Analysis of the multi-channel epileptiform EEG using the Markov chains formalism.
A multi-level scheme of syntactic reduction of the epileptiform EEG data is briefly discussed and the possibilities it opens up in describing the dynamic behaviour of a multi-channel system are indicated. A new algorithm for the inference of a Markov network from finite sets of sample symbol strings is introduced. Formulae for the time-dependent state occupation probabilities, as well as joint probability functions for pairs of channels, are given. An exemplary case of analysis in these terms, taken from an investigation of anticonvulsant drug effects on EEG seizure patterns, is presented.
Penczek
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Grochulski
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D000465:Algorithms; D000818:Animals; D000927:Anticonvulsants / Q000494:pharmacology; D001931:Brain Mapping; D004567:Electrodes, Implanted; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology*; D006801:Humans; D008390:Markov Chains; D008959:Models, Neurological; D010363:Pattern Recognition, Automated; D011336:Probability; D011817:Rabbits
D000927:Anticonvulsants