Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.1K
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...
1.1K
Electrocardiogram01:29

Electrocardiogram

4.5K
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...
4.5K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

8.1K
An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
8.1K
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

181
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...
181
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

244
Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
244
Cardiopulmonary Resuscitation III: AED Use01:23

Cardiopulmonary Resuscitation III: AED Use

208
Introduction to AEDAn Automated External Defibrillator (AED) is a portable medical device that analyzes the heart's rhythm and, if necessary, delivers an electrical shock to help the heart re-establish an effective rhythm during sudden cardiac arrest (SCA). SCA occurs when the heart suddenly and unexpectedly stops beating, leading to a loss of blood flow to the brain and other vital organs. In such emergencies, time is of the essence, and using an AED, combined with Cardiopulmonary...
208

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Hierarchical abstraction drives human-like 3-D shape processing in deep learning models.

PLoS computational biology·2026
Same author

Adaptively triggered comparisons enhance perceptual category learning: evidence from face learning.

Scientific reports·2024
Same author

Connecting Adaptive Perceptual Learning and Signal Detection Theory in Skin Cancer Screening.

CogSci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference·2024
Same author

For deep networks, the whole equals the sum of the parts.

The Behavioral and brain sciences·2023
Same author

A Novel Algorithm for Improving the Prehospital Diagnostic Accuracy of ST-Segment Elevation Myocardial Infarction.

Prehospital and disaster medicine·2023
Same author

Association of the COVID-19 Pandemic on Treatment Times for ST-Elevation Myocardial Infarction: Observations from the Los Angeles County Regional System.

The American journal of cardiology·2023
Same journal

AI in the ED: Six High-Impact Uses for Clinical Educators.

AEM education and training·2026
Same journal

Outcomes of the SAEM Competency-Based Medical Education Consensus Conference: Challenges and Opportunities in Implementing CBME.

AEM education and training·2026
Same journal

Teaching Urgency Without Losing Humanity: Why Crisis Communication Should Be Core Training in Emergency Medicine.

AEM education and training·2026
Same journal

Drivers and Outcomes of Engagement in Non-Formal Special Interest Groups Among Japanese Emergency Physicians: An Activity Theory Analysis.

AEM education and training·2026
Same journal

From Fly on the Wall to Future Colleagues: Best Practice Recommendations for Medical Student Shadowing Programs.

AEM education and training·2026
Same journal

Implementing Core Entrustable Professional Activities in Emergency Medicine Clerkships: A Psychometric Study of Student Growth.

AEM education and training·2026
See all related articles

Related Experiment Video

Updated: Nov 10, 2025

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy
07:21

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy

Published on: June 27, 2025

255

Mastering Electrocardiogram Interpretation Skills Through a Perceptual and Adaptive Learning Module.

Sally Krasne1, Carl D Stevens2,3, Philip J Kellman4

  • 1Department of Physiology David Geffen School of Medicine University of California Los Angeles CA.

AEM Education and Training
|April 2, 2021
PubMed
Summary
This summary is machine-generated.

An online learning module significantly improved electrocardiogram (ECG) interpretation accuracy and speed for medical students and residents. This ECG training tool demonstrated durable learning, enhancing diagnostic skills for common cardiac abnormalities.

More Related Videos

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

1.2K
High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

9.7K

Related Experiment Videos

Last Updated: Nov 10, 2025

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy
07:21

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy

Published on: June 27, 2025

255
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

1.2K
High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

9.7K

Area of Science:

  • Cardiology
  • Medical Education
  • Diagnostic Imaging

Background:

  • Accurate electrocardiogram (ECG) interpretation is crucial for diagnosing heart disease.
  • Prior studies indicate low ECG interpretation accuracy among medical trainees and physicians.
  • There is a need for effective educational tools to improve ECG interpretation skills.

Purpose of the Study:

  • To evaluate the effectiveness of an online ECG Perceptual and Adaptive Learning Module (ECG PALM).
  • To determine if ECG PALM is an efficient instrument for teaching ECG interpretation.
  • To assess the durability of learning through delayed testing.

Main Methods:

  • A total of 333 medical students and emergency medicine residents participated.
  • Participants completed the ECG PALM, which includes 415 unique ECG tracings.
  • Training effectiveness was measured by pretest, posttest, and delayed tests assessing accuracy and fluency (interpretation within 15 seconds).

Main Results:

  • The ECG PALM significantly improved both accuracy (effect size 0.9–3.2) and fluency (effect size 2.5–3.1) in ECG interpretation (p < 0.0045).
  • Trainees showed significantly better performance on delayed tests compared to a pretest without prior training.
  • Fluency in classifying common ECG abnormalities improved substantially after PALM training, particularly for ST-elevation myocardial infarctions (STEMIs).

Conclusions:

  • The ECG PALM is an effective supplemental tool for enhancing ECG interpretation skills.
  • The module demonstrates durable learning, with improved performance on delayed tests.
  • ECG PALM training leads to significant improvements in both accuracy and speed of interpretation for medical professionals.