Epileptic seizure prediction using zero-crossings analysis of EEG wavelet detail coefficients

Predicting the occurrence of epileptic seizures can provide an enormous aid to epileptic patients. This paper introduces a novel patient-specific method for seizure prediction applied to scalp Electroencephalography (EEG) signals. The proposed method relies on the count of zero-crossings of wavelet detail coefficients of EEG signals as the major feature. This is followed by a binary classifier that discriminates between preictal and interictal states. The proposed method is practical for real-time applications given its computational efficiency as it uses an adaptive algorithm for channel selection to identify the optimum number of needed channels. Moreover, this method is robust against the variability across seizures for the same patient. Applied to data from 8 patients, the proposed method achieved high accuracy and sensitivity with an average accuracy of 94% and an average sensitivity of 96%. These results were obtained using only 10 minutes of training data as opposed to using hours of recordings typically used in traditional approaches.