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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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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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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
An ECG utilizes electrodes on the skin...
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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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Instrumentation Amplifier01:25

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An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
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Related Experiment Video

Updated: Jan 19, 2026

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Multisite, External Validation of an AI-Enabled ECG Algorithm for Detection of Low Ejection Fraction.

Rickey E Carter1, Patrick W Johnson1, Jordan B Strom2

  • 1Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida, USA.

JACC. Advances
|January 17, 2026
PubMed
Summary

Artificial intelligence-based electrocardiogram screening (ECG-AI) effectively detects low left ventricular ejection fraction (LEF). This validated software shows high accuracy, potentially reducing the need for echocardiograms in certain patients.

Keywords:
external validationleft ventricular systolic dysfunctionsoftware as a medical device

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Area of Science:

  • Cardiology
  • Medical Devices
  • Artificial Intelligence

Background:

  • Low left ventricular ejection fraction (LEF) often goes undiagnosed.
  • Artificial intelligence-based electrocardiogram (ECG-AI) screening offers a scalable solution for LEF detection.

Purpose of the Study:

  • To validate a comprehensive ECG-AI software as a medical device for identifying LEF.
  • To assess the diagnostic performance of ECG-AI in a real-world clinical setting.

Main Methods:

  • Utilized data from four diverse US sites, including ECGs and transthoracic echocardiograms performed within 30 days.
  • Extracted and analyzed electronic health records from 13,960 patients.

Main Results:

  • The ECG-AI demonstrated strong diagnostic accuracy with an AUROC of 0.92.
  • Sensitivity was 84.5% and specificity was 83.6% for LEF detection.
  • Positive predictive value was 30.5% and negative predictive value was 98.4%.

Conclusions:

  • External validation confirmed the algorithm's robust diagnostic performance across a diverse patient population.
  • The ECG-AI's high negative predictive value suggests its utility as a rule-out strategy, potentially deferring echocardiography.
  • This study validates the ECG-AI software for LEF detection, highlighting its clinical applicability.