This paper considers a problem of detection of EEG signals. For this purpose, we propose a criterion based on univariate maximum value of the signal in order to detect the onset of severe possible seizure. We construct a hypothesis test based on this criterion according to the distribution form of our data in order to use the likelihood for the estimation of parameters. Our main contribution is the automatic detection of the breaks of seizure, as the examination of EEG signals is often done via visual inspection of the amplitude in the temporal domain by neurologist practitioners. The results of numerical simulations of the test and application to real EEG data are presented. The accuracy of detection is estimated to 64.88%, with the sensitivity estimated as 83.33%.

hypothesis testing, maxima, moving average, standard deviation, parameter estimation, electroencephalography (EEG), epilepsy.