A new system theoretic classifier for detection and prediction of epileptic seizures

A system theoretic computational approach has been recently proposed as a generalization of probabilistic networks for modeling complex systems. The computational approach, fuzzy measure-theoretic quantum approximation of an abstract system (FMQAS), generates a system measure between each pair of system objects as a relative measure of association incorporating, through a hierarchical iterative procedure, both the local and global significance of the interaction. FMQAS provides the basis for a new classification algorithm. A preliminary modification of this classification algorithm for temporal sequences is used to analyze electroencephalogram (EEG) data obtained in the temporal neighborhood of a seizure episode to obtain distinct state descriptions (patient invariant characterizations) of the seizure states. This state characterization enables seizure detection before onset with sufficient time to warn the individual or execute actions to abort the seizure formation.