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Mechanism of Cardiac Arrhythmias

Mechanism of Cardiac Arrhythmias

Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...

Disturbances in Heart Rhythm

Disturbances in Heart Rhythm

Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow heart...

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Related Experiment Video

Updated: Oct 13, 2025

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

Published on: July 29, 2011

Atrial Flutter Mechanism Detection Using Directed Network Mapping.

Muhamed Vila¹, Massimo Walter Rivolta¹, Giorgio Luongo²

¹Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy.

Frontiers in Physiology

|November 12, 2021

Summary

This summary is machine-generated.

Network theory effectively identifies atrial flutter (AFL) mechanisms by detecting electrical propagation cycles. This computational approach shows promise for automatically diagnosing AFL during electrophysiology studies.

Keywords:

atrial flutter cardiac arrhythmias catheter ablation electrograms network theory (graphs)

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Last Updated: Oct 13, 2025

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

Published on: July 29, 2011

Electrophysiological Assessment of Murine Atria with High-Resolution Optical Mapping

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Published on: February 22, 2018

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Area of Science:

Computational cardiology
Network theory applications
Electrophysiology modeling

Background:

Atrial flutter (AFL) is a common arrhythmia requiring catheter ablation.
Identifying AFL's rotational electrical activity during electrophysiological (EP) studies is challenging.
Current mapping methods demand significant effort to delineate AFL circuits.

Purpose of the Study:

To model atrial flutter electrical propagation using network theory.
To apply a cycle-finding algorithm for automatic detection of reentry circuits.
To evaluate the efficacy of directed network mapping in identifying AFL mechanisms.

Main Methods:

Modeled atrial flutter electrical propagation using network theory (NT).
Employed a directed network mapping and cycle-finding algorithm.
Tested the method on sinus rhythm, in-silico, and atrial flutter patient data.

Main Results:

The algorithm accurately detected electrical propagation in sinus rhythm and in-silico models.
Identified AFL mechanisms in 8 out of 10 patients, including mitral/tricuspid valve and figure-of-eight reentries.
Two cases showed limitations due to poor mapping quality or complex cardiac conditions.

Conclusions:

Directed network mapping is an innovative tool for automatic AFL mechanism identification.
The method demonstrates promising results in analyzing complex atrial arrhythmias.
Further research is necessary to validate its reliability across diverse clinical scenarios.