title: Multiparametric modeling of the ineffective efforts in assisted ventilation within an ICU.
abstract: In the context of assisted ventilation in ICU, it is of vital importance to keep a high synchronization between the patient's attempt to breath and the assisted ventilation event, so that the patient receives the ventilation support requested. In this work, experimental equipment is employed, which allows for unobtrusive and continuous monitoring of a multiple relevant bioparameters. These are meant to guide the medical professionals in appropriately adapting the treatment and fine-tune the ventilation. However, synchronization phenomena of different origin (neurological, mechanical, ventilation parameters) may occur, which vary among patients, and during the course of monitoring of a single patient, the timely recognition of which is challenging even for experts. The dynamics and complex causal relations among bioparameters and the ventilation synchronization are not well studied. The purpose of this work is to elaborate on a methodology toward modeling the ventilation synchronization failures based on the evolution of monitored bioparameters. Principal component analysis is employed for the transformation into a small number of features and the investigation of repeating patterns and clusters within measurements. Using these features, nonlinear prediction models based on support vector machines regression are explored, in terms of what past knowledge is required and what is the future horizon that can be predicted. The proposed model shows good correlation (over 0.74) with the actual outputs, constituting an encouraging step toward understanding of ICU ventilation dynamic phenomena.
authors.affiliation: Lab of Medical Informatics, School of Medicine, Faculty of Health Sciences, Aristotle University, PB 323, Thessaloníki, 54124, Greece. ioannach@auth.gr.
authors.forename: I G
authors.initials: IG
authors.lastname: Chouvarda
authors.affiliation: Department of Intensive Care Medicine, Medical School, University Hospital of Heraklion, University of Crete, Heraklion, Crete, Greece.
authors.forename: D
authors.initials: D
authors.lastname: Babalis
authors.affiliation: Department of Intensive Care Medicine, Alexandroupolis University Hospital, Democritus University of Thrace, Dragana, Greece.
authors.forename: V
authors.initials: V
authors.lastname: Papaioannou
authors.affiliation: Lab of Medical Informatics, School of Medicine, Faculty of Health Sciences, Aristotle University, PB 323, Thessaloníki, 54124, Greece.|Institute of Applied Biosciences, CERTH, Thessaloniki, Greece.
authors.forename: N
authors.initials: N
authors.lastname: Maglaveras
authors.affiliation: Department of Intensive Care Medicine, Medical School, University Hospital of Heraklion, University of Crete, Heraklion, Crete, Greece.
authors.forename: D
authors.initials: D
authors.lastname: Georgopoulos
mesh_terms: D016000:Cluster Analysis; D006801:Humans; D007362:Intensive Care Units; D008962:Models, Theoretical; D025341:Principal Component Analysis; D012121:Respiration, Artificial; D060388:Support Vector Machine
keywords: Assisted ventilation; ICU; Nonlinear model; PCA cluster; SVM regression