摘要：AbstractOptimizing the positive end-expiratory pressure remains challenging for any clinician treating a patient with acute respiratory distress syndrome. This paper presents an approach to automate a PEEP titration maneuver and identify the best PEEP according to maximal compliance. The respiratory system was modeled by a single-compartment model, and parameters were estimated using multiple linear regression. A classifier identified the best PEEP using the scaled relative change in compliance between PEEP levels based on empirical data from previous manual PEEP titrations. An experimental system allows the in vivo testing of the automated PEEP titration, including additional safety measures. The complete system was tested in a single animal experiment and correctly identified the best PEEP. The introduced system is a step closer towards an automated, standardized PEEP optimization and closed-loop control of mechanical ventilation.
关键词：Keywordscritical carefeedback controlmechanical ventilationdecision support systemsartificial intelligence
