title: Seizure detection in neonates: Improved classification through supervised adaptation.
abstract: The goal of neonatal seizure detection is the development of a patient independent system to alert staff in the neonatal intensive care unit of ongoing seizures. This study demonstrates the potential in adapting a patient independent classifier using patient specific data. Supervised adaptation is investigated using the basic gradient descent algorithm and least mean squares procedures. An increase in mean ROC area of 3% is obtained for the best performing learning algorithm, yielding an increase in mean accuracy of 7.7% compared to the patient independent algorithm.
authors.affiliation: Dept. of Electrical Engineering, UCC, Cork, Ireland. eoint@rennes.ucc.ie
authors.forename: E M
authors.initials: EM
authors.lastname: Thomas
authors.forename: B R
authors.initials: BR
authors.lastname: Greene
authors.forename: G
authors.initials: G
authors.lastname: Lightbody
authors.forename: W P
authors.initials: WP
authors.lastname: Marnane
authors.forename: G B
authors.initials: GB
authors.lastname: Boylan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007231:Infant, Newborn; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity