Brain tumor classification based on EEG hidden dynamics

Abstract

The hard problem of brain tumor detection based on rest ElectroEncephaloGraphic (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 flow, based on higher order cumulants, quantifies 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, confirming the very complex structure of the underlying system. Different inter-dependence 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.