Thomas, E.M., Greene, B.R., Lightbody, G., Marnane, W.P. and Boylan, G.B.

30th Annual International IEEE EMBS Conference

Seizure Detection in Neonates: Improved Classification through Supervised Adaptation

2008

August

Published

1

()

Neonatal EEG, seizure detection, supervised adaptation

903

906

Vancouver, Canada

20-AUG-08

24-AUG-08

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.

10.1109/IEMBS.2008.4649300

Science Foundation Ireland

05/PICA/1836