title: Automated classification of neonatal sleep states using EEG.
abstract: To develop a method for automated neonatal sleep state classification based on EEG that can be applied over a wide range of age.
  
  
  
  We collected 231 EEG recordings from 67 infants between 24 and 45weeks of postmenstrual age. Ten minute epochs of 8 channel polysomnography (N=323) from active and quiet sleep were used as a training dataset. We extracted a set of 57 EEG features from the time, frequency, and spatial domains. A greedy algorithm was used to define a reduced feature set to be used in a support vector machine classifier.
  
  
  
  Performance tests showed that our algorithm was able to classify quiet and active sleep epochs with 85% accuracy, 83% sensitivity, and 87% specificity. The performance was not substantially lowered by reducing the epoch length or EEG channel number. The classifier output was used to construct a novel trend, the sleep state probability index, that improves the visualisation of brain state fluctuations.
  
  
  
  A robust EEG-based sleep state classifier was developed. It performs consistently well across a large span of postmenstrual ages.
  
  
  
  This method enables the visualisation of sleep state in preterm infants which can assist clinical management in the neonatal intensive care unit.
authors.affiliation: BABA Center, Department of Children's Clinical Neurophysiology, Children's Hospital, HUS Medical Imaging Center, Helsinki University Central Hospital and University of Helsinki, Finland.
authors.forename: Ninah
authors.initials: N
authors.lastname: Koolen
authors.affiliation: Medical University Vienna, Department of Pediatrics, Division of Neonatology, Pediatric Intensive Care and Neuropediatrics, Vienna, Austria.
authors.forename: Lisa
authors.initials: L
authors.lastname: Oberdorfer
authors.affiliation: Medical University Vienna, Department of Pediatrics, Division of Neonatology, Pediatric Intensive Care and Neuropediatrics, Vienna, Austria.
authors.forename: Zsofia
authors.initials: Z
authors.lastname: Rona
authors.affiliation: Medical University Vienna, Department of Pediatrics, Division of Neonatology, Pediatric Intensive Care and Neuropediatrics, Vienna, Austria.
authors.forename: Vito
authors.initials: V
authors.lastname: Giordano
authors.affiliation: Medical University Vienna, Department of Pediatrics, Division of Neonatology, Pediatric Intensive Care and Neuropediatrics, Vienna, Austria.
authors.forename: Tobias
authors.initials: T
authors.lastname: Werther
authors.affiliation: Medical University Vienna, Department of Pediatrics, Division of Neonatology, Pediatric Intensive Care and Neuropediatrics, Vienna, Austria.
authors.forename: Katrin
authors.initials: K
authors.lastname: Klebermass-Schrehof
authors.affiliation: BABA Center, Department of Children's Clinical Neurophysiology, Children's Hospital, HUS Medical Imaging Center, Helsinki University Central Hospital and University of Helsinki, Finland.
authors.forename: Nathan
authors.initials: N
authors.lastname: Stevenson
authors.affiliation: BABA Center, Department of Children's Clinical Neurophysiology, Children's Hospital, HUS Medical Imaging Center, Helsinki University Central Hospital and University of Helsinki, Finland. Electronic address: sampsa.vanhatalo@helsinki.fi.
authors.forename: Sampsa
authors.initials: S
authors.lastname: Vanhatalo
mesh_terms: D002657:Child Development / Q000145:classification*; D004569:Electroencephalography / Q000379:methods* / Q000592:standards; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature / Q000254:growth & development / Q000502:physiology*; D007235:Infant, Premature, Diseases / Q000175:diagnosis*; D012890:Sleep; D060388:Support Vector Machine
keywords: Brain monitoring; Classification; Neonatal EEG; Sleep-wake cycling; Support vector machine