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

Electrocardiogram01:29

Electrocardiogram

7.5K
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...
7.5K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.8K
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...
1.8K
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

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

835
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...
835
Aortic Regurgitation II: Clinical Features and Diagnostic Tests01:22

Aortic Regurgitation II: Clinical Features and Diagnostic Tests

819
Aortic valve regurgitation (AR) occurs when the aortic valve fails to close properly, allowing blood to flow backward from the aorta into the left ventricle. This backflow can result in two distinct clinical presentations: acute and chronic AR, each characterized by its own set of symptoms and physical findings.Acute Aortic RegurgitationAcute AR presents with a sudden onset of severe symptoms. Patients typically experience profound dyspnea (shortness of breath), chest pain, and signs of left...
819
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

14.3K
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...
14.3K

You might also read

Related Articles

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

Sort by
Same author

Recurrent cystitis glandularis of the bladder: case report and review.

Urology case reports·2026
Same author

Purification and Biochemical Characterization of a New Thermostable and Detergent-Stable Serine Protease From a Novel Thermo-Halotolerant Bacterial Strain, Laceyella sacchari Strain BK-TM.

Biotechnology and applied biochemistry·2025
Same author

Evaluating the Expression Levels of Human Endogenous Retrovirus-K 10 (HERV-K10) Gag as a Biomarker in Prostate Cancer Tissue.

Cureus·2024
Same author

Hybrid Coupler Used as Tunable Phase Shifter Based on Varactor Diodes.

Micromachines·2024
Same author

"Click" Chemistry for the Functionalization of Graphene Oxide with Phosphorus Dendrons: Synthesis, Characterization and Preliminary Biological Properties.

Chemistry (Weinheim an der Bergstrasse, Germany)·2023
Same author

Molecular evaluation of human papillomavirus as an oncogenic biomarker in prostate cancer.

Molecular biology reports·2023

Related Experiment Video

Updated: Mar 19, 2026

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure
07:41

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure

Published on: February 8, 2022

4.5K

Detection of atrial septal aneurysm on ECG based on Deep Learning algorithm (ANN).

Mohammed Marouane Saim1, Omar Alami2, Hassan Ammor1

  • 1Mohammadia School of Engineers, Mohammed V University of Rabat, ERSC Research Center, 10080, Rabat, Morocco.

La Tunisie Medicale
|March 18, 2026
PubMed
Summary

Machine learning can detect Atrial Septal Aneurysm (ASA) using electrocardiogram (ECG) data. This study shows ML offers a promising approach for diagnosing this often incidentally found cardiac abnormality.

Keywords:
Artificial Neural NetworkAtrial septal aneurysmECGK-Fold Cross-Validation

More Related Videos

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

2.2K
Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

4.5K

Related Experiment Videos

Last Updated: Mar 19, 2026

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure
07:41

Echocardiographic Evaluation of Atrial Communications before Transcatheter Closure

Published on: February 8, 2022

4.5K
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
08:10

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

2.2K
Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

4.5K

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Atrial Septal Aneurysm (ASA) presents diagnostic challenges due to nonspecific symptoms.
  • ASA diagnosis is frequently incidental, lacking specific electrocardiogram (ECG) criteria.
  • Understanding ASA's clinical significance is limited.

Purpose of the Study:

  • To evaluate the efficacy of Machine Learning (ML) in detecting Atrial Septal Aneurysm (ASA) from ECG data.
  • To develop and validate an ML model for ASA identification.
  • To explore ML's potential in improving ASA diagnosis.

Main Methods:

  • Retrospective analysis of 233 individuals (123 with ASA, 110 without).
  • Trans-thoracic Echocardiography (TTE) confirmed ASA presence.
  • An Artificial Neural Network (ANN) was trained and tested on ECG parameters.

Main Results:

  • The ANN model achieved 73% sensitivity and 84% specificity.
  • Positive Predictive Value (PPV) was 80%, Negative Predictive Value (NPV) was 73%, and F-1 score was 0.79.
  • Area Under the Curve (AUC) of 0.8 indicated excellent diagnostic performance.

Conclusions:

  • ML, specifically ANN, demonstrates feasibility for detecting ASA via ECG.
  • This approach offers a potential non-invasive method for identifying ASA.
  • Further research can enhance clinical understanding and management of ASA.