Seizure prediction through dynamic synchronization measures of neural populations

Recent studies have focused on the phenomena of abnormal electrical brain activity which may transition into a debilitating seizure state through the entrainment of large populations of neurons. Starting from the initial epileptogenisis of a small population of abnormally firing neurons, to the mobilization of mesoscopic neuron populations behaving in a synchronous manner, a prediction methodology has been formulated that compares the initial epileptogenisis to distant neuron populations. As two neuron populations begin to operate in a synchronized manner, the respective signals phase lock, manifesting into a seizure state. The normal non-linear dynamic signal captured through an EEG enters a semi-periodic state, which can be quantified into a seizure state. A method for capturing synchronous behavior of the pathological brain state is described. An individual patient based phase-locking threshold is introduced for seizure prediction and for differentiating seizure and non-seizure states.