---

Epilepsy is the most common serious brain disorder that characterized by recurrent seizures. Epilepsy affects 65 million people worldwide today and about two million new cases occur each year. The most negative aspect of seizure that causes the patient couldn’t have normal life, is its sudden and incontrollable features. So, the achievement of an algorithm that is capable to predicting the occurrence of seizures would help sufferers to live a normal life safe, and they can move out of harm's way. In this study, we proposed a prediction method for absence seizures based on the time-frequency analysis and complexity measure in EEG signals of WAG/Rij rats as a valid animal model of human absence epilepsy. We investigated the changes of permutation Entropy and the wavelet power of theta frequency range, simultaneously. The proposed seizures prediction algorithm was applied to long-term EEG recordings of WAG/Rij rats. The results indicate that the algorithm successfully detected the pre-ictal state prior to onset of seizures in 210 out of 298 seizures.The dependence of accuracy, sensitivity and anticipation time of prediction algorithm on program settings and attributes of EEG recordings are discussed.In this study, we found that the measure of PE reduced in pre-ictal and ictal states of EEG signals in these rats. The reduction of complexity of EEG signals prior to onset of seizures that was demonstrated by means of PE might be indicating the neural synchronization of brain networks in WAG/Rij rats.

---

Nowadays, sleep deprivation is a pervasive problem that affects human physical and mental health. In this research, the effects of sleep deprivation on brain function and its diagnosis have been studied using electroencephalogram (EEG) signals recorded from 30 subjects after complete sleep and one day of sleep deprivation with open and closed eyes. Linear features like signal power and nonlinear features consisting of Shannon, Renyi, sample, and permutation entropies were extracted from signals. We used the PCA algorithm and Wilcoxon feature ranking method to extract the superior features and employed SVM, KNN, and a Decision tree to detect sleep-deprived cases. Brain maps of extracted features were plotted using the sLORETA algorithm to investigate the effects of sleep deprivation. Based on the results, the decision tree classifier with 100 superior selected features of Wilcoxon achieved the best performance with accuracy and precision of 99.0% and 99.8%, respectively. Also, comparing the results of linear and nonlinear features reveals the impressive role of the nonlinear features in the classification problem of this work. The maps of the features revealed noticeable changes in the level of attention, concentration, decision-making, and visual and movement activities.