Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel

نویسندگان

  • Rehan Ahmed
  • Andriy Temko
  • William P. Marnane
  • Geraldine B. Boylan
  • Gordon Lightbody
چکیده

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.

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عنوان ژورنال:
  • Computers in biology and medicine

دوره 82  شماره 

صفحات  -

تاریخ انتشار 2017