Epileptic Seizure Prediction based on Ratio and Differential Linear Univariate Features

Bivariate features, obtained from multichannel electroencephalogram recordings, quantify the relation between different brain regions.studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients sufferingfrom refractory epilepsy. a new bivariate approach using univariate features is proposed here. differences and ratios of 22 linearunivariate features were calculated using pairwise combination of 6 electroencephalograms channels, to create 330 differential, and330 relative features. the feature subsets were classified using support vector machines separately, as one of the two classes ofpreictal and nonpreictal. furthermore, minimum redundancy maximum relevance feature reduction method is employed to improvethe predictions and reduce the number of false alarms. the studies were carried out on features obtained from 10 patients. for reducedsubset of 30 features and using differential approach, the seizures were on average predicted in 60.9% of the cases. results of bivariate approaches were compared with those achievedfrom original linear univariate features, extracted from 6 channels. the advantage of proposed bivariate features is the smaller numberof false predictions in comparison to the original 22 univariate features. in addition, reduction in feature dimension could provide aless complex and the more cost?effective algorithm. results indicate that applying machine learning methods on a multidimensionalfeature space resulting from relative/differential pairwise combination of 22 univariate features could predict seizure onsets with highperformance.