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 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 (ΔH=〈H(ictal)〉-〈H(pre-ictal)〉) is negative in 95% of the cases (p<0.0001), and the mean complexity variation (ΔCW=〈CW(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.