Gaidar, V. O.V. O.Gaidar2026-06-302026-06-302018Gaidar, V. O. (2018). Machine learning for epilepsy detection and forecast review: new challenges and perspectives. Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics(4), 98–101. https://doi.org/10.17721/1812-5409.2018/4.1410.17721/1812-5409.2018/4.14https://ir.library.knu.ua/handle/15071834/26313The comparative analysis of machine learning methods has performed to solve the problem of early detection and prediction of epileptic seizures using electroencephalographic signals. Recent studies has shown that it is possible to predict seizures in prior of its physical appearance. Our goal is to present and analyse different approaches of seizure prediction techniques, particulary in machine learning and deep learning. Seizure prediction has made important advances over the last decade, nevertheless it is still a problem to provide steady algorithm of seizure early detection. Also, within individual patients exhibit distinctive dynamics, is it cruicial to find algorithms providing greater clinical utility. This article focuses of the problem of features development from electroencephalography signals in order to provide the accurate pattern recognition techniques for detection and classification of epilepsy seizures in advance. The mathematical model of the algorithms is constructed and quantitative data presented for estimating the methods efficiency.Key words: machine learning, epilepsy, electroencephalogram, preictal seizures.Pages of the article in the issue: 98-101Language of the article: UkrainianThe comparative analysis of machine learning methods has performed to solve the problem of early detection and prediction of epileptic seizures using electroencephalographic signals. Recent studies has shown that it is possible to predict seizures in prior of its physical appearance. Our goal is to present and analyse different approaches of seizure prediction techniques, particulary in machine learning and deep learning. Seizure prediction has made important advances over the last decade, nevertheless it is still a problem to provide steady algorithm of seizure early detection. Also, within individual patients exhibit distinctive dynamics, is it cruicial to find algorithms providing greater clinical utility. This article focuses of the problem of features development from electroencephalography signals in order to provide the accurate pattern recognition techniques for detection and classification of epilepsy seizures in advance. The mathematical model of the algorithms is constructed and quantitative data presented for estimating the methods efficiency.Key words: machine learning, epilepsy, electroencephalogram, preictal seizures.Pages of the article in the issue: 98 - 101Language of the article: UkrainianenСтаття