Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022); Krasnoyarsk; Krasnoyarsk
Год издания: 2022
Идентификатор DOI: 10.15405/epct.23021.34
Ключевые слова: Electroencephalogram data analysis, eeg, automatic seizure detection, entropy, machine learning
Аннотация: The article explores the possibility of using the entropy characteristics of the time series of the electroencephalogram signal for the task of automatically detecting epileptic seizures by electroencephalogram recording. Because of the brain is a complex distributed active environment, selfoscillating processes take place in it. TПоказать полностьюhese processes can be judged by the EEG signal, which is a reflection of the total electrical activity of brain neurons. Based on the assumption that during an epileptic seizure, excessive synchronization of neurons occurs, leading to a decrease in the dynamic complexity of the electroencephalographic signal, entropy can be considered as a parameter characterizing the degree of systemic chaos. The sample entropy method is a robust method for calculating entropy for short time series. In this work, the sample entropy was calculated for an electroencephalographic record of a patient with epilepsy obtained from an open set of clinical data. The calculation was made for different sections of the recording, corresponding to the norm and pathology (generalized epileptic seizure). It has been shown that the entropy characteristics of the electroencephalogram signal can serve as informative features for machine learning algorithms to automatically detect signs of neurological pathology associated with epilepsy.
Журнал: HYBRID METHODS OF MODELING AND OPTIMIZATION IN COMPLEX SYSTEMS
Номера страниц: 275-282
Место издания: London, United Kingdom
Издатель: European Proceedings