Assessing and minimizing the impact of an additive disturbance for human respiratory system impedance estimation

Abstract

Respiratory Oscillometry is a promising technique to provide information to medical practitioners on the respiratory system of a patient in a non-invasive fashion. It focuses on identifying the respiratory impedance between two signals: the air pressure and flow at the mouth opening. However, for conscious patients or lightly sedated ventilated patients, their respiratory effort such as breathing acts as a disturbance to the parameter estimation procedure. This paper is an extension to previous research published at the IFAC 2023 World Congress (Marchal et al., 2023) that proposed a method to estimate and remove this breathing disturbance using Gaussian Process Regression in the frequency domain. In this extension, Monte Carlo simulations are performed to validate the approach and to compare it to the Local Polynomial Method for breathing patients. In addition, measurements carried out on a lung emulator in a pressure-support ventilation mode provide further evidence of the method’s effectiveness at dealing with the disturbance experienced for ventilated patients. This is a step towards treating both breathing and ventilated patients using the same technique.