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Epilepsy is a neurological disease condition in which one experience seizures. These can be recorded using Electroencephalogram which is by nature long term recordings. A lot of work has been done towards automatic detection of these seizures in the literature with both short term and long term recordings. In this work, we have used the standard database of University of Bonn for a four different two class classification problem as addressed by various others in the literature. We have used novel single feature namely Tsallis entropy along with five different classifiers. Comparing with other literatures, we find that our method has the least computation time as low as 0.9 ms. We achieved a highest accuracy of 92.67–100% with Decision tree classifier for the four types of two class classification problem considered. Our method being very simple and also has fastest computation time in comparison with other features in the literature and thus can form as a software tool that can be installed easily and also opens future opportunities towards real time detection and prediction of epileptic seizures.

Tsallis entropy: as a new single feature with the least computation time for classification of epileptic seizures