Сибирский федеральный университет

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. These 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

• Egorova Liudmila (Siberian Federal University)

• РИНЦ (eLIBRARY.RU)