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

Patients classification on weaning trials using neural networks and wavelet transform.

Carlos Arizmendi1, Juan Viviescas1, Hernando González1

  • 1Control & Mecatrónica Research Group, Universidad Autónoma de Bucaramanga, Bucaramanga, Colombia.

Studies in Health Technology and Informatics
|July 8, 2014
PubMed
Summary

Related Concept Videos

Classification of Signals01:30

Classification of Signals

1.5K
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...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Spectrum analysis of non-uniformly sampled signals<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 author

Spatiotemporal dispersion of DENV-1 genotype V in Western Colombia.

Virus evolution·2025
Same author

Aortoesophageal Fistula in a Patient With Recent Endoscopic Balloon Dilation and History of Esophageal Myotomies for Achalasia.

Gastro hep advances·2025
Same author

Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence.

International journal of environmental research and public health·2023
Same author

Design of a Classifier to Determine the Optimal Moment of Weaning of Patients undergoing to the T-tube Test.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2022
Same author

Tackling the Diagnosis: Solid Pseudopapillary Tumor of the Pancreas in a Young Man.

Gastroenterology research·2019

Determining when to stop mechanical ventilation is crucial. This study used Wavelet Transform (WT) and Neural Networks (NN) with Genetic Algorithms (GA) to classify patients, achieving 77% accuracy in predicting successful weaning.

Area of Science:

  • Critical Care Medicine
  • Biomedical Engineering
  • Signal Processing

Background:

  • Mechanical ventilation is a life support measure for critically ill patients.
  • Weaning from mechanical ventilation requires careful assessment to distinguish between patients who can breathe spontaneously and those who cannot.
  • Failure to accurately assess weaning readiness can lead to prolonged ventilation, increasing risks and costs.

Purpose of the Study:

  • To develop and validate a classifier to accurately predict patient success in weaning from mechanical ventilation.
  • To identify key respiratory pattern features indicative of successful spontaneous breathing trials.
  • To optimize the classification model using advanced feature selection techniques.

Main Methods:

  • Respiratory patterns were analyzed using time series data.

Related Experiment Videos

  • Wavelet Transform (WT) was employed for signal decomposition and feature extraction.
  • Neural Networks (NN) were utilized as the core classification algorithm.
  • Genetic Algorithms (GA) and Forward Selection were applied for optimal feature selection from an initial set of 14 variables.

    • Main Results:

    • A classification model combining NN and GA achieved a high performance rate.
    • The optimized model successfully classified 77.00±0.06% of patients.
    • Feature selection reduced the number of variables to 6, simplifying the model without compromising accuracy.

    Conclusions:

    • The developed classifier, utilizing WT, NN, and GA, demonstrates significant potential for improving the assessment of mechanical ventilation weaning.
    • Accurate prediction of weaning success can aid clinicians in intensive care units, potentially reducing ventilation duration and associated complications.
    • The identified subset of 6 respiratory variables provides a concise yet effective basis for clinical decision-making in the weaning process.