Global optimization and spatial synchronization changes prior to epileptic seizures

Abstract

Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. In this paper, a comparative study involving a measure of chaos, in particular the short-term Lyapunov exponent (STLmax), a measure of phase (ϕmax) and a measure of energy (E) is carried out to detect the dynamical spatial synchronization changes that precede temporal lobe epileptic seizures. The measures are estimated from intracranial electroencephalographic (EEG) recordings with sub-dural and in-depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal synchronization. It is shown that spatial synchronization, as measured by the convergence of STLmax, ϕmax and E of critical sites selected by optimization versus randomly selected sites leads to long-term seizure predictability. Finally, it is shown that the seizure predictability period using STlmax is longer than that of the phase or energy synchronization measures. This points out the advantages of using synchronization of the STlmax measure in conjunction with optimization for long-term prediction of epileptic seizures.