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

Dysrhythmias II: Classification of Tachyarrhythmias01:28

Dysrhythmias II: Classification of Tachyarrhythmias

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...
Dysrhythmias I: Introduction01:15

Dysrhythmias I: Introduction

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...
Seizures: Classification01:13

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

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...
Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase of...

You might also read

Related Articles

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

Sort by
Same author

Reconfigurable aerial load transportation by multiple agents: An adaptive sliding mode approach for robust tension control.

ISA transactions·2026
Same author

Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots.

Sensors (Basel, Switzerland)·2025
Same author

Null space-based behavioral control applied to a formation of two quadrotors transporting a cable suspended load.

ISA transactions·2024
Same author

Null space-based control with gain modulation applied to a MARV in backward movement.

ISA transactions·2024
Same author

Reduced heart-rate variability and increased risk of hypertension-a prospective study of the ELSA-Brasil.

Journal of human hypertension·2021
Same author

Diabetes and subclinical hypothyroidism on heart rate variability.

European journal of clinical investigation·2020
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles

Related Experiment Video

Updated: May 25, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
08:22

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Premature Ventricular beat classification using a dynamic Bayesian Network.

Lorena S C de Oliveira1, Rodrigo V Andreão, Mario Sarcinelli-Filho

  • 1Science e Technology Institute, Federal University of Vales do Jequitinhonha e Mucuri, Teófilo Otoni, Minas Gerais, Brazil. lorenasco@yahoo.com.br

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary
This summary is machine-generated.

This study demonstrates dynamic Bayesian Networks effectively classify heartbeats in long-term ECGs. This approach improves accuracy for detecting conditions like Premature Ventricular Contractions (PVCs).

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

Related Experiment Videos

Last Updated: May 25, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
08:22

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

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

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Long-term electrocardiogram (ECG) analysis is crucial for diagnosing cardiac conditions.
  • Accurate heartbeat classification is essential for effective cardiac monitoring.
  • Existing methods may not fully leverage temporal information in ECG data.

Purpose of the Study:

  • To evaluate the dynamic Bayesian Network (DBN) framework for classifying heartbeats in long-term ECG records.
  • To compare the performance of DBNs against static Bayesian Networks for this task.
  • To develop a two-layer Decision Support System for automated heartbeat classification.

Main Methods:

  • Implementing a two-layer Decision Support System for ECG analysis.
  • Utilizing a first layer for heartbeat segmentation.
  • Employing a second layer for classifying heartbeats as Premature Ventricular Contraction (PVC) or Other using static and dynamic Bayesian Networks.
  • Testing the system on the MIT-BIH database.

Main Results:

  • The dynamic Bayesian Network approach outperformed static Bayesian Networks.
  • Achieved 95% sensitivity in heartbeat classification.
  • Obtained 98% positive predictivity for identifying cardiac events.
  • Demonstrated the benefit of incorporating temporal relationships for increased classification certainty.

Conclusions:

  • Dynamic Bayesian Networks offer a viable and effective framework for automated heartbeat classification in long-term ECGs.
  • Considering the temporal dynamics of heartbeats significantly enhances classification accuracy.
  • The proposed DBN-based Decision Support System shows high potential for clinical application in cardiac monitoring.