Time-domain exponential energy for epileptic EEG signal classification.

Automatic classification and prediction of epileptic electroencephalogram (EEG) signal are of great concern to the research community due to its non-stationary and non-linear properties. Features with minimal computation cost are highly needed for the rapid real-time precise diagnosis and implementation in the EEG scanning devices. Even though energy is a well-known feature for the analysis of signals, it is very rarely used in EEG analysis. An exponential energy feature in the time domain is proposed in this study. The proposed exponential energy feature provides a classification accuracy of 89% in the Bern-Barcelona EEG dataset and 99.5% in the Ralph Andrzejak EEG dataset. The promising results open a wide applicability of exponential energy in biomedical signal analysis.