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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Disturbances in Heart Rhythm01:29

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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.
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Electrocardiogram Fundamentals01:28

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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
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Dysrhythmias III: Characteristics of Dysrhythmias01:29

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Dysrhythmias, also known as arrhythmias, are irregular heart rhythms that result from abnormal electrical activity in the heart, affecting its ability to circulate blood efficiently. Tachyarrhythmias, a subset of dysrhythmias, are characterized by abnormally fast heart rates exceeding 100 beats per minute. Here are some types of tachyarrhythmias with their distinct ECG features:Sinus Tachycardia:Sinus tachycardia presents a regular heart rhythm with an increased rate of 101-180 beats per...
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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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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...
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Related Experiment Video

Updated: May 5, 2026

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
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Quantifying the frequency modulation in electrograms during simulated atrial fibrillation in 2D domains.

Juan P Ugarte1, Alejandro Gómez-Echavarría2, Catalina Tobón2

  • 1GIMSC, Universidad de San Buenaventura, Medellin, Colombia.

Computers in Biology and Medicine
|October 3, 2024
PubMed
Summary

A new algorithm using the fractional Fourier transform (FrFT) helps pinpoint the causes of atrial fibrillation (AF) by analyzing complex electrogram (EGM) signals. This method improves understanding of AF mechanisms for better catheter ablation strategies.

Keywords:
Cardiac computational modelingFractional Fourier transformMetaheuristic optimizationNonstationary signalsRotors

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Area of Science:

  • Cardiovascular Research
  • Signal Processing
  • Medical Physics

Background:

  • Atrial fibrillation (AF) is a common arrhythmia causing significant mortality and morbidity.
  • Current catheter ablation strategies for AF, particularly persistent AF, face challenges due to limitations in analyzing complex electrogram (EGM) signals.
  • Existing methods often overlook the time-frequency varying nature of EGM components crucial for understanding AF substrates.

Purpose of the Study:

  • To develop and validate a novel algorithm based on the fractional Fourier transform (FrFT) for analyzing non-stationary EGM signals during simulated atrial fibrillation.
  • To enhance the characterization of arrhythmogenic substrates by capturing time-varying frequency components in EGM signals.
  • To improve the efficacy of catheter ablation by providing better insights into AF mechanisms.

Main Methods:

  • Development of a pre-processing step to enhance EGM waveform features.
  • Implementation of a windowing process for dynamic EGM assessment.
  • A fractional Fourier transform (FrFT) order optimization stage to identify compact signal representations and frequency modulation rates.
  • Application of the FrFT algorithm to simulated AF episodes in 2D atrial tissue models.

Main Results:

  • The FrFT-based algorithm successfully characterized non-stationary components in simulated AF EGM signals.
  • Optimized FrFT orders were used to generate spatial maps correlated with AF propagation dynamics.
  • Extreme values in the optimum orders map effectively localized fibrillatory mechanisms responsible for AF.

Conclusions:

  • The FrFT-based algorithm offers a powerful tool for analyzing complex, time-varying EGM signals in atrial fibrillation.
  • This approach enhances the understanding of localized arrhythmogenic substrates, potentially leading to more precise and effective catheter ablation.
  • The study demonstrates the utility of signal processing techniques like FrFT in advancing cardiovascular research and clinical interventions for AF.