Detection and Prediction of Absence Seizures Based on Nonlinear Analysis of the EEG in Wag/Rij Animal Model Saleh Lashkari 1 , Ali Sheikhani 1* , Mohammad Reza Hashemi Golpayegani 2 , Ali Moghimi 3 , Hamidreza Kobravi 4 1 Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 2 Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran 3 Rayan Center for Neuroscience & Behavior, Department of Biology, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran 4 Biomedical Engineering Research Center, Mashhad Branch, Islamic Azad University, Mashhad, Iran Abstract Background: Epilepsy is a common neurological disorder with a prevalence of 1% of the world population. Absence epilepsy is a form of generalized seizures with Spike wave discharge in EEG. Epileptic patients have frequent absence seizures that cause immediate loss of consciousness. Methods: In this study, it has been tried to explore whether EEG changes can effectively detect epilepsy in animal model applying non-linear features. To predict the occurrence of absence epilepsy, a long-term EEG signal has been recorded from frontal cortex in seven Wag/Rij rats. After preprocessing, the data was transferred to the phase space to extract the brain system dynamic and geometric properties of this space. Finally, the ability of each features to predict and detect absence epilepsy with two criteria of predictive time and the accuracy of detection and its results were compared with previous studies. Results: The results indicate that the brain system dynamic changes during the transition from free- seizure to pre-seizure and then seizure. Proposed approach diagnostic characteristics yielded 97% accuracy of absence epilepsy diagnosis indicating that due to the nonlinear and complex nature of the system and the brain signal, the use of methods consistent with this nature is important in understanding the dynamic transfer between different epileptic seizures. Conclusion: By changing the state of the absence Seizures, the dynamics are changing, and the results of this research can be useful in real-time applications such as predicting epileptic seizures. Keywords: Component; Absence epilepsy, Electroencephalogram, Phase space, Nonlinear attractor, Geometric properties *Correspondence to Ali Sheikhani, Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran. Email: sheikhani al 81@srbiau.ac.ir Published online March 15, 2018 Int Clin Neurosci J. 2018 Winter;5(1):21-27 Original Article International Clinical Neuroscience Journal Open Access Scan to access more free content © 2018 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. doi:10.15171/icnj.2018.05 Citation: Lashkari S, Sheikhani A, Hashemi Golpayegani MR, Moghimi A, Kobravi H. Detection and prediction of absence seizures based on nonlinear analysis of the EEG in Wag/Rij animal model. Int Clin Neurosci J. 2018;5(1):21-27. doi: 10.15171/icnj.2018.05. Introduction Epilepsy is one of the most common neurological disorders with a prevalence of 1% of the world’s population. About 80% of people with epilepsy live in developing countries. 1 Today, people with epilepsy and their families suffer from social discrimination and social stigma in many parts of the world. Epileptic seizures can be associated with impairment or loss of consciousness. 1,2 Absence seizures are a form of generalized seizures with Spike wave discharge (SWD) in EEG. 2 These rapid and sudden seizures are transient symptoms and/or signs of abnormal, excessive or synchronous neuronal activity in the brain. 3 People with absence epilepsy have repeated seizures that cause momentary lapses of consciousness. 4 The period of short-lived absence seizures usually lasts from several seconds to about a minute, and may be repeated more than 100 times a day. 5 Since these sudden and rapid seizures often occur in childhood or adolescence, and may have significant impact on the educational development of patients. 6,7 Therefore, understanding the transition of brain activity to the absence seizure, called the pre-seizure, is a very difficult goal and is still under discussion. 8,9 The EEG, which records spontaneous electrical activity in the brain, was first measured in humans by Hans Berger in 1929. Since then, EEG is one of the most useful tools for studying cognitive processes and brain physiopathology, 9,10 especially the processes involved in epileptic seizures. 11,12 In the recent decade, dynamics from free-seizure to seizure status has been investigated with different linear or nonlinear methods. 10-12 To some extent, these results