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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

3.4K
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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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: Sep 29, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular

Hung-Yi Chen1, Chin-Sheng Lin2, Wen-Hui Fang3

  • 1Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan.

Journal of Personalized Medicine
|March 25, 2022
PubMed
Summary

A deep learning model (DLM) can estimate ejection fraction (EF) using electrocardiography (ECG), aiding in early heart failure screening. This ECG-derived EF (ECG-EF) also predicts future cardiovascular events independently, offering a valuable tool for patient management.

Keywords:
artificial intelligencecardiovascular diseasedeep learningejection fractionelectrocardiogramheart failure

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Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
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Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Ejection fraction (EF) is crucial for heart failure (HF) management.
  • Electrocardiography (ECG) is a noninvasive tool for assessing cardiac function.
  • Deep learning models (DLMs) have shown potential in estimating EF from ECG, but clinical impact requires investigation.

Purpose of the Study:

  • Develop a DLM to estimate EF from ECG (ECG-EF).
  • Assess the relationship between ECG-EF and echocardiogram-based EF (ECHO-EF).
  • Explore the predictive value of ECG-EF for future cardiovascular adverse events.

Main Methods:

  • Trained a DLM using 57,206 ECGs and corresponding echocardiograms.
  • Utilized ECG12Net architecture for the DLM.
  • Validated and tested the DLM on separate cohorts (10,762 and 20,629 ECGs).
  • Evaluated ECG-EF and ECHO-EF changes and their association with major adverse cardiovascular events (MACEs).

Main Results:

  • The best DLM achieved an AUC of 0.9472, with 86.9% sensitivity and 89.6% specificity.
  • ECG-EF correlated with ECHO-EF (0.603) with a mean absolute error of 7.436.
  • Lower ECG-EF (≤35%) indicated higher risk of CV complications and poorer EF recovery.
  • ECG-EF independently predicted MACEs and CV outcomes, outperforming ECHO-EF.

Conclusions:

  • DLM-based ECG-EF can screen for asymptomatic left ventricular dysfunction (LVD).
  • ECG-EF contributes independently to predicting future cardiovascular adverse events.
  • DLM-based ECG-EF shows promise as a supportive tool for CV disease prediction and LVD patient management.