AV nodal pathways in the R-R interval histogram of the 24-hour monitoring ECG in patients with atrial fibrillation

Computational Simulation of Cardiac Function and Blood Flow in the Circulatory System under Continuous Flow Left Ventricular Assist Device Support during Atrial Fibrillation

Prevalence of atrial fibrillation (AF) is high in heart failure patients supported by a continuous flow left ventricular assist device (CF-LVAD); however, the long term effects remain unclear. In this study, a computational model simulating effects of AF on cardiac function and blood flow for heart failure and CF-LVAD support is presented. The computational model describes left and right heart, systemic and pulmonary circulations and cerebral circulation, and utilises patient-derived RR interval series for normal sinus rhythm (SR). Moreover, AF was simulated using patient-derived unimodal and bimodal distributed RR interval series and patient specific left ventricular systolic functions. The cardiovascular system model simulated clinically-observed haemodynamic outcomes under CF-LVAD support during AF, such as reduced right ventricular ejection fraction and elevated systolic pulmonary arterial pressure. Moreover, relatively high aortic peak pressures and middle arterial peak flow rates during AF with bimodal RR interval distribution, reduced to similar levels as during normal SR and AF with unimodal RR interval distribution under CF-LVAD support. The simulation results suggest that factors such as distribution of RR intervals and systolic left ventricular function may influence haemodynamic outcome of CF-LVAD support during AF.

Cardiac arrhythmias imprint specific signatures on Lorenz plots

BACKGROUND

Despite the growing number of studies using Lorenz (Poincaré) plots (LPs) for the analysis of heart rate variability (HRV), a possible correlation between the underlying ECG waveforms and the RR scatter plots has never been systematically studied. We report a comprehensive investigation of distinct Lorenz plot patterns (LPPs) encountered in the context of major cardiac tachyarrhythmias as assessed by 24-hour Holter monitoring and detail the mechanisms underlying the specific LPPs.

METHODS

The 24-hour ambulatory electrocardiograms (AECGs) of 2700 patients with atrial and/or ventricular tachyarrhythmias and the AECGs of 200 controls with pure sinus rhythm were analyzed using an Elatec arrhythmia analyzing system (Elamedical, Paris 1996). This system allows for the generation of two-dimensional LPs and the exploration of the underlying ECG waveforms. Each LPP obtained was categorized according to its shape and basic geometric parameters. In accordance with the most characteristic LPP feature, the LPPs were grouped into the following distinct classes: 1) comet shape; 2) torpedo shape; 3) H-Fan shape; 4) SZ-Fan shape; 5) double side lobe pattern type A (DSLP-A); 6) double side lobe pattern type B (DSL-B); 7) triple side lobe pattern type A (TSLP-A); 8) triple side lobe pattern type B (TSLP-B);9 island pattern type A (IP-A); 10) island pattern type B (IP-B).

RESULTS

While a comet or a torpedo shape was associated with sinus rhythms, the other LPPs were specifically linked to the presence of cardiac tachyarrhythmias. Thus, a Fan shape was associated with atrial fibrillation or multifocal atrial tachycardia, whereas a DSLP indicated the presence of atrial premature beats, and a TSLP was highly specifically linked to frequent ventricular premature beats. An island pattern was exclusively associated with the presence of an atrial tachycardia or atrial flutter (sensitivity: 100%, specificity: 100%).

CONCLUSION

Major tachyarrhythmias imprint specific patterns on two-dimensional Lorenz plots generated from 24-hour Holter recordings. Thus, the Lorenz plot method has the potential to significantly improve the accuracy of arrhythmia detection and differentiation, particularly with respect to supraventricular tachyarrhythmias.

Exponential distribution of long heart beat intervals during atrial fibrillation and their relevance for white noise behaviour in power spectrum

The statistical properties of heart beat intervals of 130 long-term surface electrocardiogram recordings during atrial fibrillation (AF) are investigated. We find that the distribution of interbeat intervals exhibits a characteristic exponential tail, which is absent during sinus rhythm, as tested in a corresponding control study with 72 healthy persons. The rate gamma of the exponential decay lies in the range 3-12 Hz and shows diurnal variations. It equals, up to statistical uncertainties, the level of the previously uncovered white noise part of the power spectrum, which is also characteristic for AF. The overall statistical features can be described by decomposing the intervals into two statistically independent times, where the first one is associated with a correlated process with 1/f noise characteristics, while the second one belongs to an uncorrelated process and is responsible for the exponential tail. It is suggested to use gamma as a further parameter for a better classification of AF and for the medical diagnosis. The relevance of the findings with respect to a general understanding of AF is discussed.

Nonlinear modeling of the atrioventricular node physiology in atrial fibrillation

A nonlinear model of the atrioventricular (AV) node physiology in atrial fibrillation (AF) is proposed based on three assumptions: (1) normal distribution of atrial impulses, (2) right-skewed distribution of R-R intervals, (3) increase in the refractory period of the AV node due to rapid bombardment from the atria. Simulation resulted in the following conclusions, all of which are in agreement with previous experience: (1) the entry speed of atrial impulses into the AV node in AF is inversely proportional to the ventricular rate, (2) the autocorrelation function of R-R intervals is zero at all delays, (3) a newly introduced index, sign of first difference, has a negative autocorrelation function at the first delay and zero ones at all others. In spite of its simplicity, the model is able to explain what happens in atrial premature complexes, sinus tachycardia and sinus bradycardia. Different rhythms, some of which rarely seen clinically, can be reproduced by changing input patterns or by slightly manipulating the model parameters. In order to make possible a long irregular time series of R-R interval, aperiodic changes in atrial signals are shown to be necessary. In conclusion, we proposed a simple model for the AV node physiology capable of explaining the previously known facts about AF as well as predicting interesting properties of some other supraventricular arrhythmias.