title: Grading hypoxic-ischemic encephalopathy severity in neonatal EEG using GMM supervectors and the support vector machine.
abstract: OBJECTIVE
  This work presents a novel automated system to classify the severity of hypoxic-ischemic encephalopathy (HIE) in neonates using EEG.
  
  
  METHODS
  A cross disciplinary method is applied that uses the sequences of short-term features of EEG to grade an hour long recording. Novel post-processing techniques are proposed based on majority voting and probabilistic methods. The proposed system is validated with one-hour-long EEG recordings from 54 full term neonates.
  
  
  RESULTS
  An overall accuracy of 87% is achieved. The developed grading system has improved both the accuracy and the confidence/quality of the produced decision. With a new label 'unknown' assigned to the recordings with lower confidence levels an accuracy of 96% is attained.
  
  
  CONCLUSIONS
  The statistical long-term model based features extracted from the sequences of short-term features has improved the overall accuracy of grading the HIE injury in neonatal EEG.
  
  
  CONCLUSIONS
  The proposed automated HIE grading system can provide significant assistance to healthcare professionals in assessing the severity of HIE. This represents a practical and user friendly implementation which acts as a decision support system in the clinical environment. Its integration with other EEG analysis algorithms may improve neonatal neurocritical care.
authors.affiliation: Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research (INFANT), Ireland; Department of Electrical and Electronics Engineering, University College Cork, Ireland. Electronic address: rehan@eleceng.ucc.ie.
authors.forename: Rehan
authors.initials: R
authors.lastname: Ahmed
authors.affiliation: Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research (INFANT), Ireland; Department of Electrical and Electronics Engineering, University College Cork, Ireland.
authors.forename: Andriy
authors.initials: A
authors.lastname: Temko
authors.affiliation: Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research (INFANT), Ireland; Department of Electrical and Electronics Engineering, University College Cork, Ireland.
authors.forename: William
authors.initials: W
authors.lastname: Marnane
authors.affiliation: Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research (INFANT), Ireland; Department of Electrical and Electronics Engineering, University College 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 (INFANT), Ireland; Department of Pediatrics and Child Health, University College Cork, Ireland.
authors.forename: Geraldine
authors.initials: G
authors.lastname: Boylan
mesh_terms: D004569:Electroencephalography / Q000145:classification* / Q000379:methods; D005260:Female; D006801:Humans; D020925:Hypoxia-Ischemia, Brain / Q000145:classification* / Q000175:diagnosis* / Q000503:physiopathology; D007231:Infant, Newborn; D008297:Male; D060388:Support Vector Machine / Q000145:classification*
keywords: Automated neonatal HIE EEG grading system; EEG; EEG analysis algorithms; Gaussian mixture models; Hypoxic–ischemic encephalopathy; Long term EEG features; Neonatal EEG; Support vector machine