Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-II-2023); Krasnoyarsk; Krasnoyarsk

Год издания: 2024

Идентификатор DOI: 10.1051/itmconf/20245903017

Аннотация: Abstract. The paper proposes a binary classification model designed to analyze electroencephalograms data to detecting pathologies associated with epilepsy. The model is based on the Convolutional Neural Network. As input data for the neural network, images obtained by transforming the values of the original electroencephalograms time series based on the Gramian Angular Field matrix were used. The model was trained on data from the Temple University Hospital electroencephalograms Seizure Corpus open data source. The proposed model demonstrated high performance metrics: accuracy - 91%, precision - 92%, recall - 95%, F1-0.93.

Журнал: Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-II-2023)

Номера страниц: 3017

Место издания: Krasnoyarsk

• Egorova Lyudmila (Reshetnev Siberian State University of Science and Technology)
• Rozhnov Ivan (Siberian Federal University)
• Kazakovtsev Lev (Reshetnev Siberian State University of Science and Technology)
• Polyakova Anastasiya (Reshetnev Siberian State University of Science and Technology)

• РИНЦ (eLIBRARY.RU)