title: Line length as a robust method to detect high-activity events: automated burst detection in premature EEG recordings.
abstract: OBJECTIVE
  EEG is a valuable tool for evaluation of brain maturation in preterm babies. Preterm EEG constitutes of high voltage burst activities and more suppressed episodes, called interburst intervals (IBIs). Evolution of background characteristics provides information on brain maturation and helps in prediction of neurological outcome. The aim is to develop a method for automated burst detection.
  
  
  METHODS
  Thirteen polysomnography recordings were used, collected at preterm postmenstrual age of 31.4 (26.1-34.4)weeks. We developed a burst detection algorithm based on the feature line length and compared it with manual scorings of clinical experts and other published methods.
  
  
  RESULTS
  The line length-based algorithm is robust (84.27% accuracy, 84.00% sensitivity, 85.70% specificity). It is not critically dependent on the number of measurement channels, because two channels still provide 82% accuracy. Furthermore, it approximates well clinically relevant features, such as median IBI duration 5.45 (4.00-7.11)s, maximum IBI duration 14.02 (8.73-18.80)s and burst percentage 48.89 (35.45-60.12)%, with a median deviation of respectively 0.65s, 1.96s and 6.55%.
  
  
  CONCLUSIONS
  Automated assessment of long-term preterm EEG is possible and its use will optimize EEG interpretation in the NICU.
  
  
  CONCLUSIONS
  This study takes a first step towards fully automatic analysis of the preterm brain.
authors.affiliation: Department of Electrical Engineering (ESAT), Division SCD, KU Leuven, Leuven, Belgium; iMinds-KU Leuven Future Health Department, Leuven, Belgium. Electronic address: ninah.koolen@esat.kuleuven.be.
authors.forename: Ninah
authors.initials: N
authors.lastname: Koolen
authors.affiliation: Department of Pediatrics, University Hospital Gasthuisberg, Leuven, Belgium.
authors.forename: Katrien
authors.initials: K
authors.lastname: Jansen
authors.affiliation: Department of Pediatrics, University Hospital Gasthuisberg, Leuven, Belgium.
authors.forename: Jan
authors.initials: J
authors.lastname: Vervisch
authors.affiliation: Department of Electrical Engineering (ESAT), Division SCD, KU Leuven, Leuven, Belgium; iMinds-KU Leuven Future Health Department, Leuven, Belgium.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Matic
authors.affiliation: Cluster of Excellence "Hearing4all" & Methods in Neurocognitive Psychology, University of Oldenburg, Oldenburg, Germany; Department of Electrical Engineering (ESAT), Division SCD, KU Leuven, Leuven, Belgium.
authors.forename: Maarten
authors.initials: M
authors.lastname: De Vos
authors.affiliation: Neonatal Intensive Care Unit, University Hospital Gasthuisberg, Leuven, Belgium.
authors.forename: Gunnar
authors.initials: G
authors.lastname: Naulaers
authors.affiliation: Department of Electrical Engineering (ESAT), Division SCD, KU Leuven, Leuven, Belgium; iMinds-KU Leuven Future Health Department, Leuven, Belgium.
authors.forename: Sabine
authors.initials: S
authors.lastname: Van Huffel
mesh_terms: D000465:Algorithms; D001921:Brain / Q000254:growth & development / Q000502:physiology*; D058256:Brain Waves / Q000502:physiology; D004569:Electroencephalography / Q000379:methods* / Q000592:standards; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007234:Infant, Premature; D017286:Polysomnography; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: Automated detection; Background EEG; Brain maturation; Brain monitoring; Bursts; Interburst intervals; Line length; Preterm