@inbook{c9640feafcaf43d9b868e622edb26acf,
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.",
keywords = "Neonatal EEG, Seizure detection, Supervised adaptation",
author = "Thomas, \{E. M.\} and Greene, \{B. R.\} and G. Lightbody and Marnane, \{W. P.\} and Boylan, \{G. B.\}",
year = "2008",
doi = "10.1109/iembs.2008.4649300",
language = "English",
isbn = "9781424418152",
series = "Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - {"}Personalized Healthcare through Technology{"}",
publisher = "IEEE Computer Society",
pages = "903--906",
booktitle = "Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08",
address = "United States",
note = "30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 ; Conference date: 20-08-2008 Through 25-08-2008",
}