title: Online EEG channel weighting for detection of seizures in the neonate.
abstract: A framework for online dynamic channel weighting is developed for the task of EEG-based neonatal seizure detection. The channel weights are computed on-the-fly by combining the up-to-now patient-specific history and the clinically-derived prior channel importance. These estimated time-varying weights are introduced within a Bayesian probabilistic framework to provide a channel-specific and thus patient-adaptive seizure classification scheme. Validation results on one of the largest clinical datasets of neonatal seizures confirm the utility of the proposed channel weighting for the SVM-based detector recently developed by this research group. Exploiting the channel weighting, the precision-recall area can be drastically increased (up to 25%) for the most difficult patients, with the average increase from 81.0% to 84.42%. It is also shown that the increase in performance with channel weighting is proportional to the time the patient is observed.
authors.affiliation: Department of Electrical and Electronic Engineering and the Neonatal Brain Research Group, University College Cork, Ireland. andreyt@eleceng.ucc.ie
authors.forename: Andriy
authors.initials: A
authors.lastname: Temko
authors.forename: Gordon
authors.initials: G
authors.lastname: Lightbody
authors.forename: Geraldine
authors.initials: G
authors.lastname: Boylan
authors.forename: William
authors.initials: W
authors.lastname: Marnane
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D015997:Neonatal Screening / Q000379:methods*; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity