title: Validation of an automated seizure detection algorithm for term neonates.
abstract: OBJECTIVE
  The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres.
  
  
  METHODS
  EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed.
  
  
  RESULTS
  Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6-75.0%, with false detection (FD) rates of 0.04-0.36FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen's Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures.
  
  
  CONCLUSIONS
  The SDA achieved promising performance and warrants further testing in a live clinical evaluation.
  
  
  CONCLUSIONS
  The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens.
authors.affiliation: Academic Research Department of Neonatology, Institute for Women's Health, University College London, London, United Kingdom.
authors.forename: Sean R
authors.initials: SR
authors.lastname: Mathieson
authors.affiliation: Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
authors.forename: Nathan J
authors.initials: NJ
authors.lastname: Stevenson
authors.affiliation: Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
authors.forename: Evonne
authors.initials: E
authors.lastname: Low
authors.affiliation: Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
authors.forename: William P
authors.initials: WP
authors.lastname: Marnane
authors.affiliation: Academic Research Department of Neonatology, Institute for Women's Health, University College London, London, United Kingdom.
authors.forename: Janet M
authors.initials: JM
authors.lastname: Rennie
authors.affiliation: Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
authors.forename: Andrey
authors.initials: A
authors.lastname: Temko
authors.affiliation: Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
authors.forename: Gordon
authors.initials: G
authors.lastname: Lightbody
authors.affiliation: Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland. Electronic address: g.boylan@ucc.ie.
authors.forename: Geraldine B
authors.initials: GB
authors.lastname: Boylan
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D012640:Seizures / Q000175:diagnosis*
keywords: Automated seizure detection; Hypoxic-ischaemic encephalopathy; Neonatal EEG; Neonatal neurology; Neonatal seizures