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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| Autores principales: | , , |
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| Formato: | JOUR |
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_03784371_v347_n_p444_Rosso |
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| Sumario: | 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. |
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