An SVM-based system and its performance for detection of seizures in neonates

Annu Int Conf IEEE Eng Med Biol Soc. 2009:2009:2643-6. doi: 10.1109/IEMBS.2009.5332807.

Abstract

This work presents a multi-channel patient-independent neonatal seizure detection system based on the SVM classifier. Several post-processing steps are proposed to increase temporal precision and robustness of the system and their influence on performance is shown. The SVM-based system is evaluated on a large clinical dataset using several epoch-based and event based metrics and curves of performance are reported. Additionally, a new metric to measure the average duration of a false detection is proposed to accompany the event-based metrics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Automation
  • Computer Simulation
  • Data Interpretation, Statistical
  • Electroencephalography / instrumentation*
  • Electroencephalography / methods*
  • Equipment Design
  • Humans
  • Infant, Newborn
  • Neural Networks, Computer
  • Pattern Recognition, Automated / methods
  • ROC Curve
  • Reproducibility of Results
  • Seizures / diagnosis*
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted