Clustering of entropy topography in epileptic electroencephalography
- First Online:
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.
KeywordsElectroencephalography Renyi’s entropy Epilepsy SOM
- 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, BostonGoogle Scholar
- 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 22:57–67CrossRefGoogle Scholar
- 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 126–131Google Scholar
- 15.Kohonen T (1995) Self-organizing maps. Series in information sciences, vol 30. Springer, HeidelbergGoogle Scholar