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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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_03784371_v347_n_p444_Rosso |
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todo:paper_03784371_v347_n_p444_Rosso2023-10-03T15:32:43Z Evidence of self-organization in brain electrical activity using wavelet-based informational tools Rosso, O.A. Martin, M.T. Plastino, A. 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. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_03784371_v347_n_p444_Rosso |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
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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 Rosso, O.A. Martin, M.T. Plastino, A. 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. |
| format |
JOUR |
| author |
Rosso, O.A. Martin, M.T. Plastino, A. |
| author_facet |
Rosso, O.A. Martin, M.T. Plastino, A. |
| author_sort |
Rosso, O.A. |
| 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 |
| url |
http://hdl.handle.net/20.500.12110/paper_03784371_v347_n_p444_Rosso |
| work_keys_str_mv |
AT rossooa evidenceofselforganizationinbrainelectricalactivityusingwaveletbasedinformationaltools AT martinmt evidenceofselforganizationinbrainelectricalactivityusingwaveletbasedinformationaltools AT plastinoa evidenceofselforganizationinbrainelectricalactivityusingwaveletbasedinformationaltools |
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1807324597312290816 |