Statistical parameters in the dual tree complex wavelet transform domain for the detection of epilepsy and seizure

In this paper, a comprehensive statistical analysis of electroencephalogram (EEG) signals is carried out in the dual tree complex wavelet transform domain using a publicly available EEG database. It is shown that variance and kurtosis can be effective in distinguishing EEG signals at sub-band levels. It is further shown that the parameters of a normal inverse Gaussian probability density function can equally discriminate the EEG signals at sub-band levels. Thus, these statistical quantities may be used to characterize EEG signals and help the researchers in developing improved classifiers for the detection of epilepsy and seizure and building a better understanding of the diverse process of EEG signals.