Characterization of time dynamical evolution of electroencephalographic epileptic records
Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts...
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2002
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v312_n3-4_p469_Rosso http://hdl.handle.net/20.500.12110/paper_03784371_v312_n3-4_p469_Rosso |
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paper:paper_03784371_v312_n3-4_p469_Rosso2023-06-08T15:39:57Z Characterization of time dynamical evolution of electroencephalographic epileptic records EEG Epileptic seizures Nonlinear dynamics metrics tools Signal entropy Time-frequency signal analysis Wavelet analysis Brain Chaos theory Characterization Electrodes Entropy Lyapunov methods Neurology Nonlinear systems Time series analysis Signal entropy Electroencephalography Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of the brain dynamics. The processing of information by the brain is reflected in dynamical changes of the electrical activity in time, frequency, and space. Therefore, the concomitant studies require methods capable of describing the qualitative variation of the signal in both time and frequency. The entropy defined from the wavelet functions is a measure of the order/disorder degree present in a time series. In consequence, this entropy evaluates over EEG time series gives information about the underlying dynamical process in the brain, more specifically of the synchrony of the group cells involved in the different neural responses. The total wavelet entropy results independent of the signal energy and becomes a good tool for detecting dynamical changes in the system behavior. In addition the total wavelet entropy has advantages over the Lyapunov exponents, because it is parameter free and independent of the stationarity of the time series. In this work we compared the results of the time evolution of the chaoticity (Lyapunov exponent as a function of time) with the corresponding time evolution of the total wavelet entropy in two different EEG records, one provide by depth electrodes and other by scalp ones. © 2002 Published by Elsevier Science B.V. 2002 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v312_n3-4_p469_Rosso http://hdl.handle.net/20.500.12110/paper_03784371_v312_n3-4_p469_Rosso |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
EEG Epileptic seizures Nonlinear dynamics metrics tools Signal entropy Time-frequency signal analysis Wavelet analysis Brain Chaos theory Characterization Electrodes Entropy Lyapunov methods Neurology Nonlinear systems Time series analysis Signal entropy Electroencephalography |
| spellingShingle |
EEG Epileptic seizures Nonlinear dynamics metrics tools Signal entropy Time-frequency signal analysis Wavelet analysis Brain Chaos theory Characterization Electrodes Entropy Lyapunov methods Neurology Nonlinear systems Time series analysis Signal entropy Electroencephalography Characterization of time dynamical evolution of electroencephalographic epileptic records |
| topic_facet |
EEG Epileptic seizures Nonlinear dynamics metrics tools Signal entropy Time-frequency signal analysis Wavelet analysis Brain Chaos theory Characterization Electrodes Entropy Lyapunov methods Neurology Nonlinear systems Time series analysis Signal entropy Electroencephalography |
| description |
Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of the brain dynamics. The processing of information by the brain is reflected in dynamical changes of the electrical activity in time, frequency, and space. Therefore, the concomitant studies require methods capable of describing the qualitative variation of the signal in both time and frequency. The entropy defined from the wavelet functions is a measure of the order/disorder degree present in a time series. In consequence, this entropy evaluates over EEG time series gives information about the underlying dynamical process in the brain, more specifically of the synchrony of the group cells involved in the different neural responses. The total wavelet entropy results independent of the signal energy and becomes a good tool for detecting dynamical changes in the system behavior. In addition the total wavelet entropy has advantages over the Lyapunov exponents, because it is parameter free and independent of the stationarity of the time series. In this work we compared the results of the time evolution of the chaoticity (Lyapunov exponent as a function of time) with the corresponding time evolution of the total wavelet entropy in two different EEG records, one provide by depth electrodes and other by scalp ones. © 2002 Published by Elsevier Science B.V. |
| title |
Characterization of time dynamical evolution of electroencephalographic epileptic records |
| title_short |
Characterization of time dynamical evolution of electroencephalographic epileptic records |
| title_full |
Characterization of time dynamical evolution of electroencephalographic epileptic records |
| title_fullStr |
Characterization of time dynamical evolution of electroencephalographic epileptic records |
| title_full_unstemmed |
Characterization of time dynamical evolution of electroencephalographic epileptic records |
| title_sort |
characterization of time dynamical evolution of electroencephalographic epileptic records |
| publishDate |
2002 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v312_n3-4_p469_Rosso http://hdl.handle.net/20.500.12110/paper_03784371_v312_n3-4_p469_Rosso |
| _version_ |
1768546446288617472 |