title: Automated neonatal seizure detection mimicking a human observer reading EEG.
abstract: OBJECTIVE
  The description and evaluation of a novel patient-independent seizure detection for the EEG of the newborn term infant.
  
  
  METHODS
  We identified characteristics of neonatal seizures by which a human observer is able to detect them. Neonatal seizures were divided into two types. For each type, a fully automated detection algorithm was developed based on the identified human observer characteristics. The first algorithm analyzes the correlation between high-energetic segments of the EEG. The second detects increases in low-frequency activity (<8 Hz) with high autocorrelation.
  
  
  RESULTS
  The complete algorithm was tested on multi-channel EEG recordings of 21 patients with and 5 patients without electrographic seizures, totaling 217 h of EEG. Sensitivity of the combined algorithms was found to be 88%, Positive Predictive Value (PPV) 75% and the false positive rate 0.66 per hour.
  
  
  CONCLUSIONS
  Our approach to separate neonatal seizures into two types yields a high sensitivity combined with a good PPV and much lower false positive rate than previously published algorithms.
  
  
  CONCLUSIONS
  The proposed algorithm significantly improves neonatal seizure detection and monitoring.
authors.affiliation: Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. wouter.deburchgraeve@esat.kuleuven.be
authors.forename: W
authors.initials: W
authors.lastname: Deburchgraeve
authors.forename: P J
authors.initials: PJ
authors.lastname: Cherian
authors.forename: M
authors.initials: M
authors.lastname: De Vos
authors.forename: R M
authors.initials: RM
authors.lastname: Swarte
authors.forename: J H
authors.initials: JH
authors.lastname: Blok
authors.forename: G H
authors.initials: GH
authors.lastname: Visser
authors.forename: P
authors.initials: P
authors.lastname: Govaert
authors.forename: S
authors.initials: S
authors.lastname: Van Huffel
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007232:Infant, Newborn, Diseases / Q000175:diagnosis*; D011237:Predictive Value of Tests; D012640:Seizures / Q000145:classification / Q000175:diagnosis*; D012680:Sensitivity and Specificity