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 Experiment Videos

An arrhythmia classification system based on the RR-interval signal.

M G Tsipouras1, D I Fotiadis, D Sideris

  • 1Deparment of Computer Science, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina Campus, P.O. Box 1186, GR 45110 Ioannina, Greece.

Artificial Intelligence in Medicine
|April 7, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Position Paper: Artificial Intelligence in Medical Image Analysis: Advances, Clinical Translation, and Emerging Frontiers.

IEEE journal of biomedical and health informatics·2025
Same author

Physicochemical and functional assessment of messenger RNA 5'Cap-end impurities under forced degradation conditions.

Molecular therapy. Nucleic acids·2025
Same author

CARDIOCARE: An integrated platform for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023
Same author

BEM evaluation of surface octahedral strains and internal strain gradients in 3D-printed scaffolds used for bone tissue regeneration.

Journal of the mechanical behavior of biomedical materials·2021
Same author

On-Body Sensor Position Identification with a Simple, Robust and Accurate Method, Validated in Patients with Parkinson's Disease.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020
Same author

Histological validation of the automated caries detection system (ACDS) in classifying occlusal caries with the ICDAS II system in vitro.

European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry·2018

This study introduces a novel method for classifying heartbeats and detecting arrhythmias using only the RR-interval signal from ECGs. The approach achieves high accuracy in both beat and episode classification, offering a simpler alternative to complex methods.

Area of Science:

  • Cardiology
  • Biomedical Signal Processing
  • Artificial Intelligence in Healthcare

Background:

  • Cardiac arrhythmias are a significant cause of morbidity and mortality.
  • Accurate detection and classification of arrhythmias are crucial for timely intervention.
  • Current methods often rely on complex analysis of full ECG signals.

Purpose of the Study:

  • To propose a knowledge-based method for classifying individual arrhythmic beats.
  • To develop a system for detecting and classifying arrhythmic episodes.
  • To utilize solely the RR-interval signal for these classifications.

Main Methods:

  • A three-RR-interval sliding window approach for beat classification.
  • Classification of four beat categories: normal, premature ventricular contractions, ventricular flutter/fibrillation, and 2nd-degree heart block.

Related Experiment Videos

  • A knowledge-based deterministic automaton utilizing beat classifications for episode detection and classification of six rhythm types.

    • Main Results:

    • The method was evaluated using the MIT-BIH arrhythmia database.
    • Achieved 98% accuracy for arrhythmic beat classification.
    • Achieved 94% accuracy for arrhythmic episode detection and classification.

    Conclusions:

    • The proposed method effectively classifies arrhythmias using only the RR-interval signal.
    • This approach offers a simplified yet high-performing alternative to more complex methods.
    • The results demonstrate the potential for efficient and accurate arrhythmia analysis.