Skip to main navigation Skip to search Skip to main content

针对有自主呼吸慢阻肺患者的无创动态呼吸

Translated title of the contribution: Noninvasive dynamic respiratory mechanics parameter estimation for chronic obstructive pulmonary patients with spontaneous breathing
  • Beihang University

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: To study the method of estimating noninvasive dynamic respiratory mechanics parameters for patients with chronic obstructive pulmonary disease (COPD). Methods: By simplifying the human respiratory system into a first order single compartment model and setting constraints based on optimization method, the respiratory system resistance and compliance of COPD patients were estimated. Results: By using the model and setting the constraint conditions in the simulation experiment, the respiratory system resistance and compliance of COPD patients with spontaneous breathing could be estimated, and the results were relatively accurate (within 5% error). The estimated result could be obtained by data of one respiratory cycle within three respiratory cycles, which could meet the requirements of dynamic monitoring data. Conclusions: Based on optimization method, the noninvasive dynamic evaluation on respiratory resistance and compliance of COPD patients were carried out in simulation experiments and proved to be feasible for further clinical trials. The research findings could help doctors to monitor the resistance and compliance changes of COPD patients in real time after clinical trial, and provided references for diagnosis and treatment of COPD.

Translated title of the contributionNoninvasive dynamic respiratory mechanics parameter estimation for chronic obstructive pulmonary patients with spontaneous breathing
Original languageChinese (Traditional)
Pages (from-to)404-410
Number of pages7
JournalYiyong Shengwu Lixue/Journal of Medical Biomechanics
Volume34
Issue number4
DOIs
StatePublished - 1 Aug 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Fingerprint

Dive into the research topics of 'Noninvasive dynamic respiratory mechanics parameter estimation for chronic obstructive pulmonary patients with spontaneous breathing'. Together they form a unique fingerprint.

Cite this