title: Monitoring burst suppression in critically ill patients: Multi-centric evaluation of a novel method.
abstract: OBJECTIVE
  To develop a computational method to detect and quantify burst suppression patterns (BSP) in the EEGs of critical care patients. A multi-center validation study was performed to assess the detection performance of the method.
  
  
  METHODS
  The fully automatic method scans the EEG for discontinuous patterns and shows detected BSP and quantitative information on a trending display in real-time. The method is designed to work without setting any patient specific parameters and to be insensitive to EEG artifacts and periodic patterns. For validation a total of 3982 h of EEG from 88 patients were analyzed from three centers. Each EEG was annotated by two reviewers to assess the detection performance and the inter-rater agreement.
  
  
  RESULTS
  Average inter-rater agreement between pairs of reviewers was κ=0.69. On average 22% of the review segments included BSP. An average sensitivity of 90% and a specificity of 84% were measured on the consensus annotations of two reviewers. More than 95% of the periodic patterns in the EEGs were correctly suppressed.
  
  
  CONCLUSIONS
  A fully automatic method to detect burst suppression patterns was assessed in a multi-center study. The method showed high sensitivity and specificity.
  
  
  CONCLUSIONS
  Clinically applicable burst suppression detection method validated in a large multi-center study.
authors.affiliation: AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria. Electronic address: franz.fuerbass@ait.ac.at.
authors.forename: Franz
authors.initials: F
authors.lastname: Fürbass
authors.affiliation: Medical University of Vienna, Department of Neurosurgery, Vienna, Austria.
authors.forename: Johannes
authors.initials: J
authors.lastname: Herta
authors.affiliation: General Hospital Hietzing with Neurological Center Rosenhuegel, 2nd Neurological Department, Vienna, Austria.
authors.forename: Johannes
authors.initials: J
authors.lastname: Koren
authors.affiliation: Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA.
authors.forename: M Brandon
authors.initials: MB
authors.lastname: Westover
authors.affiliation: AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria.
authors.forename: Manfred M
authors.initials: MM
authors.lastname: Hartmann
authors.affiliation: Medical University of Vienna, Department of Neurosurgery, Vienna, Austria.
authors.forename: Andreas
authors.initials: A
authors.lastname: Gruber
authors.affiliation: General Hospital Hietzing with Neurological Center Rosenhuegel, 2nd Neurological Department, Vienna, Austria.
authors.forename: Christoph
authors.initials: C
authors.lastname: Baumgartner
authors.affiliation: AIT Austrian Institute of Technology, Safety & Security Department, Vienna, Austria.
authors.forename: Tilmann
authors.initials: T
authors.lastname: Kluge
mesh_terms: D003422:Critical Care / Q000379:methods*; D016638:Critical Illness / Q000628:therapy*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D012815:Signal Processing, Computer-Assisted
keywords: Automatic detection; Burst suppression pattern; EEG; Periodic pattern; Real-time monitoring