Evidence of self-organization in brain electrical activity using wavelet-based informational tools
✍ Scribed by O.A. Rosso; M.T. Martin; A. Plastino
- Publisher
- Elsevier Science
- Year
- 2005
- Tongue
- English
- Weight
- 691 KB
- Volume
- 347
- Category
- Article
- ISSN
- 0378-4371
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✦ Synopsis
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, C W ), when applied to tonic-clonic epileptic EEG 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 ðDH ¼ hH ðictalÞ i À hH ðpre-ictalÞ iÞ is negative in 95% of the cases ðpo0:0001Þ; and the mean complexity variation ðDC W ¼ hC ðictalÞ W i À hC ðpre-ictalÞ W iÞ is positive in 85% of the cases ðp ¼ 0:0002Þ: 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.