TY  - JOUR
AU  - Spasić, Slađana
PY  - 2010
UR  - http://rimsi.imsi.bg.ac.rs/handle/123456789/382
AB  - The surrogate data test for nonlinearity has been used in order to establish the existence of nonlinear dynamics and justify the use of nonlinear tools in time series analysis. We applied Higuchi fractal dimension and third-order correlation function on the rat electrocortical activity as discriminative statistics. Our particular interest in this study was to investigate the nonlinearity of cerebellar electrocortical signals in rat model of acute and repeated cerebral cortical injury. We performed the surrogate data test for nonlinearity by using the algorithm of statically transformed autoregressive process (STAP) to generate the surrogate data. Surrogate data test for nonlinearity indicated that cerebellar cortical signals have mostly nonlinear properties during all experimental conditions in the model of repeated cerebral cortical injury. We conclude that results of testing nonlinearity by Higuchi fractal dimension as discriminative statistic are more stable than those obtained by the third-order correlation.
PB  - Elsevier Science Bv, Amsterdam
T2  - Signal Processing
T1  - Surrogate data test for nonlinearity of the rat cerebellar electrocorticogram in the model of brain injury
EP  - 3025
IS  - 12
SP  - 3015
VL  - 90
DO  - 10.1016/j.sigpro.2010.04.005
ER  - 

@article{
author = "Spasić, Slađana",
year = "2010",
abstract = "The surrogate data test for nonlinearity has been used in order to establish the existence of nonlinear dynamics and justify the use of nonlinear tools in time series analysis. We applied Higuchi fractal dimension and third-order correlation function on the rat electrocortical activity as discriminative statistics. Our particular interest in this study was to investigate the nonlinearity of cerebellar electrocortical signals in rat model of acute and repeated cerebral cortical injury. We performed the surrogate data test for nonlinearity by using the algorithm of statically transformed autoregressive process (STAP) to generate the surrogate data. Surrogate data test for nonlinearity indicated that cerebellar cortical signals have mostly nonlinear properties during all experimental conditions in the model of repeated cerebral cortical injury. We conclude that results of testing nonlinearity by Higuchi fractal dimension as discriminative statistic are more stable than those obtained by the third-order correlation.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Signal Processing",
title = "Surrogate data test for nonlinearity of the rat cerebellar electrocorticogram in the model of brain injury",
pages = "3025-3015",
number = "12",
volume = "90",
doi = "10.1016/j.sigpro.2010.04.005"
}

Spasić, S.. (2010). Surrogate data test for nonlinearity of the rat cerebellar electrocorticogram in the model of brain injury. in Signal Processing
Elsevier Science Bv, Amsterdam., 90(12), 3015-3025.
https://doi.org/10.1016/j.sigpro.2010.04.005

Spasić S. Surrogate data test for nonlinearity of the rat cerebellar electrocorticogram in the model of brain injury. in Signal Processing. 2010;90(12):3015-3025.
doi:10.1016/j.sigpro.2010.04.005 .

Spasić, Slađana, "Surrogate data test for nonlinearity of the rat cerebellar electrocorticogram in the model of brain injury" in Signal Processing, 90, no. 12 (2010):3015-3025,
https://doi.org/10.1016/j.sigpro.2010.04.005 . .

TY  - JOUR
AU  - Jovanović, Aleksandar
AU  - Perović, Aleksandar
AU  - Klonowski, Wlodzimierz
AU  - Duch, Wlodzislaw
AU  - Đorđević, Zoran
AU  - Spasić, Slađana
PY  - 2010
UR  - http://rimsi.imsi.bg.ac.rs/handle/123456789/381
AB  - In this article structures in biological signals are treated. The simpler-directly visible in the signals, which still demand serious methods and algorithms in the feature detection, similarity investigation and classification. The major actions in this domain are of geometric, thus simpler sort, though there are still hard problems related to simple situations. The other large class of less simple signals unsuitable for direct geometric or statistic approach, are signals with interesting frequency components and behavior, those suitable for spectroscopic analysis. Semantics of spectroscopy, spectroscopic structures and research demanded operations and transformations on spectra and time spectra are presented. The both classes of structures and related analysis methods and tools share a large common set of algorithms, all of which aiming to the full automatization. Some of the signal features present in the brain signal patterns are demonstrated, with the contexts relevant in BCI, brain computer interfaces. Mathematical representations, invariants and complete characterization of structures in broad variety of biological signals are in the central focus.
PB  - Springer, New York
T2  - Journal of Signal Processing Systems for Signal Image and Video Technology
T1  - Detection of Structural Features in Biological Signals
EP  - 129
IS  - 1
SP  - 115
VL  - 60
DO  - 10.1007/s11265-009-0407-7
ER  - 

@article{
author = "Jovanović, Aleksandar and Perović, Aleksandar and Klonowski, Wlodzimierz and Duch, Wlodzislaw and Đorđević, Zoran and Spasić, Slađana",
year = "2010",
abstract = "In this article structures in biological signals are treated. The simpler-directly visible in the signals, which still demand serious methods and algorithms in the feature detection, similarity investigation and classification. The major actions in this domain are of geometric, thus simpler sort, though there are still hard problems related to simple situations. The other large class of less simple signals unsuitable for direct geometric or statistic approach, are signals with interesting frequency components and behavior, those suitable for spectroscopic analysis. Semantics of spectroscopy, spectroscopic structures and research demanded operations and transformations on spectra and time spectra are presented. The both classes of structures and related analysis methods and tools share a large common set of algorithms, all of which aiming to the full automatization. Some of the signal features present in the brain signal patterns are demonstrated, with the contexts relevant in BCI, brain computer interfaces. Mathematical representations, invariants and complete characterization of structures in broad variety of biological signals are in the central focus.",
publisher = "Springer, New York",
journal = "Journal of Signal Processing Systems for Signal Image and Video Technology",
title = "Detection of Structural Features in Biological Signals",
pages = "129-115",
number = "1",
volume = "60",
doi = "10.1007/s11265-009-0407-7"
}

Jovanović, A., Perović, A., Klonowski, W., Duch, W., Đorđević, Z.,& Spasić, S.. (2010). Detection of Structural Features in Biological Signals. in Journal of Signal Processing Systems for Signal Image and Video Technology
Springer, New York., 60(1), 115-129.
https://doi.org/10.1007/s11265-009-0407-7

Jovanović A, Perović A, Klonowski W, Duch W, Đorđević Z, Spasić S. Detection of Structural Features in Biological Signals. in Journal of Signal Processing Systems for Signal Image and Video Technology. 2010;60(1):115-129.
doi:10.1007/s11265-009-0407-7 .

Jovanović, Aleksandar, Perović, Aleksandar, Klonowski, Wlodzimierz, Duch, Wlodzislaw, Đorđević, Zoran, Spasić, Slađana, "Detection of Structural Features in Biological Signals" in Journal of Signal Processing Systems for Signal Image and Video Technology, 60, no. 1 (2010):115-129,
https://doi.org/10.1007/s11265-009-0407-7 . .