Dispersion measures and entropy for seizure detection

Electroencephalogram (EEG) is an important technique for detecting epileptic seizures. In this paper a method of classification of EEG signal into normal, interictal and ictal classes is presented. Statistical measures such as median absolute deviation (MAD), variance and entropy showing the dispersion and rhythmicity, were calculated for each frame of EEG signals. The classification was done using a linear classifier. The direct time domain approach adopted without resorting into any kind of transformations yields an accuracy of 100%.