Cargando…
Estimating Patient-Level Uncertainty in Seizure Detection Using Group-Specific Out-of-Distribution Detection Technique
Epilepsy is a chronic neurological disorder affecting around 1% of the global population, characterized by recurrent epileptic seizures. Accurate diagnosis and treatment are crucial for reducing mortality rates. Recent advancements in machine learning (ML) algorithms have shown potential in aiding c...
Autores principales: | Wong, Sheng, Simmons, Anj, Villicana, Jessica Rivera, Barnett, Scott |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611125/ https://www.ncbi.nlm.nih.gov/pubmed/37896469 http://dx.doi.org/10.3390/s23208375 |
Ejemplares similares
-
EEG datasets for seizure detection and prediction— A review
por: Wong, Sheng, et al.
Publicado: (2023) -
Out-of-hospital multimodal seizure detection: a pilot study
por: Nielsen, Jonas Munch, et al.
Publicado: (2023) -
Semantic enhanced for out-of-distribution detection
por: Jiang, Weijie, et al.
Publicado: (2022) -
Modified-Distribution Entropy as the Features for the Detection of Epileptic Seizures
por: Aung, Si Thu, et al.
Publicado: (2020) -
EEG seizure detection: concepts, techniques, challenges, and future trends
por: Ein Shoka, Athar A., et al.
Publicado: (2023)