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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.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
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Aortic Regurgitation II: Clinical Features and Diagnostic Tests01:22

Aortic Regurgitation II: Clinical Features and Diagnostic Tests

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Aortic valve regurgitation (AR) occurs when the aortic valve fails to close properly, allowing blood to flow backward from the aorta into the left ventricle. This backflow can result in two distinct clinical presentations: acute and chronic AR, each characterized by its own set of symptoms and physical findings.Acute Aortic RegurgitationAcute AR presents with a sudden onset of severe symptoms. Patients typically experience profound dyspnea (shortness of breath), chest pain, and signs of left...
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Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies01:22

Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies

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The key clinical manifestations of Rheumatic heart disease (RHD) include several distinct cardiac symptoms.Carditis, a hallmark of acute rheumatic fever, involves inflammation of the heart's endocardium, myocardium, and pericardium. Chronic RHD often results from recurrent episodes of carditis. Its symptoms include the following:Murmurs are caused by valvular damage, especially to the mitral and aortic valves. Mitral stenosis or regurgitation is common, with characteristic heart murmurs...
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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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Mitral Stenosis II: Clinical features and Diagnostic Tests01:23

Mitral Stenosis II: Clinical features and Diagnostic Tests

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Mitral stenosis is a heart condition in which the mitral valve, which allows blood to flow from the left atrium to the left ventricle, becomes narrowed or stenotic. This narrowing hinders blood flow and leads to clinical symptoms requiring specific medical evaluations and management strategies. The following overview outlines the clinical symptoms, assessments, diagnostic findings, prevention methods, and treatments for mitral stenosis.Clinical ManifestationsDyspnea (shortness of breath): This...
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Related Experiment Video

Updated: Dec 25, 2025

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
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Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography.

Joon-Myoung Kwon1,2, Soo Youn Lee3, Ki-Hyun Jeon4,2

  • 1Department of Emergency Medicine Mediplex Sejong Hospital Incheon Korea.

Journal of the American Heart Association
|March 24, 2020
PubMed
Summary

A new deep learning algorithm accurately detects significant aortic stenosis (AS) using electrocardiograms (ECGs). This AI tool, analyzing ECG data, offers a promising method for early AS identification, improving patient prognosis.

Keywords:
aortic valve stenosisdeep learningelectrocardiography

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Severe, symptomatic aortic stenosis (AS) carries a poor prognosis.
  • Early detection of AS is challenging due to its prolonged asymptomatic phase.
  • Current screening tools are often ineffective during the asymptomatic period.

Purpose of the Study:

  • To develop and validate a deep learning algorithm for detecting significant AS using electrocardiograms (ECGs).
  • To combine multilayer perceptron and convolutional neural network for enhanced diagnostic accuracy.
  • To assess the algorithm's performance on both 12-lead and single-lead ECGs.

Main Methods:

  • A retrospective cohort study involving adult patients with both ECG and echocardiography data.
  • Development of a deep learning algorithm using 39,371 ECGs, with internal and external validation on 6,453 and 10,865 ECGs, respectively.
  • Utilized demographic information, ECG features, and 500-Hz, 12-lead ECG raw data; sensitivity maps identified key diagnostic regions.

Main Results:

  • The algorithm achieved high accuracy in detecting significant AS.
  • Areas under the ROC curve for 12-lead ECG were 0.884 (internal) and 0.861 (external).
  • Single-lead ECG performance was also strong, with AUCs of 0.845 (internal) and 0.821 (external); sensitivity maps highlighted the T wave in precordial leads.

Conclusions:

  • The deep learning algorithm demonstrates significant potential for accurate AS detection using ECGs.
  • The algorithm shows high diagnostic performance with both 12-lead and single-lead ECG data.
  • This AI-driven approach may facilitate earlier identification of significant AS, potentially improving patient outcomes.