title: Artefact in Physiological Data Collected from Patients with Brain Injury: Quantifying the Problem and Providing a Solution Using a Factorial Switching Linear Dynamical Systems Approach.
abstract: High-resolution, artefact-free and accurately annotated physiological data are desirable in patients with brain injury both to inform clinical decision-making and for intelligent analysis of the data in applications such as predictive modelling. We have quantified the quality of annotation surrounding artefactual events and propose a factorial switching linear dynamical systems (FSLDS) approach to automatically detect artefact in physiological data collected in the neurological intensive care unit (NICU).
  
  
  
  Retrospective analysis of the BrainIT data set to discover potential hypotensive events corrupted by artefact and identify the annotation of associated clinical interventions. Training of an FSLDS model on clinician-annotated artefactual events in five patients with severe traumatic brain injury.
  
  
  
  In a subset of 187 patients in the BrainIT database, 26.5 % of potential hypotensive events were abandoned because of artefactual data. Only 30 % of these episodes could be attributed to an annotated clinical intervention. As assessed by the area under the receiver operating characteristic curve metric, FSLDS model performance in automatically identifying the events of blood sampling, arterial line damping and patient handling was 0.978, 0.987 and 0.765, respectively.
  
  
  
  The influence of artefact on physiological data collected in the NICU is a significant problem. This pilot study using an FSLDS approach shows real promise and is under further development.
authors.affiliation: School of Informatics, University of Edinburgh, Edinburgh, UK.
authors.forename: Konstantinos
authors.initials: K
authors.lastname: Georgatzis
authors.affiliation: School of Informatics, University of Edinburgh, Edinburgh, UK.
authors.forename: Partha
authors.initials: P
authors.lastname: Lal
authors.affiliation: Academic Unit of Anaesthesia, Pain and Critical Care Medicine, University of Glasgow, Level 4, Walton Building, Glasgow Royal Infirmary, 84 Castle Street, Glasgow, G4 0SF, UK. Christopher.Hawthorne@glasgow.ac.uk.
authors.forename: Christopher
authors.initials: C
authors.lastname: Hawthorne
authors.affiliation: Department of Clinical Physics, NHS Greater Glasgow and Clyde, Glasgow, UK.
authors.forename: Martin
authors.initials: M
authors.lastname: Shaw
authors.affiliation: Department of Clinical Physics, NHS Greater Glasgow and Clyde, Glasgow, UK.
authors.forename: Ian
authors.initials: I
authors.lastname: Piper
authors.affiliation: Department of Clinical Physics, NHS Greater Glasgow and Clyde, Glasgow, UK.
authors.forename: Claire
authors.initials: C
authors.lastname: Tarbert
authors.affiliation: School of Mathematics and Statistics, University of Glasgow, Glasgow, UK.
authors.forename: Rob
authors.initials: R
authors.lastname: Donald
authors.affiliation: School of Informatics, University of Edinburgh, Edinburgh, UK.
authors.forename: Christopher K I
authors.initials: CK
authors.lastname: Williams
mesh_terms: D062186:Arterial Pressure; D016477:Artifacts; D016208:Databases, Factual; D006801:Humans; D007022:Hypotension / Q000503:physiopathology*; D019586:Intracranial Hypertension / Q000503:physiopathology*; D007427:Intracranial Pressure; D016014:Linear Models; D000069550:Machine Learning; D008490:Medical Informatics; D008991:Monitoring, Physiologic; D010865:Pilot Projects; D012189:Retrospective Studies; D012815:Signal Processing, Computer-Assisted
keywords: Brain injury; Critical care; Information science; Physiological monitoring