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Related Concept Videos

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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...
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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...
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
Pulse rhythm01:30

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac muscle...
Disturbances in Heart Rhythm01:29

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: May 25, 2026

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

Atrial Fibrillation detection using time-varying coherence function and Shannon Entropy.

J Lee1, D McManus, K Chon

  • 1Department of Biomedical Engineering, Worcester Polytechnic Institute, MA 01609, USA. jinseok@wpi.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

A new method for detecting Atrial Fibrillation (AF) uses time-varying coherence functions (TVCF) and Shannon Entropy (SE). This approach achieved high accuracy, sensitivity, and specificity in distinguishing AF from normal sinus rhythm.

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

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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Last Updated: May 25, 2026

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

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Published on: July 20, 2022

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
09:17

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

Published on: July 29, 2011

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

Published on: April 11, 2025

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiology

Background:

  • Atrial Fibrillation (AF) is a common arrhythmia requiring accurate detection.
  • Existing methods for AF detection may have limitations in accuracy and specificity.
  • Novel signal processing techniques are needed for improved automated diagnosis.

Purpose of the Study:

  • To develop and validate a novel method for automatic detection of Atrial Fibrillation (AF).
  • To assess the efficacy of time-varying coherence functions (TVCF) and Shannon Entropy (SE) for AF detection.
  • To evaluate the performance of the proposed method on established cardiac rhythm databases.

Main Methods:

  • Introduced a novel method utilizing time-varying coherence functions (TVCF) and Shannon Entropy (SE).
  • Estimated TVCF by multiplying two time-varying transfer functions (TVTFs) derived from adjacent signal segments.
  • Validated the algorithm on RR interval time series from the MIT-BIH Atrial Fibrillation and Normal Sinus Rhythm databases.

Main Results:

  • Achieved an accuracy of 97.49% for Atrial Fibrillation detection using the combined TVCF and SE method.
  • Demonstrated high sensitivity (97.41%) and specificity (97.54%) on the MIT-BIH AF database.
  • Attained 100% specificity on the MIT-BIH Normal Sinus Rhythm database, indicating excellent discrimination.

Conclusions:

  • The proposed method combining TVCF and SE offers a highly accurate and specific approach for automated Atrial Fibrillation detection.
  • This technique shows significant potential for clinical application in diagnosing cardiac arrhythmias.
  • The method effectively differentiates between Atrial Fibrillation and normal sinus rhythm using RR interval data.