Clustering of entropy topography in epileptic electroencephalography

Abstract

Epileptic seizures have been considered sudden and unpredictable events for centuries. A seizure seems to occur when a massive group of neurons in the cerebral cortex begins to discharge in a highly organized rhythmic pattern, then it develops according to some poorly described dynamics. As proved by the results reported by different research groups, seizures appear not completely random and unpredictable events. Thus, it is reasonable to wonder when, where and why the epileptogenic processes start up in the brain and how they result in a seizure. In order to detect these phenomena from the very beginning (hopefully minutes before the seizure itself), we introduced a technique, based on entropy topography, that studies the synchronization of the electric activity of neuronal sources in the brain. We tested it over 3 EEG data set from patients affected by partial epilepsy and 25 EEG recordings from patients affected by generalized seizures as well as over 40 recordings from healthy subjects. Entropy showed a very steady spatial distribution and appeared linked to the brain zone where seizures originated. A self-organizing map-based spatial clustering of entropy topography showed that the critical electrodes shared the same cluster long time before the seizure onset. The healthy subjects showed a more random behaviour.

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References

  1. 1.

    Im C-H, Jung H-K, Jung K-Y, Lee SY (2007) Reconstruction of continuous and focalized brain functional source images from electroencephalography. IEEE Trans Magnet 43(4):1709–1712

    Article  Google Scholar 

  2. 2.

    Im C-H, Lee C, An K-O, Jung H-K, Jung K-Y, Lee SY (2007) Precise estimation of brain electrical sources using anatomically constrained area source (acas) localization. IEEE Trans Magnet 43(4):1713–1716

    Article  Google Scholar 

  3. 3.

    Knutsson E, Hellstrand E, Schneider S, Striebel W (1993) Multichannel magnetoencephalography for localization of epileptogenic activity in intractable epilepsies. IEEE Trans Magnet 29(6):3321–3324

    Article  Google Scholar 

  4. 4.

    Sackellares JC, Iasemidis LD, Gilmore RL, Roper SN (1986) Clinical application of computed EEG topography. In: Duffy FH (eds) Topographic mapping of brain electrical activity. Butterworths, Boston

    Google Scholar 

  5. 5.

    Nuwer MR (1988) Quantitative eegs. J Clin Neurophysiol 5:1–86

    Article  Google Scholar 

  6. 6.

    Babiloni C, Binetti G, Cassetta E, Cerboneschi D, Dal Forno G, Del Percio C, Ferreri F, Ferri R, Lanuzza B, Miniussi C, oretti DVM, Nobili F, Pascual-Marqui RD, Rodriguez G, Romani G, Salinari S, Tecchio F, Vitali P, Zanetti O, Zappasodi F, Rossini PM (2004) Mapping distributed sources of cortical rhythms in mild alzheimer’s disease. A multicentric EEG study. NeuroImage 22:57–67

    Article  Google Scholar 

  7. 7.

    Miyagi Y, Morioka T, Fukui K, Kawamura T, Hashiguchi K, Yoshida F, Shono T, Sasaki T (1988) Spatio-temporal analysis by voltage topography of ictal electroencephalogram on mr surface anatomy scan for the localization of epileptogenic areas. Min Invasive Neurosurg 48(2):97–100

    Article  Google Scholar 

  8. 8.

    Ebersole JS (1997) Defining epileptic foci: past, present, future. J Clin Neurophysiol 14:470–483

    Article  Google Scholar 

  9. 9.

    Scherg M (1994) From EEG source localization to source imaging. Acta Neurol Scand 152:29–30

    Article  Google Scholar 

  10. 10.

    Tekgul H, Bourgeois BF, Gauvreau K, Bergin AM (2005) Electroencephalography in neonatal seizures: comparison of a reduced and a full 10/20 montage. Pediatr Neurol 32(3):155–161

    Article  Google Scholar 

  11. 11.

    Nayak D, Valentin A, Alarcon G, Garcia Seoane JJ, Brunnhuber F, Juler J, Polkey CE, Binnie CD (2004) Characteristics of scalp electrical fields associated with deep medial temporal epileptiform discharges. J Clin Neurophysiol 115(6):1423–1435

    Article  Google Scholar 

  12. 12.

    Skrandies W, Dralle D (2004) Topography of spectral EEG and late vep components in patients with benign rolandic epilepsy of childhood. J Neural Transm 111(2):223–230

    Article  Google Scholar 

  13. 13.

    Hild KE II, Erdogmus D, Principe JC (2001) On-line minimum mutual information method for time varying blind source separation. In: 3rd International conference on independent component analysis and blind signal separation, pp 126–131

  14. 14.

    Mammone N, Morabito FC (2008) Enhanced automatic artifact detection based on independent component analysis and renyi’s entropy. Neural Netw 21(7):1029–1040

    Article  Google Scholar 

  15. 15.

    Kohonen T (1995) Self-organizing maps. Series in information sciences, vol 30. Springer, Heidelberg

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank the doctors of the Epilepsy Regional Center of the Riuniti Hospital of Reggio Calabria (Italy) for their insightful comments and suggestions.

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Correspondence to Nadia Mammone.

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Mammone, N., Inuso, G., La Foresta, F. et al. Clustering of entropy topography in epileptic electroencephalography. Neural Comput & Applic 20, 825–833 (2011). https://doi.org/10.1007/s00521-010-0505-2

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Keywords

  • Electroencephalography
  • Renyi’s entropy
  • Epilepsy
  • SOM