title: Multi-channel EEG based neonatal seizure detection.
abstract: A multi-channel method for patient specific and patient independent, EEG based neonatal seizure detection is presented. Two classifier configurations are proposed and tested, along with a number of classifier models. Existing methods for neonatal seizure detection have been empirical threshold based or based on a single EEG channel. The optimum patient specific classifier for EEG based neonatal seizure detection was found to be an Early Integration configuration employing a linear discriminant classifier model. This yielded a mean classification accuracy of 74.66% for 11 neonatal records. The optimum patient independent classifier was an Early Integration configuration with a linear discriminant classifier model giving a mean accuracy of 72.81%
authors.affiliation: Sch. of Electr., Electron. & Mech. Eng., Univ. Coll. Dublin, Ireland. barry.greene@ee.ucd.ie
authors.forename: Barry R
authors.initials: BR
authors.lastname: Greene
authors.forename: Richard B
authors.initials: RB
authors.lastname: Reilly
authors.forename: Geraldine
authors.initials: G
authors.lastname: Boylan
authors.forename: Philip
authors.initials: P
authors.lastname: de Chazal
authors.forename: Sean
authors.initials: S
authors.lastname: Connolly
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods; D004867:Equipment Design; D006801:Humans; D007231:Infant, Newborn; D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis* / Q000473:pathology; D012815:Signal Processing, Computer-Assisted; D012984:Software