Optimization of an NLEO-based algorithm for automated detection of spontaneous activity transients in early preterm EEG

  • Palmu K
  • Stevenson N
  • Wikström S
 et al. 
  • 25

    Readers

    Mendeley users who have this article in their library.
  • 29

    Citations

    Citations of this article.

Abstract

We propose here a simple algorithm for automated detection of spontaneous activity transients (SATs) in early preterm electroencephalography (EEG). The parameters of the algorithm were optimized by supervised learning using a gold standard created from visual classification data obtained from three human raters. The generalization performance of the algorithm was estimated by leave-one-out cross-validation. The mean sensitivity of the optimized algorithm was 97% (range 91-100%) and specificity 95% (76-100%). The optimized algorithm makes it possible to systematically study brain state fluctuations of preterm infants.

Author-supplied keywords

  • Automated detection
  • Burst
  • EEG
  • NLEO
  • Preterm
  • SAT

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Daipayan GuhaUniversity of Toronto Division of Neurosurgery

    Follow
  • Kirsi Palmu

  • Sverre Wikström

  • Lena Hellström-Westas

  • Sampsa Vanhatalo

  • J. Matias Palva

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free