Seizure prediction with cross-higher-order spectral analysis of EEG signals

Abstract

Epilepsy is a neurological disorder that happens because of the propagation of abnormal signals produced by clusters of neurons in the brain. The majority of those with epileptic seizures can be treated by drug therapies and surgery. However, 25% of the patients with diagnosed epilepsy continue to have seizures. Seizures can cause serious injuries and limit the independence and mobility of an individual. Seizure detection and prediction could lead to a better understanding of seizures and with that help preventing patient injury.This paper discusses extraction and evaluation of nonlinear multivariate features using the cross-bispectral method to help predict epileptic seizure occurrences. These ten statistic features were employed to discriminate pre-ictal from interictal states. Therefore, the features were given to the support vector machine classifier as the input. Outputs were then processed in order to evaluate the sensitivity, false positive rate (FPR) and the prediction time. The proposed method obtained sensitivity of 100% and average FPR of 0.044 per hour by using the “Freiburg epileptic seizure prediction” dataset. This high sensitivity index and low FPR index compared with other studies show the ability of cross-higher-order spectral method to analyze epileptic EEG signals. The proposed method is also fast and easy and may be helpful in other applications of EEG analysis such as sleep stage identification and brain–computer interface.