation (AF) and iv) atrial fibrillation during isoproterenol (AFISO) administration. The level of organization between two electrograms was assessed by the synchronization index (S), whereas the degree of recurrence of a pattern in a signal was defined by the regularity index (R). In addition, the level of predictability (LP) and regularity of LAP series were computed. RESULTS: LAP series analysis shows a reduction of both LP and R index during isoproterenol infusion in SR and AF (R(SR )= 0.75 ± 0.07 R(SRISO )= 0.69 ± 0.10, p < 0.0001; R(AF )= 0.31 ± 0.08 R(AFISO )= 0.26 ± 0.09, p < 0.0001; LP(SR )= 99.99 ± 0.001 LP(SRISO )= 99.97 ± 0.03, p < 0.0001; LP(AF )= 69.46 ± 21.55 LP(AFISO )= 55 ± 24.75; p < 0.0001). Electrograms analysis shows R index reductions both in SR (R(SR )= 0.49 ± 0.08 R(SRISO )= 0.46 ± 0.09 p < 0.0001) and in AF (R(AF )= 0.29 ± 0.09 R(AFISO )= 0.28 ± 0.08 n.s.). CONCLUSIONS: The proposed parameters succeeded in discriminating the subtle changes due to isoproterenol infusion during both the rhythms especially when considering LAP series analysis. The reduced value of analyzed parameters after isoproterenol administration could reflect an important pro-arrhythmic influence of adrenergic activation on favoring maintenance of AF

Effects of Electrical and Structural Remodeling on Atrial Fibrillation Maintenance: A Simulation Study

Atrial fibrillation, a common cardiac arrhythmia, often progresses unfavourably: in patients with long-term atrial fibrillation, fibrillatory episodes are typically of increased duration and frequency of occurrence relative to healthy controls. This is due to electrical, structural, and contractile remodeling processes. We investigated mechanisms of how electrical and structural remodeling contribute to perpetuation of simulated atrial fibrillation, using a mathematical model of the human atrial action potential incorporated into an anatomically realistic three-dimensional structural model of the human atria. Electrical and structural remodeling both shortened the atrial wavelength - electrical remodeling primarily through a decrease in action potential duration, while structural remodeling primarily slowed conduction. The decrease in wavelength correlates with an increase in the average duration of atrial fibrillation/flutter episodes. The dependence of reentry duration on wavelength was the same for electrical vs. structural remodeling. However, the dynamics during atrial reentry varied between electrical, structural, and combined electrical and structural remodeling in several ways, including: (i) with structural remodeling there were more occurrences of fragmented wavefronts and hence more filaments than during electrical remodeling; (ii) dominant waves anchored around different anatomical obstacles in electrical vs. structural remodeling; (iii) dominant waves were often not anchored in combined electrical and structural remodeling. We conclude that, in simulated atrial fibrillation, the wavelength dependence of reentry duration is similar for electrical and structural remodeling, despite major differences in overall dynamics, including maximal number of filaments, wave fragmentation, restitution properties, and whether dominant waves are anchored to anatomical obstacles or spiralling freely

Understanding and Interpreting Dominant Frequency Analysis of AF Electrograms

Dominant frequency analysis of atrial electrograms has been used to understand the pathophysiology of atrial fibrillation (AF). Although dominant frequency is an effective tool to estimate activation rate during AF, other factors besides activation rate may alter the results. Therefore, an adequate conceptual understanding of frequency domain analysis is required to properly use this technique and interpret the results. This review, while avoiding the use of formulas and equations, aims to explain fundamental theory of how signals can be decomposed into sine waves and how these sine waves relate to the activation rate detected from the electrograms. Through a series of examples and illustrations this relationship can be easily conceptualized. This will in turn allow the strengths and limitations of dominant frequency analysis to be better understood and improve its applicability to potential clinical usages

Wave similarity mapping shows the spatiotemporal distribution of fibrillatory wave complexity in the human right atrium during paroxysmal and chronic atrial fibrillation

Introduction: The complexity of waveforms during atrial fibrillation may reflect critical activation patterns for the arrhythmia perpetuation. In this study, we introduce a novel concept of map, based on the analysis of the wave morphology, which gives a direct evidence in the human right atrium on the spatiotemporal distribution of fibrillatory wave complexity in paroxysmal (PAF) and chronic (CAF) atrial fibrillation. Methods and Results: Electrograms were recorded from a 64-electrode catheter in the right atrium of 15 patients during PAF (n = 8) and CAF (n = 7). Wave similarity maps were constructed by calculating the degree of morphological similarity of activation waves (S) at each atrial site and by following its temporal evolution. During PAF the spatiotemporal distribution of the waveforms was highly consistent across the subjects and was determined by the anatomic location. Wave similarity maps showed the existence of an extended area with low similarity index, which covered the low posteroseptal atrium (S = 0.28 \uc2\ub1 0.09) and the septal region (S = 0.22 \uc2\ub1 0.04), and the presence of a large tongue with high similarity index, which penetrated the lateral wall (S = 0.55 \uc2\ub1 0.08) starting from the high anterolateral atrium (S = 0.54 \uc2\ub1 0.06). A completely different spatiotemporal pattern was seen during CAF. No distinct regions with different similarity indexes were recognized, but a uniformly distributed low similarity index (S = 0.27 \uc2\ub1 0.07) was found. The spatial pattern was highly stable in time with fluctuations of S < 0.04. Conclusion: Quantification of the spatiotemporal distribution of fibrillatory wave complexity is feasible in humans by wave similarity mapping. Anatomic anchoring of waveforms during PAF and pattern destruction during CAF was determined