Simulation of ventricular rate control during atrial fibrillation using ionic channel blockers

ECG-based estimation of respiration-induced autonomic modulation of AV nodal conduction during atrial fibrillation

Introduction: Information about autonomic nervous system (ANS) activity may offer insights about atrial fibrillation (AF) progression and support personalized AF treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in atrioventricular (AV) nodal refractory period and conduction delay. Methods: A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where an ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis. Results: We demonstrated using synthetic data that the 1D-CNN can estimate the respiratory modulation from RR series alone with a Pearson sample correlation of r = 0.805 and that the addition of either respiration signal (r = 0.830), AFR (r = 0.837), or both (r = 0.855) improves the estimation. Discussion: Initial results from analysis of ECG data suggest that our proposed estimate of respiration-induced autonomic modulation, a resp, is reproducible and sufficiently sensitive to monitor changes and detect individual differences. However, further studies are needed to verify the reproducibility, sensitivity, and clinical significance of a resp.

A compact multi-functional model of the rabbit atrioventricular node with dual pathways

The atrioventricular node (AVN) is considered a “black box”, and the functioning of its dual pathways remains controversial and not fully understood. In contrast to numerous clinical studies, there are only a few mathematical models of the node. In this paper, we present a compact, computationally lightweight multi-functional rabbit AVN model based on the Aliev-Panfilov two-variable cardiac cell model. The one-dimensional AVN model includes fast (FP) and slow (SP) pathways, primary pacemaking in the sinoatrial node, and subsidiary pacemaking in the SP. To obtain the direction-dependent conduction properties of the AVN, together with gradients of intercellular coupling and cell refractoriness, we implemented the asymmetry of coupling between model cells. We hypothesized that the asymmetry can reflect some effects related to the complexity of the real 3D structure of AVN. In addition, the model is accompanied by a visualization of electrical conduction in the AVN, revealing the interaction between SP and FP in the form of ladder diagrams. The AVN model demonstrates broad functionality, including normal sinus rhythm, AVN automaticity, filtering of high-rate atrial rhythms during atrial fibrillation and atrial flutter with Wenckebach periodicity, direction-dependent properties, and realistic anterograde and retrograde conduction curves in the control case and the cases of FP and SP ablation. To show the validity of the proposed model, we compare the simulation results with the available experimental data. Despite its simplicity, the proposed model can be used both as a stand-alone module and as a part of complex three-dimensional atrial or whole heart simulation systems, and can help to understand some puzzling functions of AVN.

An atrioventricular node model incorporating autonomic tone

The response to atrial fibrillation (AF) treatment is differing widely among patients, and a better understanding of the factors that contribute to these differences is needed. One important factor may be differences in the autonomic nervous system (ANS) activity. The atrioventricular (AV) node plays an important role during AF in modulating heart rate. To study the effect of the ANS-induced activity on the AV nodal function in AF, mathematical modelling is a valuable tool. In this study, we present an extended AV node model that incorporates changes in autonomic tone. The extension was guided by a distribution-based sensitivity analysis and incorporates the ANS-induced changes in the refractoriness and conduction delay. Simulated RR series from the extended model driven by atrial impulse series obtained from clinical tilt test data were qualitatively evaluated against clinical RR series in terms of heart rate, RR series variability and RR series irregularity. The changes to the RR series characteristics during head-down tilt were replicated by a 10% decrease in conduction delay, while the changes during head-up tilt were replicated by a 5% decrease in the refractory period and a 10% decrease in the conduction delay. We demonstrate that the model extension is needed to replicate ANS-induced changes during tilt, indicating that the changes in RR series characteristics could not be explained by changes in atrial activity alone.

A Review on Atrial Fibrillation (Computer Simulation and Clinical Perspectives)

Atrial fibrillation (AF), a heart condition, has been a well-researched topic for the past few decades. This multidisciplinary field of study deals with signal processing, finite element analysis, mathematical modeling, optimization, and clinical procedure. This article is focused on a comprehensive review of journal articles published in the field of AF. Topics from the age-old fundamental concepts to specialized modern techniques involved in today’s AF research are discussed. It was found that a lot of research articles have already been published in modeling and simulation of AF. In comparison to that, the diagnosis and post-operative procedures for AF patients have not yet been totally understood or explored by the researchers. The simulation and modeling of AF have been investigated by many researchers in this field. Cellular model, tissue model, and geometric model among others have been used to simulate AF. Due to a very complex nature, the causes of AF have not been fully perceived to date, but the simulated results are validated with real-life patient data. Many algorithms have been proposed to detect the source of AF in human atria. There are many ablation strategies for AF patients, but the search for more efficient ablation strategies is still going on. AF management for patients with different stages of AF has been discussed in the literature as well but is somehow limited mostly to the patients with persistent AF. The authors hope that this study helps to find existing research gaps in the analysis and the diagnosis of AF.

Identification of Biomarkers Related to Atrial Fibrillation With Mitral Regurgitation

BACKGROUND

We aimed to explore the biomarkers associated with atrial fibrillation (AF) with mitral regurgitation (MR).

METHODS

The gene expression profile data GSE115574 were downloaded from Gene Expression Omnibus database, which were obtained from patients with degenerative MR with AF and sinus rhythm (SR). The differentially expressed genes (DEGs) in samples of AF with MR compared with those of SR with MR were selected, followed by functional enrichment analysis, protein-protein interaction (PPI) network analysis, transcription factor (TF) prediction, and drug-gene interaction prediction.

RESULTS

By comparing the genes' expression profiles between AF with MR and SR with MR, 379 DEGs were obtained. The upregulated genes, such as NMNAT2, LDHB, and hexosaminidase subunit beta (HEXB), were significantly enriched in metabolic pathways. Hub genes, such as amyloid beta precursor protein (APP), CDH2, SPP1, and STC2, were significantly associated with functions related to extracellular matrix organization and vitamin D response. Additionally, two TFs, PRDM3 and LSM6, were predicted for the key module genes. APP predicted the most drug molecules, that is, 22 molecules, and SPP1 predicted 10 drug molecules.

CONCLUSIONS

Dysregulation of the metabolic pathway may play a critical role in AF with MR. Changes in functions related to the extracellular matrix and vitamin D response may also be associated with AF progression in patients with MR. Furthermore, APP, STC2, and SPP1 may serve as potential therapeutic targets of AF.