A Model-based Approach to Generating Annotated Pressure Support Waveforms

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

During pressure support ventilation, every breath is triggered by the patient. Mismatches between the patient and the ventilator are called asynchronies. It has been reported that large numbers of asynchronies may be harmful and may lead to increased mortality. Automatic asynchrony detection and classification, with subsequent feedback to clinicians, will improve lung ventilation and, possibly, patient outcome. Machine learning techniques have been used to detect asynchronies. However, large, diverse and high-quality training and verification data sets are needed. In this work, we propose a model for generating a large, realistic, labeled, synthetic dataset for training and testing machine learning algorithms to detect a wide variety of asynchrony types. Next to a morphological evaluation of the obtained waveforms, validation of the proposed model includes a test with a machine learning algorithm trained on clinical data.
Original languageEnglish
Title of host publication2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
PublisherInstitute of Electrical and Electronics Engineers
Pages4188-4191
Number of pages4
ISBN (Electronic)978-1-7281-1179-7
ISBN (Print)978-1-7281-1180-3
DOIs
Publication statusPublished - 9 Dec 2021
Event43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
- Virtual, Mexico
Duration: 1 Nov 20215 Nov 2021
Conference number: 43
https://embc.embs.org/2021/

Conference

Conference43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Abbreviated titleEMBC 2021
Country/TerritoryMexico
Period1/11/215/11/21
Internet address

Keywords

  • Training
  • Ventilators
  • Machine learning algorithms
  • Biological system modeling
  • Lung
  • Machine learning
  • Ventilation
  • Humans
  • Machine Learning
  • Positive-Pressure Respiration
  • Respiration, Artificial
  • Ventilators, Mechanical
  • Respiration

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