Conference Publication Details
Mandatory Fields
Thomas E.;Greene B.;Lightbody G.;Marnane W.;Boylan G.
Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
Seizure detection in neonates: Improved classification through supervised adaptation
Scopus: 2 ()
Optional Fields
Neonatal EEG Seizure detection Supervised adaptation
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. 2008 IEEE.
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