Thomas, EM,Greene, BR,Lightbody, G,Marnane, WP,Boylan, GB,

Seizure Detection in Neonates: Improved Classification through Supervised Adaptation

2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8

2008

March

Validated

1

()

Neonatal EEG seizure detection supervised adaptation EEG ECG

903

906

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.