title: EEG-based neonatal seizure detection with Support Vector Machines.
abstract: OBJECTIVE
  The study presents a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier.
  
  
  METHODS
  A machine learning algorithm (SVM) is used as a classifier to discriminate between seizure and non-seizure EEG epochs. Two post-processing steps are proposed to increase both the temporal precision and the robustness of the system. The resulting system is validated on a large clinical dataset of 267 h of EEG data from 17 full-term newborns with seizures.
  
  
  RESULTS
  The performance of the system using event-based metrics is reported. The system showed the best up-to-date performance of a neonatal seizure detection system. The system was able to achieve an average good detection rate of ~89% with one false seizure detection per hour, ~96% with two false detections per hour, or ~100% with four false detections per hour. An analysis of errors revealed sources of misclassification in terms of both missed seizures and false detections.
  
  
  CONCLUSIONS
  The results obtained with the proposed SVM-based seizure detection system allow for its practical application in neonatal intensive care units.
  
  
  CONCLUSIONS
  The proposed SVM-based seizure detection system can greatly assist clinical staff, in a neonatal intensive care unit, to interpret the EEG. The system allows control of the final decision by choosing different confidence levels which makes it flexible for clinical needs. The obtained results may provide a reference for future seizure detection systems.
authors: Neonatal Brain Research Group, University College Cork, Ireland. Electronic address: andreyt@eleceng.ucc.ie.
authors: A
authors: A
authors: Temko
authors: Neonatal Brain Research Group, University College Cork, Ireland.
authors: E
authors: E
authors: Thomas
authors: Neonatal Brain Research Group, University College Cork, Ireland; Department of Electrical and Electronic Engineering, University College Cork, Ireland.
authors: W
authors: W
authors: Marnane
authors: Neonatal Brain Research Group, University College Cork, Ireland; Department of Electrical and Electronic Engineering, University College Cork, Ireland.
authors: G
authors: G
authors: Lightbody
authors: Neonatal Brain Research Group, University College Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Ireland.
authors: G
authors: G
authors: Boylan
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D003951:Diagnostic Errors; D004569:Electroencephalography / Q000145:classification / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016014:Linear Models; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012640:Seizures / Q000145:classification / Q000175:diagnosis*