Epileptic Seizure Prediction by Using Empirical Mode Decomposition and Complexity Analysis of Single-Channel Scalp Electroencephalogram

Abstract-This paper presents a new approach to recognize and predict succedent epileptic seizures by using single-channel electroencephalogram (EEG) analysis. Eight channels of EEG from each patient of the seven consenting patients with generalized epilepsy were collected in Epilepsy Center of Xijing Hospital. The raw EEGs were decomposed by the algorithm of empirical mode decomposition (EMD), the complxity measures were extracted from the seven compenents of EMD, and then a four layer(7-6-2-1) artificial neural network(ANN) was employed for prediction. The performance obtained for the proposed scheme in predicting seizures is: sensitivity 50~77.8%, specificity 71.4~88.1% and accuracy 71.7~78.3%, depending on the different EEG leads. This method is also computationally fast and can be used to monitor epilepsy for real-time clinical application.