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

Ventilation mode recognition using artificial neural networks

M A Leon1, F L Lorini

  • 1Medical Informatics Group, University of Missouri-Columbia 65211, USA.

Computers and Biomedical Research, an International Journal
|February 11, 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

Left ventricular systolic function evaluated by strain echocardiography and relationship with mortality in patients with severe sepsis or septic shock: a systematic review and meta-analysis.

Critical care (London, England)·2018
Same author

A standardized protocol for the perioperative management of myasthenia gravis patients. Experience with 110 patients.

Acta anaesthesiologica Scandinavica·2011
Same author

Fungal colonization and/or infection in non-neutropenic critically ill patients: results of the EPCAN observational study.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology·2008
Same author

Early diagnosis of candidiasis in non-neutropenic critically ill patients.

The Journal of infection·2004
Same author

A prospective, randomized study in critically ill patients using the Oligon Vantex catheter.

The Journal of hospital infection·2003
Same author

The utility of serology in diagnosing candidosis in non-neutropenic critically ill patients.

Mycoses·2001
Same journal

Wavelet domain nonlinear filtering for evoked potential signal enhancement.

Computers and biomedical research, an international journal·2001
Same journal

Time-frequency analysis of the RT and RR variability to stratify hypertrophic cardiomyopathy patients.

Computers and biomedical research, an international journal·2001
Same journal

Controlled auxotonic twitch in papillary muscle: a new computer-based control approach.

Computers and biomedical research, an international journal·2001
Same journal

Controlling for chance agreement in the validation of medical expert systems with no gold standard: PNEUMON-IA and RENOIR revisited.

Computers and biomedical research, an international journal·2001
Same journal

Artificial neural networks compared to factor analysis for low-dimensional classification of high-dimensional body fat topography data of healthy and diabetic subjects.

Computers and biomedical research, an international journal·2000
Same journal

On the parallelization of linkmap from the LINKAGE/FASTLINK package.

Computers and biomedical research, an international journal·2000
See all related articles

Artificial neural networks accurately identify breathing support modes using airflow and airway pressure signals. This technology shows promise for advanced respiratory pattern recognition in clinical settings.

Area of Science:

  • Biomedical Engineering
  • Computational Neuroscience
  • Respiratory Physiology

Background:

  • Mechanical ventilation is crucial for patients with respiratory failure.
  • Distinguishing between spontaneous and pressure support ventilation modes is essential for patient management.
  • Current methods for mode identification can be labor-intensive and prone to error.

Purpose of the Study:

  • To evaluate the efficacy of artificial neural networks in identifying spontaneous and pressure support ventilation modes.
  • To determine the optimal input data (flow, pressure, or both) for neural network-based mode recognition.
  • To explore the potential of artificial intelligence in automated respiratory pattern analysis.

Main Methods:

  • Waveform data (flow and airway pressure) were collected from 13 patients under general anesthesia.

Related Experiment Videos

  • The inspiratory phase of each breath was extracted and normalized.
  • Artificial neural networks were trained and tested using varying combinations of flow and pressure signals.

    • Main Results:

    • Networks utilizing both flow and pressure waveforms achieved 100% accuracy in identifying ventilation modes.
    • Networks using only pressure signals demonstrated high accuracy (97%).
    • Networks using only flow signals achieved 78% accuracy.

    Conclusions:

    • Artificial neural networks are highly effective for recognizing breathing support modes.
    • The integration of both flow and pressure data maximizes recognition accuracy.
    • This approach supports the application of neural networks in broader respiratory pattern recognition challenges.