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Since the manual detection of electrographic seizures in continuouselectroencephalogram (EEG) monitoring is very time-consuming and requires atrained expert, attempts to develop automatic seizure detection are diverse andongoing. Machine learning approaches are intensely being applied to thisproblem due to their ability to classify seizure conditions from a large amountof data, and provide pre-screened results for neurologists. Several features,data transformations, and classifiers have been explored to analyze andclassify seizures via EEG signals. In the literature, some jointly-appliedfeatures used in the classification may have shared similar contributions,making them redundant in the learning process. Therefore, this paper aims tocomprehensively summarize feature descriptions and their interpretations incharacterizing epileptic seizures using EEG signals, as well as to reviewclassification performance metrics. To provide meaningful information offeature selection, we conducted an experiment to examine the quality of eachfeature independently. The Bayesian error and non-parametric probabilitydistribution estimation were employed to determine the significance of theindividual features. Moreover, a redundancy analysis using a correlation-basedfeature selection was applied. The results showed that the following features--variance, energy, nonlinear energy, and Shannon entropy computed on a raw EEGsignal, as well as variance, energy, kurtosis, and line length calculated onwavelet coefficients-- were able to significantly capture the seizures. Animprovement of 4.77--13.51% in the Bayesian error from the baseline wasobtained.

A review of feature extraction and performance evaluation in epileptic seizure detection using EEG