title: Automatic detection of rhythmic and periodic patterns in critical care EEG based on American Clinical Neurophysiology Society (ACNS) standardized terminology.
abstract: OBJECTIVE
  Continuous EEG from critical care patients needs to be evaluated time efficiently to maximize the treatment effect. A computational method will be presented that detects rhythmic and periodic patterns according to the critical care EEG terminology (CCET) of the American Clinical Neurophysiology Society (ACNS). The aim is to show that these detected patterns support EEG experts in writing neurophysiological reports.
  
  
  METHODS
  First of all, three case reports exemplify the evaluation procedure using graphically presented detections. Second, 187 hours of EEG from 10 critical care patients were used in a comparative trial study. For each patient the result of a review session using the EEG and the visualized pattern detections was compared to the original neurophysiology report.
  
  
  RESULTS
  In three out of five patients with reported seizures, all seizures were reported correctly. In two patients, several subtle clinical seizures with unclear EEG correlation were missed. Lateralized periodic patterns (LPD) were correctly found in 2/2 patients and EEG slowing was correctly found in 7/9 patients. In 8/10 patients, additional EEG features were found including LPDs, EEG slowing, and seizures.
  
  
  CONCLUSIONS
  The use of automatic pattern detection will assist in review of EEG and increase efficiency. The implementation of bedside surveillance devices using our detection algorithm appears to be feasible and remains to be confirmed in further multicenter studies.
authors.affiliation: Austrian Institute of Technology GmbH (AIT), Safety & Security Department, Vienna, Austria. Electronic address: franz.fuerbass@ait.ac.at.
authors.forename: F
authors.initials: F
authors.lastname: Fürbass
authors.affiliation: Austrian Institute of Technology GmbH (AIT), Safety & Security Department, Vienna, Austria.
authors.forename: M M
authors.initials: MM
authors.lastname: Hartmann
authors.affiliation: Medical University of South Carolina, Comprehensive Epilepsy Center, Charleston, SC, USA.
authors.forename: J J
authors.initials: JJ
authors.lastname: Halford
authors.affiliation: General Hospital Hietzing with Neurological Center Rosenhuegel, 2nd Neurological Department, Vienna, Austria.
authors.forename: J
authors.initials: J
authors.lastname: Koren
authors.affiliation: Medical University of Vienna, Department of Neurosurgery, Vienna, Austria.
authors.forename: J
authors.initials: J
authors.lastname: Herta
authors.affiliation: Medical University of Vienna, Department of Neurosurgery, Vienna, Austria.
authors.forename: A
authors.initials: A
authors.lastname: Gruber
authors.affiliation: General Hospital Hietzing with Neurological Center Rosenhuegel, 2nd Neurological Department, Vienna, Austria.
authors.forename: C
authors.initials: C
authors.lastname: Baumgartner
authors.affiliation: Austrian Institute of Technology GmbH (AIT), Safety & Security Department, Vienna, Austria.
authors.forename: T
authors.initials: T
authors.lastname: Kluge
mesh_terms: D000465:Algorithms; D001331:Automation; D001922:Brain Abscess / Q000175:diagnosis; D003196:Computer Graphics; D003422:Critical Care / Q000592:standards*; D004569:Electroencephalography / Q000592:standards*; D005260:Female; D006801:Humans; D008875:Middle Aged; D020521:Stroke / Q000175:diagnosis; D009626:Terminology as Topic; D014584:User-Computer Interface
keywords: ACNS ICU terminology; Automatic detection; Critical care; Détection automatique; EEG; Motifs rythmiques et périodiques; Rhythmic and periodic patterns; Soins intensifs; Terminologie ACNS USI