Atrioventricular nodal function during atrial fibrillation: Model building and robust estimation

Model-based estimation of AV-nodal refractory period and conduction delay trends from ECG

Introduction: Atrial fibrillation (AF) is the most common arrhythmia, associated with significant burdens to patients and the healthcare system. The atrioventricular (AV) node plays a vital role in regulating heart rate during AF by filtering electrical impulses from the atria. However, it is often insufficient in regards to maintaining a healthy heart rate, thus the AV node properties are modified using rate-control drugs. Moreover, treatment selection during permanent AF is currently done empirically. Quantifying individual differences in diurnal and short-term variability of AV-nodal function could aid in personalized treatment selection. Methods: This study presents a novel methodology for estimating the refractory period (RP) and conduction delay (CD) trends, and their uncertainty in the two pathways of the AV node during 24 h using non-invasive data. This was achieved by utilizing a network model together with a problem-specific genetic algorithm and an approximate Bayesian computation algorithm. Diurnal variability in the estimated RP and CD was quantified by the difference between the daytime and nighttime estimates, and short-term variability was quantified by the Kolmogorov-Smirnov distance between adjacent 10-min segments in the 24-h trends. Additionally, the predictive value of the derived parameter trends regarding drug outcome was investigated using several machine learning tools. Results: Holter electrocardiograms from 51 patients with permanent AF during baseline were analyzed, and the predictive power of variations in RP and CD on the resulting heart rate reduction after treatment with four rate control drugs was investigated. Diurnal variability yielded no correlation to treatment outcome, and no prediction of drug outcome was possible using the machine learning tools. However, a correlation between the short-term variability for the RP and CD in the fast pathway and resulting heart rate reduction during treatment with metoprolol (ρ = 0.48, p < 0.005 in RP, ρ = 0.35, p < 0.05 in CD) were found. Discussion: The proposed methodology enables non-invasive estimation of the AV node properties during 24 h, which-indicated by the correlation between the short-term variability and heart rate reduction-may have the potential to assist in treatment selection.

ECG based assessment of circadian variation in AV-nodal conduction during AF—Influence of rate control drugs

The heart rate during atrial fibrillation (AF) is highly dependent on the conduction properties of the atrioventricular (AV) node. These properties can be affected using β-blockers or calcium channel blockers, mainly chosen empirically. Characterization of individual AV-nodal conduction could assist in personalized treatment selection during AF. Individual AV nodal refractory periods and conduction delays were characterized based on 24-hour ambulatory ECGs from 60 patients with permanent AF. This was done by estimating model parameters from a previously created mathematical network model of the AV node using a problem-specific genetic algorithm. Based on the estimated model parameters, the circadian variation and its drug-dependent difference between treatment with two β-blockers and two calcium channel blockers were quantified on a population level by means of cosinor analysis using a linear mixed-effect approach. The mixed-effects analysis indicated increased refractoriness relative to baseline for all drugs. An additional decrease in circadian variation for parameters representing conduction delay was observed for the β-blockers. This indicates that the two drug types have quantifiable differences in their effects on AV-nodal conduction properties. These differences could be important in treatment outcome, and thus quantifying them could assist in treatment selection.

Non-invasive Characterization of Human AV-Nodal Conduction Delay and Refractory Period During Atrial Fibrillation

During atrial fibrillation (AF), the heart relies heavily on the atrio-ventricular (AV) node to regulate the heart rate. Thus, characterization of AV-nodal properties may provide valuable information for patient monitoring and prediction of rate control drug effects. In this work we present a network model consisting of the AV node, the bundle of His, and the Purkinje fibers, together with an associated workflow, for robust estimation of the model parameters from ECG. The model consists of two pathways, referred to as the slow and the fast pathway, interconnected at one end. Both pathways are composed of interacting nodes, with separate refractory periods and conduction delays determined by the stimulation history of each node. Together with this model, a fitness function based on the Poincaré plot accounting for dynamics in RR interval series and a problem specific genetic algorithm, are also presented. The robustness of the parameter estimates is evaluated using simulated data, based on clinical measurements from five AF patients. Results show that the proposed model and workflow could estimate the slow pathway parameters for the refractory period, RminSP and ΔR^SP, with an error (mean ± std) of 10.3 ± 22 and −12.6 ± 26 ms, respectively, and the parameters for the conduction delay, Dmin,totSP and ΔDtotSP, with an error of 7 ± 35 and 4 ± 36 ms. Corresponding results for the fast pathway were 31.7 ± 65, −0.3 ± 77, 17 ± 29, and 43 ± 109 ms. These results suggest that both conduction delay and refractory period can be robustly estimated from non-invasive data with the proposed methodology. Furthermore, as an application example, the methodology was used to analyze ECG data from one patient at baseline and during treatment with Diltiazem, illustrating its potential to assess the effect of rate control drugs.

Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes

OBJECTIVE

A model for simulating multi-lead ECG signals during paroxysmal atrial fibrillation (AF) is proposed.

SIGNIFICANCE

The model is of particular significance when evaluating detection performance in the presence of brief AF episodes, especially since annotated databases with such episodes are lacking.

APPROACH

The proposed model accounts for important characteristics such as switching between sinus rhythm and AF, varying P-wave morphology, repetition rate of f-waves, presence of atrial premature beats, and various types of noise.

MAIN RESULTS

Two expert cardiologists assessed the realism of simulated signals relative to real ECG signals, both in sinus rhythm and AF. The cardiologists identified the correct rhythm in all cases, and considered two-thirds of the simulated signals as realistic. The proposed model was also investigated by evaluating the performance of two AF detectors which explored either rhythm only or both rhythm and morphology. The results show that detection performance is strongly dependent on AF episode duration, and, consequently, demonstrate that the model can play a significant role in the investigation of detector properties.

Autonomic influence on atrial fibrillatory process: head-up and head-down tilting

BACKGROUND

Changes in the autonomic nervous system (ANS) tone are present before, during, and after episodes of atrial fibrillation (AF). Atrial fibrillatory rate (AFR, the inverse of the atrial cycle length) has been used as a surrogate marker for local refractoriness and is a key characteristic of the fibrillatory process in patients with AF. Aim of this study is to assess changes in AFR, as an effect of autonomic balance change.

METHODS

Forty patients undergoing cardiac cardioversion for symptomatic persistent AF were included in the study. Surface ECG was recorded during rest, head-down (HDT, -30°), and head-up tilt (HUT, +60°). A median value of AFR was computed in each phase of the protocol.

RESULTS

AFR decreased during HDT compared to the baseline (B) condition in all patients but three (median AFR_B = 391 fpm vs. AFR_HDT = 377 fpm, p < .0001). HUT increased AFR, making it significantly higher than HDT and baseline conditions (median AFR_HUT = 396 fpm, p < .0001 vs. B and HDT). Heart rate (HR) increased during HUT, but had a heterogeneous behavior in the population during HDT: about one third of the patients had an HR lower during HDT than during baseline, whereas the remaining two third had an increase in HR during HDT.

CONCLUSIONS

Dominant sympathetic/vagal tone during HUT/HDT significantly affects AFR, increasing/decreasing in respect to baseline. It may be worth exploring the possibility that patients with AF of shorter duration can convert to sinus rhythm during HDT.

Noninvasive characterization of atrioventricular conduction in patients with atrial fibrillation

The atrioventricular (AV) node plays a fundamental role in patients with atrial fibrillation (AF), acting as a filter to the numerous irregular atrial impulses which bombard the node. A phenomenological approach to better understand AV nodal electrophysiology is to analyze the ventricular response with respect to irregularity. In different cohorts of AF patients, such analysis has been performed with the aim to evaluate the association between ventricular response characteristics and long-term clinical outcome and to determine whether irregularity is affected by rate-control drugs. Another approach to studying AV nodal characteristics is to employ a mathematical model which accounts for the refractory periods of the two AV nodal pathways. With atrial fibrillatory rate and RR intervals as input, the model has been considered for analyzing data during (i) rest and head-up tilt test, (ii) tecadenoson and esmolol, and (iii) rate-control drugs. The present paper provides an overview of our recent work on the characterization and assessment of AV nodal conduction using these two approaches.

Noninvasive Assessment of Atrioventricular Nodal Function: Effect of Rate-Control Drugs during Atrial Fibrillation

BACKGROUND

During atrial fibrillation (AF), conventional electrophysiological techniques for assessment of refractory period or conduction velocity of the atrioventricular (AV) node cannot be used. We aimed at evaluating changes in AV nodal properties during administration of tecadenoson and esmolol using a novel ECG-based method.

METHODS

Fourteen patients (age 58 ± 8 years, 10 men) with AF were randomly assigned to either 75 or 300 μg intravenous tecadenoson. After tecadenoson wash-out, patients received esmolol continuously (100 μg/kg per min for 10 mins, then 50 μg/kg per min for 50 mins). Atrial fibrillatory rate (AFR) and heart rate (HR) were assessed in 15-min segments. Using the novel method, we assessed the absolute refractory periods of the slow and fast pathways (aRPs and aRPf) of the AV node to produce an estimate of the functional refractory period.

RESULTS

During esmolol infusion, AFR and HR were significantly decreased and the absolute refractory period was significantly prolonged in both pathways (aRPs: 387 ± 73 vs 409 ± 62 ms, P < 0.05; aRPf: 490 ± 80 vs 529 ± 58 ms, P < 0.05). During both tecadenoson doses, HR decreased significantly and AFR was unchanged. Both aRPs and aRPf were prolonged for a 75 μg dose (aRPs: 322 ± 97 vs 476 ± 75 ms, P < 0.05; aRPf: 456 ± 102 vs 512 ± 55 ms, P < 0.05) whereas a trend toward prolongation was observed for a 300 μg dose.

CONCLUSIONS

The estimated parameters reflect expected changes in AV nodal properties, i.e., slower conduction through the AV node for tecadenoson and prolongation of the AV node refractory period for esmolol. Thus, the proposed approach may be used to assess drug effects on the AV node in AF patients.

Statistical modeling of the atrioventricular node during atrial fibrillation: data length and estimator performance

The atrioventricular (AV) node plays a central role during atrial fibrillation (AF). We have recently proposed a statistical AV node model defined by parameters characterizing the arrival rate of atrial impulses, the probability of an impulse choosing either one of the dual AV nodal pathways, the refractory periods of the pathways, and the prolongation of refractory periods. All model parameters are estimated from the RR series using maximum likelihood (ML) estimation, except for the mean arrival rate of atrial impulses which is estimated by the AF frequency derived from the f-waves. The aim of this study is to present a unified approach to ML estimation which also involves the shorter refractory period, thus avoiding our previous Poincaré plot analysis which becomes biased. In addition, the number of RR intervals required for accurate parameter estimation is presented. The results show that the shorter refractory period can be accurately estimated, and that the resulting estimates converge to the true values when about 500 RR intervals are available.

Ventricular activity cancellation in electrograms during atrial fibrillation with constraints on residuals' power

During atrial fibrillation (AF), cancellation of ventricular activity from atrial electrograms (AEG) is commonly performed by template matching and subtraction (TMS): a running template, built in correspondence of QRSs, is subtracted from the AEG to uncover atrial activity (AA). However, TMS can produce poor cancellation, leaving high-power residues. In this study, we propose to modulate the templates before subtraction, in order to make the residuals as similar as possible to the nearby atrial activity, avoiding high-power ones. The coefficients used to modulate the template are estimated by maximizing, via Multi-swarm Particle Swarm Optimization, a fitness function. The modulated TMS method (mTMS) was tested on synthetic and real AEGs. Cancellation performances were assessed using: normalized mean squared error (NMSE, computed on simulated data only), reduction of ventricular activity (VDR), and percentage of segments (PP) whose power was outside the standard range of the atrial power. All testings suggested that mTMS is an improvement over TMS alone, being, on simulated data, NMSE and PP significantly decreased while VDR significantly increased. Similar results were obtained on real electrograms (median values of CS1 recordings PP: 2.44 vs. 0.38 p < 0.001; VDR: 6.71 vs. 8.15 p < 0.001).