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

2.2K
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...
2.2K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

519
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...
519
Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

907
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
907
Pulse rhythm01:30

Pulse rhythm

768
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
768
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

891
Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
891
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

3.4K
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...
3.4K

You might also read

Related Articles

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

Sort by
Same author

Beyond infarct size: redefining post-infarction remodelling in the era of multiparametric CMR.

European heart journal. Cardiovascular Imaging·2026
Same author

Carbon dioxide is a triple vasodilator.

Cardiovascular research·2026
Same author

Impact of Virtual Reality on Transcatheter Aortic Valve Implantation: A Prospective Randomized Controlled Trial.

Circulation. Cardiovascular imaging·2026
Same author

Predicting Transmural Lesion Formation and Steam-Pop Occurrence During Bipolar Ablation-Ex Vivo Porcine Model.

Journal of arrhythmia·2026
Same author

Context-Aware Sentence Classification of Radiology Reports Using Synthetic Data: Development and Validation Study.

Journal of medical Internet research·2026
Same author

Palliative care in cardiovascular medicine.

European heart journal·2026
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 6, 2025

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

417

Identifying Ventricular Dysfunction Indicators in Electrocardiograms via Artificial Intelligence-Driven Analysis.

Hisaki Makimoto1,2,3,4, Takayuki Okatani2, Masanori Suganuma2

  • 1Cardiovascular Centre, Jichi Medical University, Shimotsuke 329-0498, Japan.

Bioengineering (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence can detect ventricular dysfunction using electrocardiograms (ECGs). Dual-beat ECGs and specific waveform segments, particularly from the P- to T-wave, offer the most precise classification.

Keywords:
artificial intelligenceelectrocardiogramventricular dysfunction

More Related Videos

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

3.6K
Ablation of Ischemic Ventricular Tachycardia Using a Multipolar Catheter and 3-dimensional Mapping System for High-density Electro-anatomical Reconstruction
06:57

Ablation of Ischemic Ventricular Tachycardia Using a Multipolar Catheter and 3-dimensional Mapping System for High-density Electro-anatomical Reconstruction

Published on: January 31, 2019

14.6K

Related Experiment Videos

Last Updated: Jun 6, 2025

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

417
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

3.6K
Ablation of Ischemic Ventricular Tachycardia Using a Multipolar Catheter and 3-dimensional Mapping System for High-density Electro-anatomical Reconstruction
06:57

Ablation of Ischemic Ventricular Tachycardia Using a Multipolar Catheter and 3-dimensional Mapping System for High-density Electro-anatomical Reconstruction

Published on: January 31, 2019

14.6K

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Artificial intelligence (AI) shows promise in identifying ventricular dysfunction from electrocardiograms (ECGs).
  • Specific ECG waveforms indicative of cardiac dysfunction remain largely undefined.
  • Accurate, non-invasive methods for assessing ventricular function are crucial in clinical practice.

Purpose of the Study:

  • To develop and validate AI models for detecting reduced left ventricular ejection fraction (LVEF) using ECG data.
  • To identify specific ECG configurations and waveform segments most effective for diagnosing ventricular dysfunction.
  • To determine the diagnostic utility of different ECG leads and segments for assessing cardiac function.

Main Methods:

  • Analysis of ECG and echocardiography data from 17,422 patients in Japan and Germany.
  • Development of 10-layer convolutional neural networks (CNNs) for LVEF classification (<50%).
  • Evaluation of model performance using four-fold cross-validation across various ECG configurations (3s strips, single-beat, two-beat overlay) and segments (PQRST, QRST, P, QRS, PQRS).

Main Results:

  • Two-beat ECG configurations demonstrated superior performance in detecting ventricular dysfunction compared to single-beat and 3s strip models.
  • Single-beat models identified limb leads I and aVR as particularly indicative of dysfunction.
  • ECG segments from the QRS complex to the T-wave were most informative, with P-wave segments further enhancing diagnostic accuracy.

Conclusions:

  • Dual-beat ECG analysis, processed by AI, enables highly precise classification of ventricular function.
  • Specific ECG segments, notably from the P-wave through the T-wave, are more effective for assessing ventricular dysfunction.
  • ECG leads I and aVR exhibit significant diagnostic utility for evaluating ventricular dysfunction.