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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.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

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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.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
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Related Experiment Video

Updated: Jun 7, 2025

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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ECG-based machine learning model for AF identification in patients with first ischemic stroke.

Chih-Chieh Yu1,2, Yu-Qi Peng3, Chen Lin3

  • 1Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei City.

International Journal of Stroke : Official Journal of the International Stroke Society
|November 13, 2024
PubMed
Summary

A new convolutional neural network (CNN) model can identify atrial fibrillation (AF) in stroke patients using electrocardiograms (ECG) and predict future AF events, aiding early treatment to reduce stroke recurrence.

Keywords:
AsiaRisk factorsacuteantithromboticpreventionstroke

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Area of Science:

  • Cardiology
  • Neurology
  • Artificial Intelligence in Medicine

Background:

  • Atrial fibrillation (AF) increases stroke recurrence risk, often requiring oral anticoagulants.
  • Detecting AF in stroke patients is challenging.
  • Previous studies lack clear benefits of universal anticoagulation for embolic stroke of undetermined source.

Purpose of the Study:

  • Develop a convolutional neural network (CNN) model to detect AF from 12-lead sinus-rhythm ECGs in stroke patients.
  • Evaluate the model's ability to predict future AF occurrence.

Main Methods:

  • Trained a CNN model using ECG data from Taipei Veterans General Hospital.
  • Performed external validation on ischemic stroke patients from National Taiwan University Hospital.
  • Assessed model performance for AF detection and future AF prediction.

Main Results:

  • Achieved AUC of 0.91 (internal) and 0.69 (external) for AF detection.
  • Demonstrated 97% sensitivity and negative predictive value for AF detection.
  • Identified a high-risk group with a 4.06-fold increased risk of future AF incidence.

Conclusions:

  • The CNN model effectively identifies AF in stroke patients via ECG.
  • The model predicts future AF events, enabling early anticoagulation.
  • This approach may reduce recurrent stroke risk; prospective studies are needed.