Print Email Facebook Twitter Automatic patient-ventilator asynchrony detection framework using objective asynchrony definitions Title Automatic patient-ventilator asynchrony detection framework using objective asynchrony definitions Author van de Kamp, Lars (Eindhoven University of Technology; Demcon Life Sciences and Health) Reinders, Joey (Demcon Life Sciences and Health) Hunnekens, Bram (Demcon Life Sciences and Health) Oomen, T.A.E. (TU Delft Team Jan-Willem van Wingerden; Eindhoven University of Technology) van de Wouw, Nathan (Eindhoven University of Technology) Date 2024 Abstract Patient-ventilator asynchrony is one of the largest challenges in mechanical ventilation and is associated with prolonged ICU stay and increased mortality. The aim of this paper is to automatically detect and classify the different types of patient-ventilator asynchronies during a patient's breath using the typically available data on commercially available ventilators. This is achieved by a detection and classification framework using an objective definition of asynchrony and a supervised learning approach. The achieved detection performance of the near-real time framework on a clinical dataset is a significant improvement over current clinical practice, therewith and, this framework has the potential to significantly improve the patient comfort and treatment outcomes. Subject ClassificationDetectionMechanical ventilationPatient-ventilator asynchronyRecurrent neural networksSupervised learning To reference this document use: http://resolver.tudelft.nl/uuid:5800bca7-69b7-4c26-bde5-414940de6ff3 DOI https://doi.org/10.1016/j.ifacsc.2023.100236 ISSN 2468-6018 Source IFAC Journal of Systems and Control, 27 Part of collection Institutional Repository Document type journal article Rights © 2024 Lars van de Kamp, Joey Reinders, Bram Hunnekens, T.A.E. Oomen, Nathan van de Wouw Files PDF 1-s2.0-S2468601823000226-main.pdf 1.14 MB Close viewer /islandora/object/uuid:5800bca7-69b7-4c26-bde5-414940de6ff3/datastream/OBJ/view