Kristína Mezeiová

Learn More
OBJECTIVE The objective of this study is to find the best set of characteristics of polysomnographic signals for the automatic classification of sleep stages. METHODS A selection was made from 74 measures, including linear spectral measures, interdependency measures, and nonlinear measures of complexity that were computed for the all-night(More)
OBJECTIVE Potential differences between coherence and phase synchronization analyses of human sleep electroencephalogram (EEG) are assessed and occurrences of phase vs. complete synchronization between EEG signals from different locations during different sleep stages are investigated. METHODS Linear spectral coherence, mean phase coherence (MPC) z-score(More)
False nearest neighbors (FNN) method is examinated with respect to equivariance of individual observables. The aim is to reveal the most appropriate observable for phase space reconstruction. Results calculated for benchmark systems are compared with symbolic observability degrees. The FNN method resulted in different values of embedding dimensions when(More)
If data are generated by a d-dimensional system, but only one observable is known, Takens' theorem guarantees that reconstruction diffeomorphic to the original dynamics can be build from the single time series in (2d+1)-dimensional phase space. However, some recent results show that, under certain conditions reconstruction in lower dimension is possible,(More)
  • 1