Signal Reconstruction of Pulmonary Vein Recordings Using a Phenomenological Mathematical Model: Application to Pulmonary Vein Isolation Therapy

Electrogram fractionation during sinus rhythm occurs in normal voltage atrial tissue in patients with atrial fibrillation

INTRODUCTION

Electrogram (EGM) fractionation is often associated with diseased atrial tissue; however, mechanisms for fractionation occurring above an established threshold of 0.5 mV have never been characterized. We sought to investigate during sinus rhythm (SR) the mechanisms underlying bipolar EGM fractionation with high-density mapping in patients with atrial fibrillation (AF).

METHODS

Forty-five patients undergoing AF ablation (73% paroxysmal, 27% persistent) were mapped at high density (18562 ± 2551 points) during SR (Rhythmia). Only bipolar EGMs with voltages above 0.5 mV were considered for analysis. When fractionation (>40 ms and >4 deflections) was detected, we classified the mechanisms as slow conduction, wave-front collision, or a pivot point. The relationship between EGM duration and amplitude, and tissue anisotropy and slow conduction, was then studied using a computational model.

RESULTS

Of the 45 left atria analyzed, 133 sites of EGM fragmentation were identified with voltages above 0.5 mV. The most frequent mechanism (64%) was slow conduction (velocity 0.45 m/s ± 0.2) with mean EGM voltage of 1.1 ± 0.5 mV and duration of 54.9 ± 9.4 ms. Wavefront collision was the second most frequent (19%), characterized by higher voltage (1.6 ± 0.9 mV) and shorter duration (51.3 ± 11.3 ms). Pivot points (9%) were associated with the highest degree of fractionation with 70.7 ± 6.6 ms and 1.8 ± 1 mV. In 10 sites (8%) fractionation was unexplained. The EGM duration was significantly different among the 3 mechanisms (p = 0.0351).

CONCLUSION

In patients with a history of AF, EGM fractionation can occur at amplitudes > 0.5 mV when in SR in areas often considered not to be diseased tissue. The main mechanism of EGM fractionation is slow conduction, followed by wavefront collision and pivot sites. This article is protected by copyright. All rights reserved.

Calibration of ionic and cellular cardiac electrophysiology models

Cardiac electrophysiology models are among the most mature and well-studied mathematical models of biological systems. This maturity is bringing new challenges as models are being used increasingly to make quantitative rather than qualitative predictions. As such, calibrating the parameters within ion current and action potential (AP) models to experimental data sets is a crucial step in constructing a predictive model. This review highlights some of the fundamental concepts in cardiac model calibration and is intended to be readily understood by computational and mathematical modelers working in other fields of biology. We discuss the classic and latest approaches to calibration in the electrophysiology field, at both the ion channel and cellular AP scales. We end with a discussion of the many challenges that work to date has raised and the need for reproducible descriptions of the calibration process to enable models to be recalibrated to new data sets and built upon for new studies. This article is categorized under: Analytical and Computational Methods > Computational Methods Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Cellular Models.

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies

The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.