Engineering nonlinear epileptic biomarkers using deep learning and Benford’s law

In this study, we designed two deep neural networks to encode 16 features for early seizure detection in intracranial EEG and compared them and their frequency responses to 16 widely used engineered metrics to interpret their properties: epileptogenicity index (EI), phase locked high gamma (PLHG), t...

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Detalles Bibliográficos
Autores principales: Caffarini, Joseph, Gjini, Klevest, Sevak, Brinda, Waleffe, Roger, Kalkach-Aparicio, Mariel, Boly, Melanie, Struck, Aaron F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967852/
https://www.ncbi.nlm.nih.gov/pubmed/35354911
http://dx.doi.org/10.1038/s41598-022-09429-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967852/
https://www.ncbi.nlm.nih.gov/pubmed/35354911
http://dx.doi.org/10.1038/s41598-022-09429-w

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