Evidence of self-organization in brain electrical activity using wavelet-based informational tools

In the present work, we show that appropriate information-theory tools based on the wavelet transform (relative wavelet energy; normalized total wavelet entropy, H; generalized wavelet complexity, CW), when applied to tonic-clonic epileptic EEC data, provide one with valuable insights into the dynam...

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Detalles Bibliográficos
Publicado: 2005
Materias:
Complexity
EEG
Epileptic seizures
Signal entropy
Time-frequency signal analysis
Wavelet analysis
Brain
Computational complexity
Data reduction
Electroencephalography
Entropy
Signal theory
Wavelet transforms
Information theory
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v347_n_p444_Rosso
http://hdl.handle.net/20.500.12110/paper_03784371_v347_n_p444_Rosso
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_03784371_v347_n_p444_Rosso
record_format dspace
spelling paper:paper_03784371_v347_n_p444_Rosso2025-07-30T18:16:35Z Evidence of self-organization in brain electrical activity using wavelet-based informational tools Complexity EEG Epileptic seizures Signal entropy Time-frequency signal analysis Wavelet analysis Brain Computational complexity Data reduction Electroencephalography Entropy Signal theory Wavelet transforms Epileptic seizures Signal entropy Time-frequency signal analysis Wavelet analysis Information theory In the present work, we show that appropriate information-theory tools based on the wavelet transform (relative wavelet energy; normalized total wavelet entropy, H; generalized wavelet complexity, CW), when applied to tonic-clonic epileptic EEC data, provide one with valuable insights into the dynamics of neural activity. Twenty tonic-clonic secondary generalized epileptic records pertaining to eight patients have been analyzed. If the electromyographic activity is excluded the difference between the ictal and pre-ictal mean entropic values (ΔH = 〈H(ictal)〉 - 〈H(pre-ictal)〉) is negative in 95% of the cases (p< 0.0001), and the mean complexity variation (ΔCW = 〈C W (ictal)〉 - 〈CW (pre-ictal)〉) is positive in 85% of the cases (p = 0.0002). Thus during the seizure entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus in this kind of seizures triggers a self-organized brain state characterized by both order and maximal complexity. © 2004 Published by Elsevier B.V. 2005 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v347_n_p444_Rosso http://hdl.handle.net/20.500.12110/paper_03784371_v347_n_p444_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 Complexity
EEG
Epileptic seizures
Signal entropy
Time-frequency signal analysis
Wavelet analysis
Brain
Computational complexity
Data reduction
Electroencephalography
Entropy
Signal theory
Wavelet transforms
Epileptic seizures
Signal entropy
Time-frequency signal analysis
Wavelet analysis
Information theory
spellingShingle Complexity
EEG
Epileptic seizures
Signal entropy
Time-frequency signal analysis
Wavelet analysis
Brain
Computational complexity
Data reduction
Electroencephalography
Entropy
Signal theory
Wavelet transforms
Epileptic seizures
Signal entropy
Time-frequency signal analysis
Wavelet analysis
Information theory
Evidence of self-organization in brain electrical activity using wavelet-based informational tools
topic_facet Complexity
EEG
Epileptic seizures
Signal entropy
Time-frequency signal analysis
Wavelet analysis
Brain
Computational complexity
Data reduction
Electroencephalography
Entropy
Signal theory
Wavelet transforms
Epileptic seizures
Signal entropy
Time-frequency signal analysis
Wavelet analysis
Information theory
description In the present work, we show that appropriate information-theory tools based on the wavelet transform (relative wavelet energy; normalized total wavelet entropy, H; generalized wavelet complexity, CW), when applied to tonic-clonic epileptic EEC data, provide one with valuable insights into the dynamics of neural activity. Twenty tonic-clonic secondary generalized epileptic records pertaining to eight patients have been analyzed. If the electromyographic activity is excluded the difference between the ictal and pre-ictal mean entropic values (ΔH = 〈H(ictal)〉 - 〈H(pre-ictal)〉) is negative in 95% of the cases (p< 0.0001), and the mean complexity variation (ΔCW = 〈C W (ictal)〉 - 〈CW (pre-ictal)〉) is positive in 85% of the cases (p = 0.0002). Thus during the seizure entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus in this kind of seizures triggers a self-organized brain state characterized by both order and maximal complexity. © 2004 Published by Elsevier B.V.
title Evidence of self-organization in brain electrical activity using wavelet-based informational tools
title_short Evidence of self-organization in brain electrical activity using wavelet-based informational tools
title_full Evidence of self-organization in brain electrical activity using wavelet-based informational tools
title_fullStr Evidence of self-organization in brain electrical activity using wavelet-based informational tools
title_full_unstemmed Evidence of self-organization in brain electrical activity using wavelet-based informational tools
title_sort evidence of self-organization in brain electrical activity using wavelet-based informational tools
publishDate 2005
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v347_n_p444_Rosso
http://hdl.handle.net/20.500.12110/paper_03784371_v347_n_p444_Rosso
_version_ 1840322836689846272

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