Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models

The development of seizure prediction models is often based on long-term scalp electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive, and come at a relatively low-cost. However, they suffer from major shortcomings. First, long-term EEG is usually highly contamin...

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Detalles Bibliográficos
Autores principales: Lopes, Fábio, Leal, Adriana, Pinto, Mauro F., Dourado, António, Schulze-Bonhage, Andreas, Dümpelmann, Matthias, Teixeira, César
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10090199/
https://www.ncbi.nlm.nih.gov/pubmed/37041158
http://dx.doi.org/10.1038/s41598-023-30864-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10090199/
https://www.ncbi.nlm.nih.gov/pubmed/37041158
http://dx.doi.org/10.1038/s41598-023-30864-w

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