title: Assessment of validity and predictability of the FiO2-SpO2 transfer-function in preterm infants.
abstract: In this paper an investigation of the gain, delay, and time-constant parameters of the transfer function describing the relation between fraction of inspired oxygen (FiO2) and oxygen saturation in the blood (SpO2) in preterm infants is presented. The parameters were estimated following FiO2 adjustments and goodness of fit was used to assess the validity of the model when using an assumed first-order transfer function. For responses identified to be first-order, the estimated parameters were then clustered to identify areas where they tended to be concentrated. Each group described an operating region of the transfer function; thus, predicting the right operating region could potentially assist a range-based robust inspired oxygen controller to provide more optimal control by adapting itself to different clusters. Accordingly, the samples were assigned labels based on their cluster associations and 14 features available at the time of each adjustment were used as inputs to an artificial neural network to classify the clustered samples. The validity study suggested that 37% of the adjustments were followed by first-order responses. Prediction studies on the first-order responses indicated that the clusters could be predicted with an average accuracy of 64% when the parameters were divided into two groups.
authors.affiliation: School of Engineering and ICT, University of Tasmania, Hobart, TAS 7001, Australia.
authors.forename: O Sadeghi
authors.initials: OS
authors.lastname: Fathabadi
authors.forename: T J
authors.initials: TJ
authors.lastname: Gale
authors.forename: K
authors.initials: K
authors.lastname: Lim
authors.forename: B P
authors.initials: BP
authors.lastname: Salmon
authors.forename: K I
authors.initials: KI
authors.lastname: Wheeler
authors.forename: J C
authors.initials: JC
authors.lastname: Olivier
authors.forename: P A
authors.initials: PA
authors.lastname: Dargaville
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D045422:Continuous Positive Airway Pressure; D016208:Databases, Factual; D003935:Diagnostic Techniques, Cardiovascular; D006801:Humans; D007223:Infant; D007234:Infant, Premature / Q000502:physiology*; D001239:Inhalation; D016571:Neural Networks, Computer; D010100:Oxygen / Q000097:blood*; D012815:Signal Processing, Computer-Assisted
chemical_list: D010100:Oxygen