Dynamic time warping based neonatal seizure detection system.
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.
Department of Electrical and Electronic Engineering and the Neonatal Brain Research Group, University College Cork, Ireland. rehan.andreyt@eleceng.ucc.ie
Rehan
R
Ahmed
Andrey
A
Temko
William
W
Marnane
Geraldine
G
Boylan
Gordon
G
Lighbody
D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine