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Differential Pressure Spirometry for Mechanical Ventilation Using Dichotomic Search.

Noe A Rodriguez-Olivares1,2,3, Luciano Nava-Balanzar1, Leonardo Barriga-Rodriguez1

  • 1Division of Electrical Engineering and ElectronicsCenter for Engineering and Industrial Development (CIDESI) Queretaro 76125 Mexico.

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Summary

A new method accurately estimates breathing flow for mechanical ventilators using a D-lite sensor and dichotomous search, improving tidal volume calculation. This technique enhances patient safety during invasive mechanical ventilation (IMV).

Keywords:
Binary search algorithmCOVID-19D-liteFiO₂dichotomic searchflow estimationmechanical ventilationspirometrytidal volumeventuri

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Area of Science:

  • Biomedical Engineering
  • Respiratory Physiology

Background:

  • Accurate flow estimation is critical for invasive mechanical ventilation (IMV) to trigger assistance and calculate tidal volume.
  • The COVID-19 pandemic highlighted limitations in direct spirometry measurements on many mechanical ventilators.
  • Existing flow estimation methods using D-lite sensors have limitations in real-world conditions due to regression model constraints.

Purpose of the Study:

  • To develop and validate a novel, efficient, and accurate flow estimation method for IMV.
  • To adapt the dichotomous search algorithm for improved flow estimation using a D-lite sensor.
  • To provide technical recommendations for safe implementation of the method in embedded systems.

Main Methods:

  • Utilized a D-lite sensor, a fraction of inspired oxygen (FiO2) cell, and two pressure sensors.
  • Implemented a dichotomous search algorithm for flow estimation, replacing conventional regression methods.
  • Validated the method experimentally for tidal volume calculation within a +/-10% error range.
  • Performed flow calculations under various ambient conditions and compared results with gas analyzers.

Main Results:

  • The novel method demonstrated a fast calibration process and good low-flow estimation capabilities.
  • Flow estimation achieved an average relative error of up to 4.86% when compared with gas analyzers.
  • The method's accuracy in computing tidal volume met the +/-10% error requirement.
  • Consideration of FiO2 percentage and effective low-flow estimation enhance its utility in clinical settings.

Conclusions:

  • The developed flow estimation method offers a reliable and efficient solution for IMV, particularly when direct measurement is unavailable.
  • The use of a dichotomous search algorithm provides advantages in calibration speed and computational time.
  • The method's accuracy and consideration of gas mixture composition make it suitable for real-world ventilation scenarios.
  • Technical guidelines are provided for the safe integration of this method into embedded systems for IMV devices.