Discrimination of scalp EEG signals in wavelet transform domain and channel selection for the patient-invariant seizure detection

Abstract

In this paper, a statistical method of classifying Electroencephalogram (EEG) data for automatic detection of epileptic seizure is carried out using a publicly available scalp EEG database. The classification is carried out to distinguish the seizure segments from the non-seizure ones. The higher order moments (specifically variance) have been calculated in various sub-bands in the wavelet transform domain and utilized as the discriminating feature in the Support Vector Machine(SVM) classifier. The method is tested on 175 hours of continuous EEG data from five patients and on an average, 99% accuracy has been achieved with very high values of sensitivity and specificity. Furthermore, on the basis of the figure of merits, for their excellent performance for all the patients, seven channels have been selected for the patient-invariant seizure detection which might help the electroencephalographers reducing their laborious job of monitoring the EEG data from all the channels.