title: Improvement of an automated neonatal seizure detector using a post-processing technique.
abstract: Visual recognition of neonatal seizures during continuous EEG monitoring in neonatal intensive care units (NICUs) is labor-intensive, has low inter-rater agreement and requires special expertise that is not available around the clock. Development of an accurate automated seizure detection system with a low false alarm rate will support clinical decision making and alleviate significantly the workload. However, this is an ongoing difficult challenge for engineers as the neonatal EEG signal is non-stationary and often includes complex patterns of seizures and artifacts. In this study, we show an improvement of our previously developed neonatal seizure detector (developed using heuristic if-then rules). In order to improve the detection accuracy, mean phase coherence as a new feature is used to characterize artifacts and also support vector machine is applied to perform the post-processing step to remove false detections. As a result, the false alarm rate drops 42% (from 2.6 h(-1) to 1.5 h(-1)), whereas the good detection rate reduces only by 4%.
authors.forename: A H
authors.initials: AH
authors.lastname: Ansari
authors.forename: V
authors.initials: V
authors.lastname: Matic
authors.forename: M
authors.initials: M
authors.lastname: De Vos
authors.forename: G
authors.initials: G
authors.lastname: Naulaers
authors.forename: P J
authors.initials: PJ
authors.lastname: Cherian
authors.forename: S
authors.initials: S
authors.lastname: Van Huffel
mesh_terms: D000465:Algorithms; D016477:Artifacts; D004569:Electroencephalography; D006801:Humans; D007231:Infant, Newborn; D007232:Infant, Newborn, Diseases; D012640:Seizures; D060388:Support Vector Machine