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

Comput Biol Med. 2017 Mar 1:82:100-110. doi: 10.1016/j.compbiomed.2017.01.017. Epub 2017 Jan 26.

Abstract

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.

Keywords: Automated neonatal seizure detection; Fusion; Gaussian dynamic time warping; Sequential classifier.

MeSH terms

  • Algorithms*
  • Computer Simulation
  • Diagnosis, Computer-Assisted / methods*
  • Electrocardiography / methods*
  • Epilepsy, Benign Neonatal / diagnosis*
  • Female
  • Humans
  • Infant, Newborn
  • Male
  • Models, Statistical
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Spatio-Temporal Analysis
  • Support Vector Machine*