title: University of Queensland vital signs dataset: development of an accessible repository of anesthesia patient monitoring data for research.
abstract: BACKGROUND
  Data recorded from the devices used to monitor a patient's vital signs are often used in the development of displays, alarms, and information systems, but high-resolution, multiple-parameter datasets of anesthesia monitoring data from patients during anesthesia are often difficult to obtain. Existing databases have typically been collected from patients in intensive care units. However, the physical state of intensive care patients is dissimilar to those undergoing surgery, more frequent and marked changes to cardiovascular and respiratory variables are seen in operating room patients, and additional and highly relevant information to anesthesia (e.g., end-tidal agent monitoring, etc.) is omitted from these intensive care databases. We collected a set of high-quality, high-resolution, multiple-parameter monitoring data suitable for anesthesia monitoring research.
  
  
  METHODS
  Vital signs data were recorded from patients undergoing anesthesia at the Royal Adelaide Hospital. Software was developed to capture, time synchronize, and interpolate vital signs data from Philips IntelliVue MP70 and MP30 patient monitors and Datex-Ohmeda Aestiva/5 anesthesia machines into 10 millisecond resolution samples. The recorded data were saved in a variety of accessible file formats.
  
  
  RESULTS
  Monitoring data were recorded from 32 cases (25 general anesthetics, 3 spinal anesthetics, 4 sedations) ranging in duration from 13 minutes to 5 hours (median 105 min). Most cases included data from the electrocardiograph, pulse oximeter, capnograph, noninvasive arterial blood pressure monitor, airway flow, and pressure monitor and, in a few cases, the Y-piece spirometer, electroencephalogram monitor, and arterial blood pressure monitor. Recorded data were processed and saved into 4 file formats: (1) comma-separated values text files with full numerical and waveform data, (2) numerical parameters recorded in comma-separated values files at 1-second intervals, (3) graphical plots of all waveform data in a range of resolutions as Portable Network Graphics image files, and (4) graphical overview plots of numerical data for entire cases as Portable Network Graphics and Scalable Vector Graphics files. The complete dataset is freely available online via doi:102.100.100/6914 and has been listed in the Australian National Data Service Collections Registry.
  
  
  CONCLUSIONS
  The present dataset provides clinical anesthesia monitoring data from entire surgical cases where patients underwent anesthesia, includes a wide range of vital signs variables that are commonly monitored during surgery, and is published in accessible, user-friendly file formats. The text and image file formats let researchers without engineering or computer science backgrounds easily access the data using standard spreadsheet and image browsing software. In future work, monitoring data should be collected from a wider range and larger number of cases, and software tools are needed to support searching and navigating the database.
authors.affiliation: School of Information Technology and Electrical Engineering, The University of Queensland St Lucia QLD 4072, Brisbane, Australia. naskies@acm.org
authors.forename: David
authors.initials: D
authors.lastname: Liu
authors.forename: Matthias
authors.initials: M
authors.lastname: Görges
authors.forename: Simon A
authors.initials: SA
authors.lastname: Jenkins
mesh_terms: D000758:Anesthesia / Q000379:methods*; D035843:Biomedical Research / Q000379:methods*; D016208:Databases, Factual; D006785:Hospitals, University; D006801:Humans; D008991:Monitoring, Physiologic / Q000379:methods*; D011793:Queensland; D055986:Vital Signs / Q000502:physiology