Vladimir Krajca
Czech Technical University in Prague
52 Papers
411 Citations
Vladimir Krajca is an academic researcher from Czech Technical University in Prague. The author has contributed to research in topics: Electroencephalography & Sleep Stages. The author has an hindex of 13, co-authored 52 publications.
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Papers
Early reduction in prefrontal theta QEEG cordance value predicts response to venlafaxine treatment in patients with resistant depressive disorder.
Martin Bareš,Martin Brunovsky,Miloslav Kopecek,Miloslav Kopecek,Tomas Novak,Pavla Stopkova,Jiri Kozeny,P. Sos,Vladimir Krajca,Cyril Höschl +9 more
TL;DR: In this paper, the authors examined whether decrease of prefrontal quantitative EEG (QEEG) cordance value after 1 week of venlafaxine treatment predicts clinical response to Vaxine in resistant patients.
129
Electroencephalographic spectral and coherence analysis of ketamine in rats: correlation with behavioral effects and pharmacokinetics.
Tomáš Páleníček,M. Fujakova,Martin Brunovský,Marie Balíková,Jiří Horáček,Ingmar Gorman,Filip Tylš,B. Tislerova,P. Sos,Věra Bubeníková-Valešová,Cyril Höschl,Vladimir Krajca +11 more
TL;DR: Ketamine at behaviorally active doses induces a robust increase in EEG power spectra and coherence, and the maximum levels of change correlated with the kinetics of ketamine.
92
Automatic identification of significant graphoelements in multichannel EEG recordings by adaptive segmentation and fuzzy clustering.
TL;DR: A new approach to visual evaluation of long-term EEG recordings is proposed, based on multichannel adaptive segmentation, subsequent feature extraction, and automatic classification of the acquired segments by fuzzy cluster analysis (fuzzy c-means algorithm).
88
Robust removal of short-duration artifacts in long neonatal EEG recordings using wavelet-enhanced ICA and adaptive combining of tentative reconstructions
TL;DR: Results prove, both qualitatively and quantitatively, that the RAR method is capable of automatically rejecting the mentioned artifacts without changes in overall signal properties such as the spectrum.
45
Multivariate Analysis of Full-Term Neonatal Polysomnographic Data
Vaclav Gerla,K. Paul,Lenka Lhotska,Vladimir Krajca +3 more
- 01 Jan 2009
TL;DR: This paper addresses the problem of computer analysis of neonatal polygraphic signals by applying methods designed for differentiating three important neonatal behavioral states: quiet sleep, active sleep, and wakefulness.
41