Seizure Prediction Methods: A Review of the Current Predicting Techniques

Epilepsy is a common neurological disorder affecting more than 1.5 percent worldwide. Twenty percent of epilepsies are drug resistant. Therefore, early detection of prediction of epileptic seizures is of prime significance to decrease the burdens of the disease. There is strong evidence indicating seizures develop minutes to hours before clinical onset. This change is based on quantitative studies of long term electroencephalographic monitoring (EEG) from patients administered for epilepsy surgery. The possibility of early prediction of seizure has drawn the research interest of diverse fields in medical, engineering, and patent publications. Techniques used to predict seizures include frequency-based methods, statistical analysis of EEG signals, non-linear dynamics (chaos), and intelligent expert systems. Developing efficient methods to predict seizures can lead to designing novel diagnostic and therapeutic techniques for the early diagnosis of seizure attack or preventing the attacks through appropriate modulations of brain activities. The present study reviews the most important and recent methods for seizure prediction. In line with introduction of different efficient seizure predicting approaches, great research interest has been focused on developing new modalities that incorporate these approaches to predict early onset of seizures minutes to hours before they initiate. These modalities will enable experts to develop new interventional treatments such as appropriate responsive electric stimulation applied immediately after the prediction to prevent the seizures or modulating stimulation in controlling the seizure attacks. In the near future, seizures can be predicted in time and prevented before clinical and physical indications.