Non-Invasive Characterization of Atrial Activity Immediately Prior to Termination of Paroxysmal Atrial Fibrillation

Autonomic modulation before and after paroxysmal atrial fibrillation events in patients with ischemic heart disease

BACKGROUND

The autonomic activity plays a critical role in generating paroxysmal atrial fibrillation (PAF). This study aimed to evaluate the changes in the autonomic nerve activity before and after PAF events in patients with and without ischemic heart disease (IHD).

METHODS

The study included 49 patients (71.43 ± 12.24 years old, 26 males) with PAF events lasting more than 30 s during 24-hr ambulatory Holter monitoring. The 20-min intervals before and after PAF events were divided into eight segments of 5 min each. Heart rate variability (HRV) analyses of the time and frequency domains were applied to each time segment.

RESULTS

Patients with IHD had significant increases in the root mean square successive differences (r-MSSD, p = .008) and HF component (p = .04), followed by a significant increase in the LF/HF ratio (p = .02) preceding the onset of PAF. Patients without IHD had only a significant increase in the r-MSSD (p = .045) preceding the onset of PAF. During the termination of PAF events, patients in both the IHD and control groups had a significantly decreased r-MSSD and HF, respectively.

CONCLUSION

Ischemic heart disease causes a sympathovagal imbalance in the initiation of PAF. Decreased parasympathetic activity regulated the termination of PAF in both the IHD and control groups. The modification of the autonomic nervous system (ANS) activity should be individualized due to the autonomic complexity in AF arrhythmogenesis and termination.

Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model

Mathematical models of cardiac electrical excitation are increasingly complex, with multiscale models seeking to represent and bridge physiological behaviours across temporal and spatial scales. The increasing complexity of these models makes it computationally expensive to both evaluate long term (more than 60 s) behaviour and determine sensitivity of model outputs to inputs. This is particularly relevant in models of atrial fibrillation (AF), where individual episodes last from seconds to days, and interepisode waiting times can be minutes to months. Potential mechanisms of transition between sinus rhythm and AF have been identified but are not well understood, and it is difficult to simulate AF for long periods of time using state-of-the-art models. In this study, we implemented a Moe-type cellular automaton on a novel, topologically equivalent surface geometry of the left atrium. We used the model to simulate stochastic initiation and spontaneous termination of AF, arising from bursts of spontaneous activation near pulmonary veins. The simplified representation of atrial electrical activity reduced computational cost, and so permitted us to investigate AF mechanisms in a probabilistic setting. We computed large numbers (approx. 10⁵) of sample paths of the model, to infer stochastic initiation and termination rates of AF episodes using different model parameters. By generating statistical distributions of model outputs, we demonstrated how to propagate uncertainties of inputs within our microscopic level model up to a macroscopic level. Lastly, we investigated spontaneous termination in the model and found a complex dependence on its past AF trajectory, the mechanism of which merits future investigation.

Time and frequency recurrence analysis of persistent atrial fibrillation after electrical cardioversion

Electrical cardioversion (ECV) has become a mainstay of therapy for the treatment of persistent atrial fibrillation (AF), which is an arrhythmia that affects up to 1% of the general population. The procedure is initially effective, but it is also characterized by a high rate of AF recurrence. As a consequence, it would be clinically useful to predict normal sinus rhythm (NSR) maintenance after ECV before it is attempted. In this respect, several clinical, echocardiographic and demographic parameters have been analyzed by other authors. However, these indices are weak predictors of ECV outcome. In this work, surface electrocardiographic (ECG) recordings were used to extract the atrial activity (AA) signal and parametrize the fibrillatory (f) waves, both in time and frequency, to obtain AF recurrence predictors. Parameters as f waves amplitude (fWA), AA mean power, dominant atrial frequency (DAF), its first harmonic, etc were studied. Obtained results showed that fWA was the most significant predictor of AF recurrence 1 month later. Concretely, 72.73% of the patients resulting in NSR, 83.87% relapsing to AF and 80.0% with unsuccessful ECV, were correctly identified. Therefore, fWA classified satisfactorily 79.37% of the analyzed patients. In addition, a forward stepwise discriminant analysis, with a leave-one-out cross validation approach, proved that fWA and DAF combination provided an improved diagnostic ability of 85.71%. In this case 86.36%, 83.87% and 90% of the patients who resulted in NSR, relapsed to AF and with unsuccessful ECV, were correctly discerned, respectively. In conclusion, fWA could be considered as a promising predictor of ECV outcome during the first month following the procedure. Additionally, time and frequency indices could yield complementary information useful to predict the cardioversion outcome. Finally, further studies are needed to validate the robustness of these parameters and the repeatability of the obtained results on wider databases.