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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

7.5K
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.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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

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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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Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

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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...
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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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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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Related Experiment Video

Updated: Mar 21, 2026

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

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Predicting Recurrence and Outcomes After Stressor-Associated Atrial Fibrillation Using ECG-Based Deep Learning.

Julian S Haimovich1,2,3, Samuel Friedman4, Christopher Reeder4

  • 1Cardiovascular Disease Initiative Broad Institute of MIT and Harvard Cambridge MA USA.

Journal of the American Heart Association
|March 20, 2026
PubMed
Summary
This summary is machine-generated.

Stressor-associated atrial fibrillation (AF) recurrence is common and linked to adverse events. Artificial intelligence (AI) integrated with ECGs can improve risk prediction for AF recurrence.

Keywords:
artificial intelligenceatrial fibrillationelectronic health records

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

  • Cardiology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Stressor-associated atrial fibrillation (AF) is new-onset AF triggered by acute stressors.
  • Identifying patients at high risk for AF recurrence is crucial for management.
  • Current clinical factors have limited predictive value for AF recurrence.

Purpose of the Study:

  • To evaluate the utility of artificial intelligence (AI)-enabled 12-lead ECG models in estimating the risk of AF recurrence.
  • To develop and validate a predictive model for AF recurrence incorporating clinical factors, stressor type, and AI-derived risk estimates.

Main Methods:

  • Retrospective analysis of 3371 patients with stressor-associated AF.
  • Quantified cumulative incidence of AF recurrence, accounting for death as a competing risk.
  • Developed a penalized regression model using clinical data, stressor type, and AI-based ECG risk estimates to predict recurrence.

Main Results:

  • The 10-year cumulative incidence of AF recurrence was 41%.
  • AF recurrence was associated with a 2.24-fold increased risk of AF-related adverse events.
  • The combined clinical-AI model demonstrated superior discrimination for AF recurrence (AUC 0.768) compared to clinical factors alone (AUC 0.707).

Conclusions:

  • High rates of AF recurrence following stressor-associated AF pose a significant risk for adverse cardiovascular events.
  • AI-enhanced ECG risk estimates improve the prediction of AF recurrence.
  • These models can help identify high-risk individuals for targeted monitoring and preventive interventions.