| Issue |
ITM Web Conf.
Volume 59, 2024
II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
|
|
|---|---|---|
| Article Number | 03017 | |
| Number of page(s) | 6 | |
| Section | Data Mining, Machine Learning and Patern Recognition | |
| DOI | https://doi.org/10.1051/itmconf/20245903017 | |
| Published online | 25 January 2024 | |
Electroencephalogram data analysis using Convolutional Neural Networks and Gramian Angular Field
1
Siberian Federal University,
79 Svobodny Prospekt,
660041
Krasnoyarsk,
Russia
2
Reshetnev Siberian State University of Science and Technology,
31 Krasnoyarskiy Rabochiy Prospekt,
660037
Krasnoyarsk,
Russia
* Corresponding author: egorova_ld@rambler.ru
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.
© The Authors, published by EDP Sciences, 2024
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