Brain tumor classification based on EEG hidden dynamics

The hard problem of brain tumor detection based on rest EEG analysis is investigated, relying on the hypothesis that the EEG signal contains more hidden useful information than what is clinically employed. A nonlinear analysis is applied to the pair (F3, F4) of EEG leads, that describe the electrical activity of the left and right brain hemisphere respectively. The hidden dynamic of the pair (F3, F4) is tested against a hierarchy of null hypotheses, corresponding to one-and two-dimensional nonlinear Markov models of increasing order. An approximative measure of information ow, based on higher order cumulants, quantiies the hidden dynamic of each time series and is used as a discriminating statistic for testing the null hypotheses. The minimum order of the accepted Markov models represents a measure of the intrinsic nonlinearity of the underlying system. Rest EEG records of 6 patients with evidence of meningeoma or malignant glioma in lead F4, or without any pathology, are investigated. A high order hidden dynamic is detected in normal EEG records, connrming the very complex structure of the underlying system. Diierent interdependence degrees between the hidden dynamics of leads (F3, F4) discriminate meningeoma, malignant glioma, and no pathological status, while loss of structure in the hidden dynamic can represent a good hint for glioma/meningeoma localization.