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  • This paper addresses the problem of multichannel analysis of newborn EEG signals. We apply designed methods for problem of the differentiation between three important neonatal states: quiet sleep, active sleep and wakeness state. The proportion of these states is a significant indicator for maturity of the newborn brain in clinical practise. The indispensable part of our work is a comparison of the classifier based on Markov models with various learning algorithms, in particular nearest neighbour, cluster analysis, and induction of decision rules.
  • This paper addresses the problem of multichannel analysis of newborn EEG signals. We apply designed methods for problem of the differentiation between three important neonatal states: quiet sleep, active sleep and wakeness state. The proportion of these states is a significant indicator for maturity of the newborn brain in clinical practise. The indispensable part of our work is a comparison of the classifier based on Markov models with various learning algorithms, in particular nearest neighbour, cluster analysis, and induction of decision rules. (en)
Title
  • Není k dispozici (cs)
  • Multichannel Analysis of the Newborn EEG Data
  • Multichannel Analysis of the Newborn EEG Data (en)
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  • Není k dispozici (cs)
  • Multichannel Analysis of the Newborn EEG Data
  • Multichannel Analysis of the Newborn EEG Data (en)
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  • RIV/68407700:21230/06:03123076!RIV07-MSM-21230___
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  • 487226
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  • RIV/68407700:21230/06:03123076
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  • Classification; EEG; Neonatal; Newborn (en)
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  • Lhotská, Lenka
  • Gerla, Václav
  • Paul, K.
  • Krajča, V.
http://localhost/t...ganizacniJednotka
  • 21230
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