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

Pulse rhythm01:30

Pulse rhythm

947
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...
947
Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

1.3K
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 heart...
1.3K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

8.7K
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...
8.7K
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

129
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...
129
Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

146
Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
146
Dysrhythmias I: Introduction01:15

Dysrhythmias I: Introduction

178
Dysrhythmias refers to abnormalities in the heart's rhythm. They result from disruptions in the heart's electrical conduction system, which includes the sinoatrial(SA)node, atrioventricular(AV) node, the bundle of His, bundle branches, and Purkinje fibers.Definition and PathophysiologyDysrhythmias result from disorders of impulse formation, impulse conduction, or both. The heart contains specialized cells in the sinoatrial node, atrioventricular node, and the bundle of His and Purkinje fibers...
178

You might also read

Related Articles

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

Sort by
Same author

Electrophysiological and Molecular Features of Remdesivir-Induced Cardiac Toxicity in Male and Female Guinea Pigs.

International journal of molecular sciences·2026
Same author

The 'myocardial work index' does not measure myocardial work.

European heart journal. Imaging methods and practice·2026
Same author

Deep learning-based non-contrast cine CMR for optimized prediction of left ventricular adverse remodeling after ST-elevation myocardial infarction.

International journal of cardiology·2026
Same author

Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.

Briefings in bioinformatics·2026
Same author

Mechanisms of rectified gap junctional coupling enhancing pacemaking activity of biologically engineered pacemaker cells.

NPJ systems biology and applications·2026
Same author

The Dual Nature of Sinoatrial Node Remodelling in Athletes: A Systematic Review of Electrophysiological Adaptations and the Pathological Tipping Point.

International journal of molecular sciences·2025

Related Experiment Video

Updated: Sep 25, 2025

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

3.9K

Generalizable Beat-by-Beat Arrhythmia Detection by Using Weakly Supervised Deep Learning.

Yang Liu1, Qince Li1,2, Runnan He2

  • 1School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin, China.

Frontiers in Physiology
|April 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a weakly supervised deep learning framework (WSDL-AD) for accurate beat-by-beat arrhythmia detection using electrocardiogram (ECG) data. The WSDL-AD model significantly improves the detection of ectopic beats, outperforming existing methods.

Keywords:
cardiac arrhythmiaelectrocardiogramgeneralization abilityheartbeat classificationweakly supervised learning

More Related Videos

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.4K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

2.2K

Related Experiment Videos

Last Updated: Sep 25, 2025

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

3.9K
Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.4K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

2.2K

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Beat-by-beat arrhythmia detection in ambulatory ECG monitoring is crucial but challenging due to the demanding nature of manual analysis and limitations in current automated methods.
  • Existing automatic arrhythmia detection systems struggle with generalization due to insufficient large-sample, finely-annotated ECG data for training.
  • The lack of detailed beat-level annotations in large ECG datasets hinders the development of robust and widely applicable arrhythmia detection models.

Purpose of the Study:

  • To develop a weakly supervised deep learning framework for arrhythmia detection (WSDL-AD) that enables fine-grained, beat-by-beat analysis using coarsely annotated ECG data.
  • To improve the generalization ability of arrhythmia detection models by leveraging large datasets with less granular labels.
  • To enhance the accuracy and stability of heartbeat classification under weak supervision through novel techniques.

Main Methods:

  • Proposed a weakly supervised deep learning framework (WSDL-AD) integrating heartbeat and recording classification for end-to-end training with only recording-level labels.
  • Employed techniques such as knowledge-based features, masked aggregation, and supervised pre-training to enhance weak supervision for heartbeat classification.
  • Trained the WSDL-AD model for detecting ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) on multiple large-sample, coarsely annotated datasets.

Main Results:

  • The WSDL-AD model demonstrated significant improvements in detection accuracy compared to state-of-the-art supervised learning methods.
  • Achieved an 8%-290% improvement in F1 score for supraventricular ectopic beats detection and a 4%-11% improvement for ventricular ectopic beats detection.
  • Validated performance on three independent benchmarks according to AAMI recommendations, confirming enhanced generalization and fine detection granularity.

Conclusions:

  • The WSDL-AD framework effectively utilizes abundant coarsely labeled ECG data to achieve superior generalization ability compared to previous methods.
  • The proposed approach retains fine detection granularity, making it suitable for clinical and telehealth applications.
  • This weakly supervised method offers a promising solution for improving the efficiency and accuracy of ambulatory ECG analysis for cardiac arrhythmias.