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

Support vector machine-based expert system for reliable heartbeat recognition.

Stanislaw Osowski1, Linh Tran Hoai, Tomasz Markiewicz

  • 1Institute of Theory of Electrical Engineering and Electrical Measurements, Warsaw University of Technology, 00-661 Warsaw, Poland. sto@iem.pw.edu.pl

IEEE Transactions on Bio-Medical Engineering
|April 10, 2004
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

Revision Total Hip Arthroplasty Using a Custom Monoflange Acetabular Component for Severe Bone Loss and Pelvic Discontinuity Due to Acetabular Component Loosening: A Case Report.

Cureus·2024
Same author

Localization of spleen and kidney organs from CT scans based on classification of slices in rotational views.

Scientific reports·2023
Same author

siRNA-Mediated MELK Knockdown Induces Accelerated Wound Healing with Increased Collagen Deposition.

International journal of molecular sciences·2023
Same author

The next level of preoperative arteries computed tomography angiography.

Kardiologia polska·2021
Same author

A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2021
Same author

Three-Dimensional Volumetric Renal Reconstruction Based on Geometrical Coefficients.

Studies in health technology and informatics·2020
Same journal

Enhancing Volumetric Imaging in Linear-Array Photoacoustic Tomography: multiview fusion with deep learning.

IEEE transactions on bio-medical engineering·2026
Same journal

Robust Rule-based Heuristic Assistance Strategy for a Semi-Active Shoulder Exoskeleton Used in Overhead Work.

IEEE transactions on bio-medical engineering·2026
Same journal

Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

IEEE transactions on bio-medical engineering·2026
Same journal

Systematic Evaluation of Hip Exoskeleton Assistance Parameters for Enhancing Gait Stability During Ground Slip Perturbations.

IEEE transactions on bio-medical engineering·2026
Same journal

SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

IEEE transactions on bio-medical engineering·2026
Same journal

Modeling Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework.

IEEE transactions on bio-medical engineering·2026
See all related articles

This study introduces a reliable expert system for heartbeat recognition using support vector machines (SVM) and advanced ECG waveform analysis. The novel approach accurately identifies 13 heart rhythm types, enhancing diagnostic capabilities.

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Accurate heartbeat recognition is crucial for diagnosing cardiac conditions.
  • Existing electrocardiogram (ECG) analysis methods face challenges in reliability and complexity.
  • Expert systems offer a promising avenue for automated and precise cardiac rhythm classification.

Purpose of the Study:

  • To develop a robust expert system for reliable heartbeat recognition using ECG data.
  • To integrate advanced signal processing techniques with machine learning for improved classification accuracy.
  • To evaluate the performance of the proposed system in identifying diverse heart rhythm types.

Main Methods:

  • Utilized support vector machine (SVM) in classification mode for heartbeat recognition.

Related Experiment Videos

  • Applied two distinct preprocessing methods: higher-order statistics (HOS) and Hermite characterization of the QRS complex.
  • Combined two neural classifiers, integrating them into a final expert system using the least mean square (LMS) method for weighted voting.

    • Main Results:

    • Achieved reliable recognition of 13 different heart rhythm types based on ECG waveforms.
    • Demonstrated the advantage of the proposed combined classifier approach over individual methods.
    • Validated the system's effectiveness through numerical experiments on ECG data.

    Conclusions:

    • The proposed expert system offers a reliable and advantageous solution for heartbeat recognition.
    • The integration of SVM with HOS and Hermite characterization provides a powerful tool for ECG analysis.
    • This approach holds significant potential for improving automated cardiac diagnostics.