Noninvasive Breathing Effort Estimation of Mechanically Ventilated Patients Using Sparse Optimization

Mechanical ventilators facilitate breathing for patients who cannot breathe (sufficiently) on their own. The aim of this paper is to estimate relevant lung parameters and the spontaneous breathing effort of a ventilated patient that help keeping track of the patient’s clinical condition. A key challenge is that estimation using the available sensors for typical model structures results in a non-identifiable parametrization. A sparse optimization algorithm to estimate the lung parameters and the patient effort, without interfering with the patient’s treatment, using an $\ell _{1}$-regularization approach is presented. It is confirmed that accurate estimates of the lung parameters and the patient effort can be retrieved through a simulation case study and an experimental case study.