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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

10.0K
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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Dysrhythmias V: Evaluating Dysrhythmias01:30

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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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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

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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: May 7, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Artificial Intelligence Electrocardiogram and Left Ventricular Systolic Dysfunction in Kenya.

Ambarish Pandey1, Neil Keshvani1,2,3, Matthew W Segar4

  • 1Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas.

JAMA Cardiology
|May 6, 2026
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Summary

An AI-ECG algorithm shows high sensitivity and negative predictive value for detecting left ventricular systolic dysfunction (LVSD) risk. This artificial intelligence electrocardiogram tool is promising for scalable screening in resource-limited settings.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Early detection of heart failure with reduced ejection fraction is crucial but challenging in resource-limited settings due to limited echocardiography access.
  • Artificial intelligence electrocardiogram (AI-ECG) algorithms show potential for identifying left ventricular systolic dysfunction (LVSD).

Purpose of the Study:

  • To determine the frequency of patients with high probability of LVSD by AI-ECG in Kenya.
  • To assess AI-ECG algorithm performance against echocardiography as the gold standard.

Main Methods:

  • A cross-sectional study enrolled 1444 adult patients from 8 healthcare facilities in Kenya.
  • Participants underwent 12-lead ECG, with a subset also completing echocardiography.
  • AI-ECG (AiTiALVSD) was used to identify LVSD risk, compared against echocardiographic confirmation (LVEF <40%).

Main Results:

  • LVSD was identified in 14.1% of participants.
  • The AI-ECG algorithm demonstrated high sensitivity (95.6%) and negative predictive value (99.1%).
  • The algorithm achieved an AUC of 0.96, with consistent performance across cardiovascular risk strata.

Conclusions:

  • The AI-ECG algorithm shows potential clinical utility for screening LVSD risk.
  • The algorithm's high sensitivity and negative predictive value make it suitable for resource-limited settings.
  • This AI-ECG approach may offer a scalable solution for early detection of LVSD.