Full text restriction information:Access to this item is restricted until 12 months after publication by the request of the publisher.
Restriction lift date:2018-01-26
Citation:Ahmed, R., Temko, A., Marnane, W. P., Boylan, G. and Lightbody, G. (2017) 'Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel', Computers in Biology and Medicine, 82, pp. 100-110. doi:10.1016/j.compbiomed.2017.01.017
Seizure events in newborns change in frequency, morphology, and propagation. This contextual information is explored at the classifier level in the proposed patient-independent neonatal seizure detection system. The system is based on the combination of a static and a sequential SVM classifier. A Gaussian dynamic time warping based kernel is used in the sequential classifier. The system is validated on a large dataset of EEG recordings from 17 neonates. The obtained results show an increase in the detection rate at very low false detections per hour, particularly achieving a 12% improvement in the detection of short seizure events over the static RBF kernel based system.
This website uses cookies. By using this website, you consent to the use of cookies in accordance with the UCC Privacy and Cookies Statement. For more information about cookies and how you can disable them, visit our Privacy and Cookies statement