Epileptic Seizure Detection and Prediction Based on Dynamic Changes in Cerebral Blood Flow
Epilepsy is the third most common neurological illness. There are about 200,000 new epilepsy cases reported in the U.S. every year. Approximately 20% - 25% of epileptic patients do not respond to medical treatments. One of the most disabling aspects of epilepsy is the sudden occurrence of seizure in patients without an aura or warning. Early detection of a seizure onset from a localized epileptic focus is critical for implementing appropriate preventive measures, either to suppress the seizure or to warn the individual to seek medical attention or a safe ground.
Currently, electroencephalography (EEG) is considered the gold standard of monitoring the neural discharges during an epileptic seizure. However, no reliable seizure detection tool has been developed based on EEG due to issues related to specificity, sensitivity to patients' motion, absence of reference period, and others. On the other hand, changes in cerebral blood flow (CBF) prior to the electrical discharge in the epileptic focus may be a reliable measure for seizure detection and prediction. The goal of this project is to develop an early seizure warning system for epileptic patients based on changes in CBF.
Our group has investigated CBF in the rat neocortex measured by laser Doppler technique.
We have also investigated CBF in epileptic patients by Thermal Diffusion Flowmetry (TDF). TDF probes are implanted between the cranium
and cortex of the patient, on both the epileptic and the non-epileptic sides of the brain, to measure blood perfusion through heat transfer
and temperature gradient in the brain tissue.
An algorithm can be developed to predict an impending seizure based on the CBF perturbation minutes before the onset.