TY  - JOUR
  - Palmu, K,Stevenson, N,Wikstrom, S,Hellstrom-Westas, L,Vanhatalo, S,Palva, JM
  - 2010
  - January
  - Physiological Measurement
  - Optimization of an NLEO-based algorithm for automated detection of spontaneous activity transients in early preterm EEG
  - Validated
  - ()
  - EEG preterm SAT burst automated detection NLEO BRAIN PREMATURE ARTIFACTS INFANTS CORTEX BIRTH
  - 31
  - 85
  - 93
  - 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.
  - DOI 10.1088/0967-3334/31/11/N02
DA  - 2010/01
ER  - 

@article{V70046601,
   = {Palmu,  K and Stevenson,  N and Wikstrom,  S and Hellstrom-Westas,  L and Vanhatalo,  S and Palva,  JM },
   = {2010},
   = {January},
   = {Physiological Measurement},
   = {Optimization of an NLEO-based algorithm for automated detection of spontaneous activity transients in early preterm EEG},
   = {Validated},
   = {()},
   = {EEG preterm SAT burst automated detection NLEO BRAIN PREMATURE ARTIFACTS INFANTS CORTEX BIRTH},
   = {31},
  pages = {85--93},
   = {{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.}},
   = {DOI 10.1088/0967-3334/31/11/N02},
  source = {IRIS}
}