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Updated: Aug 11, 2025

Transesophageal Atrial Burst Pacing for Atrial Fibrillation Induction in Rats

Transesophageal Atrial Burst Pacing for Atrial Fibrillation Induction in Rats

Published on: February 14, 2022

An objective tool for quantifying atrial fibrillation substrate in rats.

Michael Murninkas^1,2, Roni Gillis^1,2, Sigal Elyagon^1,2

¹Cardiac Arrhythmia Research Laboratory, Department of Physiology and Cell Biology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

American Journal of Physiology. Heart and Circulatory Physiology

|February 3, 2023

Summary

This summary is machine-generated.

We developed a new algorithm to remove ventricular signals from rodent atrial fibrillation (AF) recordings. This tool objectively quantifies AF complexity, improving rodent models for research.

Keywords:

Lempel-Ziv algorithm atrial arrhythmia atrial remodeling supraventricular arrhythmia waveform complexity

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Optimization of Transesophageal Atrial Pacing to Assess Atrial Fibrillation Susceptibility in Mice

Optimization of Transesophageal Atrial Pacing to Assess Atrial Fibrillation Susceptibility in Mice

Published on: June 29, 2022

Area of Science:

Biomedical Engineering
Cardiovascular Research
Computational Biology

Background:

Rodent models are crucial for atrial fibrillation (AF) research, but analyzing atrial electrograms is challenging due to ventricular signal interference.
Existing methods struggle to differentiate between regular and irregular atrial arrhythmias, limiting objective AF substrate analysis.
Implantation of recording electrodes can induce atrial remodeling, further complicating data interpretation.

Purpose of the Study:

To develop and validate an algorithm for cleaning atrial electrograms from ventricular signals in rodents.
To objectively quantify atrial fibrillation (AF) substrate using waveform complexity analysis.
To assess the algorithm's ability to detect AF and electrode implantation-induced atrial remodeling.

Main Methods:

Developed an algorithm to automatically subtract ventricular signals (QRS complex) from atrial electrograms.
Utilized the Lempel-Ziv complexity algorithm to analyze the complexity of purified atrial signals.
Defined a Complexity Ratio (CR) by normalizing post-burst to pre-burst signal complexity.
Validated the CR cutoff using Receiver Operating Characteristic (ROC) curve analysis.

Main Results:

The CR algorithm achieved high sensitivity (94.5%) and specificity (93.1%) in detecting irregular arrhythmic events (AUC=0.96).
Automated analysis revealed a progressive increase in atrial signal complexity over time post-implantation.
Power spectrum analysis showed augmentation of high frequencies, correlating with increased signal complexity.
The CR algorithm effectively detected atrial remodeling induced by electrode implantation.

Conclusions:

The developed algorithm provides an objective and efficient method for analyzing atrial fibrillation (AF) substrate in rodents.
This tool overcomes technical limitations of ventricular signal mixing, enabling standardized AF research.
The CR analysis facilitates the study of AF pathophysiology and atrial remodeling in preclinical models.