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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

2.5K
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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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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
An ECG utilizes electrodes on the skin...
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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: Jul 28, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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An explainable artificial intelligence-enabled electrocardiogram analysis model for the classification of reduced

Susumu Katsushika1, Satoshi Kodera1, Shinnosuke Sawano1

  • 1Department of Cardiovascular Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.

European Heart Journal. Digital Health
|June 2, 2023
PubMed
Summary
This summary is machine-generated.

We developed an interpretable artificial intelligence (AI) model using electrocardiograms (ECG) to identify reduced left ventricular ejection fraction (LVEF). The AI model

Keywords:
Artificial intelligenceEchocardiographyElectrocardiogramExplainable Artificial intelligenceLeft ventricular dysfunctionMachine learning

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • The
  • black box
  • nature of artificial intelligence (AI) limits its clinical application.
  • Developing interpretable AI models is crucial for clinical practice.
  • Reduced left ventricular ejection fraction (LVEF) is a significant indicator of cardiac health.

Purpose of the Study:

  • To develop an AI model for classifying patients with reduced LVEF using 12-lead ECGs.
  • To ensure the AI model provides decision-interpretability for clinical use.

Main Methods:

  • A random forest model was trained on 29,907 ECGs to identify reduced LVEF.
  • Shapley additive explanations (SHAP) were applied to extract decision criteria.
  • Extracted criteria were clustered and visually interpreted for clinical relevance.

Main Results:

  • The AI model identified reduced LVEF using six key ECG findings.
  • These criteria were consistent across central and co-operative datasets.
  • Cardiologists' accuracy improved from 62.9% to 73.9% after interpreting the criteria.

Conclusions:

  • An interpretable AI model for reduced LVEF classification using ECGs was developed.
  • The model's decision criteria, based on specific ECG findings, are clinically relevant.
  • This interpretable AI approach can enhance clinical decision-making in cardiology.