Szczegóły publikacji
Opis bibliograficzny
Impact of the pre-processing and balancing of EEG data on the performance of graph neural network for epileptic seizure classification / Szymon Mazurek, Rosmary Blanco, Joan Falcó-Roget, Jan K. Argasiński, Alessandro Crimi // W: Artificial Intelligence and Soft Computing : 22nd International Conference, ICAISC 2023 : Zakopane, Poland, June 18–22, 2023 : proceedings , Pt. 2 / eds. Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek M. Zurada. — Cham : Springer Nature Switzerland, cop. 2023. — ( Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 14126. Lecture Notes in Artificial Intelligence ). — ISBN: 978-3-031-42507-3; e-ISBN: 978-3-031-42508-0. — S. 258–268. — Bibliogr., Abstr. — Publikacja dostępna online od: 2023-09-14. — Sz. Mazurek, A. Crimi - afiliacja: Sano Centre for Computational Medicine, Computer Vision Group, Kraków, Poland
Autorzy (5)
- Mazurek Szymon
- Blanco Rosmary
- Falcó-Roget Joan
- Argasiński Jan
- Crimi Alessandro
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 155416 |
|---|---|
| Data dodania do BaDAP | 2024-10-21 |
| DOI | 10.1007/978-3-031-42508-0_24 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Artificial Intelligence and Soft Computing 2023 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Automated seizure detection in electroencephalography (EEG) recordings is a time consuming task, dependent on the expert performing the review. The rapid development in the field of deep learning shows promise in the creation of automated models performing EEG signal classification. However, EEG data requires careful artifact removal, as well as strategies for dealing with inherent imbalance present within samples extracted from epileptic patients. In this work, a simple graph neural network (GNN) using attention to perform classification of EEG segments is proposed. We also elaborate on the effectiveness of signal pre-processing and imbalance handling methods, showing their impact on the model’s performance. The results demonstrate that the classificator’s performance can be enhanced by choosing proper pre-processing and signal balancing methods. We anticipate that these approaches can be adopted by the researchers working on EEG classification with deep learning models, helping to improve the the robustness of constructed models.
