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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.
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Electrocardiogram Fundamentals01:28

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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
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Imaging Studies for Cardiovascular System I:Echocardiography01:17

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Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
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Correlation between ECG and Cardiac Cycle01:25

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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: Nov 18, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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A method to screen left ventricular dysfunction through ECG based on convolutional neural network.

Jin-Yu Sun1,2, Yue Qiu1,2, Hong-Cheng Guo3

  • 1Department of Cardiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China.

Journal of Cardiovascular Electrophysiology
|February 10, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence, specifically a convolutional neural network (CNN), can screen patients for reduced left ventricular ejection fraction (LVEF) using only electrocardiogram (ECG) data. This noninvasive method shows potential for identifying cardiac dysfunction.

Keywords:
artificial intelligenceconvolutional neural networkdeep learningelectrocardiogramheart failureleft ventricular ejection fraction

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Left ventricular ejection fraction (LVEF) is a key indicator of cardiac function.
  • Reduced LVEF signifies left ventricular dysfunction, a critical condition.
  • Current screening methods may be invasive or costly.

Purpose of the Study:

  • To develop an artificial intelligence (AI)-based method for screening patients with LVEF ≤ 50%.
  • To utilize electrocardiogram (ECG) data alone for this screening process.
  • To evaluate the efficacy of a convolutional neural network (CNN) for this diagnostic task.

Main Methods:

  • A convolutional neural network (CNN) model was developed and trained.
  • Standard 12-lead ECG and transthoracic echocardiogram (TTE) data were collected and paired.
  • Data was randomly divided into training, validation, and testing sets (9:1:1 ratio).

Main Results:

  • The CNN algorithm achieved an overall accuracy of 73.9% in the testing set.
  • Sensitivity was 69.2%, specificity 70.5%, positive predictive value 70.1%, and negative predictive value 69.9%.
  • Performance metrics indicate potential for AI-driven screening of cardiac dysfunction.

Conclusions:

  • A well-trained CNN algorithm can serve as a low-cost, noninvasive tool.
  • This AI approach can identify patients with left ventricular dysfunction.
  • ECG-based AI screening offers a promising alternative for early detection.