Seizure detection in neonates: Improved classification through supervised adaptation

• Published in: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2008; pp. 903 - 906
• Main Authors: Thomas, E.M., Greene, B.R., Lightbody, G., Marnane, W.P., Boylan, G.B.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2008
• Subjects: Algorithms; Artificial Intelligence; Diagnosis, Computer-Assisted - methods; Electroencephalography; Electroencephalography - methods; Hospitals; Humans; Infant, Newborn; Neonatal EEG; Pattern Recognition, Automated - methods; Pediatrics; Reproducibility of Results; seizure detection; Seizures - diagnosis; Sensitivity and Specificity; supervised adaptation; Training
• ISBN: 9781424418145; 1424418143
• ISSN: 1094-687X; 1557-170X
• DOI: 10.1109/IEMBS.2008.4649300

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.