Conference Publication Details
Mandatory Fields
Ahmed, R;Temko, A;Marnane, W;Boylan, G;Lighbody, G
2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Dynamic Time Warping Based Neonatal Seizure Detection System
2012
January
Validated
1
WOS: 3 ()
Optional Fields
SUPPORT VECTOR MACHINES RECOGNITION
4919
4922
Neonatal seizures patterns evolve with changing frequency, morphology and propagation. This study is an initial attempt to incorporate the characteristics of temporal evolution of neonatal seizures into our developed neonatal seizure detector. The previously designed SVM-based neonatal seizure detector is modified by substituting the Gaussian kernel with the Gaussian dynamic time warping kernel, to enable the SVM to classify variable length sequences of feature vectors of neonatal seizures. The preliminary results obtained compare favorably with the conventional SVM. The fusion of the two approaches is expected to improve the current state of the art neonatal seizure detection system
Grant Details