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

ANN-based QRS-complex analysis of ECG

G Vijaya1, V Kumar, H K Verma

  • 1Department of ECE, REC, Warangal, India.

Journal of Medical Engineering & Technology
|July 29, 1998
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

Rare occurrence of intracranial epidermoid tumor in an unusual location with atypical radiological features.

Asian journal of neurosurgery·2015
Same author

Changes in nerve growth factor level and symptom severity following antibiotic treatment for refractory overactive bladder.

International urogynecology journal·2013
Same author

Is there a role for synthetic meshes or biological grafts in vaginal prolapse surgery?

Women's health (London, England)·2010
Same author

Mesocolic hernia: an unusual internal hernia.

Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association·2009
Same author

SVM based system for classification of microcalcifications in digital mammograms.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference·2007
Same author

Artifacts and noise removal in electrocardiograms using independent component analysis.

International journal of cardiology·2007

This study introduces an artificial neural network (ANN) for accurate QRS complex detection in electrocardiogram (ECG) signals. The ANN achieves 99.11% sensitivity, improving ECG analysis for medical diagnosis.

Area of Science:

  • Biomedical Engineering
  • Computational Cardiology
  • Artificial Intelligence in Medicine

Background:

  • Accurate QRS complex detection is crucial for ECG analysis and diagnosis.
  • Traditional rule-based methods have limitations in handling diverse ECG signals.
  • Artificial Neural Networks (ANNs) offer a promising alternative for complex signal processing.

Purpose of the Study:

  • To develop and validate an ANN for reliable QRS complex detection in normal and abnormal ECGs.
  • To analyze and measure morphological components of the QRS complex across all 12 ECG leads.
  • To compare ANN-based analysis with expert visual measurements.

Main Methods:

  • Development of an ANN model for QRS complex detection.
  • Training the ANN using the backpropagation algorithm on over 100 ECGs from the CSE Data Set-3.

Related Experiment Videos

  • Testing the trained ANN on the entire CSE Data Set-3.
  • Implementing QRS detection and analysis software in C-language on a PC-AT.

    • Main Results:

    • The ANN achieved a sensitivity of 99.11% for QRS complex detection.
    • Morphological components of the QRS complex were analyzed and measured.
    • Results were validated against CSE multilead measurement results and expert visual measurements.
    • Software implementation demonstrated successful PC-AT operation.

    Conclusions:

    • The developed ANN provides a highly sensitive and accurate method for QRS complex detection in ECGs.
    • The ANN-based approach facilitates detailed morphological analysis of the QRS complex.
    • This method shows strong agreement with established measurement techniques and expert assessments, enhancing diagnostic capabilities.