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Digitization and Analysis of Capnography Using Image Processing Technique.

Bhuwaneswaran Vijayam1, Eko Supriyanto1,2, M B Malarvili1

  • 1School of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia (UTM), Skudai, Malaysia.

Frontiers in Digital Health
|November 15, 2021
PubMed
Summary

Capnography, the graphical analysis of carbon dioxide (CO2) expiration, is expanding beyond anesthesia. Image processing offers a viable method for digitizing capnography data, enabling efficient analysis for various medical applications.

Keywords:
EtCO2capnographycapnometryend tidal carbon dioxidetime based capnography

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Area of Science:

  • Medical Technology
  • Physiology
  • Data Analysis

Background:

  • Capnometry studies carbon dioxide expiration, with capnography providing its graphical representation.
  • Capnography's applications are expanding into metabolism, circulation, and respiratory mechanics beyond anesthesia and emergency medicine.
  • Accurate calculation of capnographic parameters is crucial, yet a gold standard device is lacking.

Purpose of the Study:

  • To evaluate the feasibility of using image processing techniques for capnography digitization.
  • To assess the reliability and efficiency of a developed algorithm for analyzing capnographic parameters.
  • To address the growing need for data analysis and interpretation of capnographic parameters in diverse clinical settings.

Main Methods:

  • Development of a digitization algorithm for capnographic data using image processing.
  • Testing the algorithm with eight identical breath waves analyzed by four independent investigators.
  • Analysis of calculated capnographic parameters for inter-investigator variability and calculability.

Main Results:

  • The developed algorithm demonstrated no significant differences in parameter values between investigators.
  • While most parameters were reliably calculated, a few related parameters remained incalculable.
  • The image processing technique proved to be fast, convenient, easy to use, and efficient for capnography digitization.

Conclusions:

  • Digitization of capnography via image processing presents a promising and efficient option for data analysis.
  • The proposed algorithm requires further refinement and large-scale testing for broader clinical adoption.
  • This technique supports the expanding use of capnography in diagnosing both lung and non-lung-related diseases.