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

Computerized electrocardiogram diagnosis: fuzzy approach.

R Degani1

  • 1LADSEB-CNR, Padova, Italy.

Methods of Information in Medicine
|November 1, 1992
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

Limnological and ecological methods: approaches, and sampling strategies for middle Xingu River in the area of influence of future Belo Monte Power Plant.

Brazilian journal of biology = Revista brasleira de biologia·2015
Same author

A standard communications protocol for computerized electrocardiography.

Journal of electrocardiology·1992
Same author

The diagnostic performance of computer programs for the interpretation of electrocardiograms.

The New England journal of medicine·1991
Same author

Common standards for quantitative electrocardiography: goals and main results. CSE Working Party.

Methods of information in medicine·1990
Same author

Evaluation of ECG interpretation systems: signal analysis.

Methods of information in medicine·1990
Same author

Methodology of ECG interpretation in the Padova program.

Methods of information in medicine·1990

This study explores computerized electrocardiographic (ECG) signal analysis. A fuzzy set model addresses challenges in ECG classification due to biological variability and vague diagnostic criteria.

Area of Science:

  • Biomedical Engineering
  • Medical Informatics
  • Signal Processing

Background:

  • Computerized analysis of electrocardiographic (ECG) signals presents significant challenges.
  • Biological variability and lack of standardized diagnostic criteria complicate ECG data classification.
  • Existing diagnostic methods include statistical and deterministic approaches.

Purpose of the Study:

  • To investigate advanced methods for computerized electrocardiographic signal analysis.
  • To address the complexity of ECG classification arising from biological variability and undefined diagnostic standards.
  • To present a novel model for ECG classification utilizing fuzzy set theory.

Main Methods:

  • Description of two fundamental diagnostic process methods: statistical and deterministic models.

Related Experiment Videos

  • Illustration of a specific model for ECG classification.
  • Application of fuzzy set formalism to handle imprecise knowledge of cardiac system states and vague pathological class definitions.

    • Main Results:

    • The proposed fuzzy set model offers a framework for managing uncertainty in ECG analysis.
    • This approach provides a method to classify ECG signals despite inherent biological variability.
    • The model accommodates vague definitions of pathological cardiac conditions.

    Conclusions:

    • Fuzzy set formalism is a suitable tool for addressing imprecision and vagueness in ECG classification.
    • The developed model enhances the computerized analysis of electrocardiographic signals.
    • This work contributes to more robust and accurate automated diagnostic systems for cardiac conditions.