Transactions on Machine Intelligence 2821-1693 2 4 2019 12 01 Comparative Prediction of Epileptic Seizures Based on Phase Synchronization in EEG 235 245 220852 10.47176/TMI.2019.235 EN S. H. Shokouh-Alaei Faculty of Engineering, Islamic Azad University, Mashhad branch, Mashhad, Iran M. A. Khalilzadeh Faculty of Engineering, Islamic Azad University, Mashhad branch, Mashhad, Iran Journal Article 2019 06 20 Epileptic seizure prediction has garnered significant attention in recent years due to its potential to improve patient outcomes and reduce the burden of epilepsy. The integration of advanced machine learning techniques, particularly deep learning, into seizure prediction models has opened new avenues for research. Currently, epilepsy patients who have not achieved complete seizure control face the challenge of sudden and unpredictable epileptic seizures. A method capable of predicting seizure onset could significantly enhance the quality of life for these individuals. The fundamental basis of seizure prediction lies in distinguishing the preictal phase dynamics from other phases. In this study, phase synchronization is employed as an indicator for analyzing interactions between different brain regions and as a reliable metric for identifying the preictal period. Furthermore, seizure prediction horizon and seizure onset time are two critical factors in evaluating seizure prediction methods. Although previous studies have primarily used these temporal parameters for assessment, this paper incorporates them into a neuro-fuzzy model, allowing for adaptive seizure prediction based on patient feedback. The implementation of the proposed model on intracranial EEG signals demonstrated that, across various time window values, sensitivity and specificity exceeded 70%. https://www.tmachineintelligence.ir/article_220852_e9d53751ae42c2e9bea9d0c2b0a2c79c.pdf