Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data

BACKGROUND: Assistive automatic seizure detection can empower human annotators to shorten patient monitoring data review times. We present a proof-of-concept for a seizure detection system that is sensitive, automated, patient-specific, and tunable to maximise sensitivity while minimizing human anno...

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Detalles Bibliográficos
Autores principales: Roy, Subhrajit, Kiral, Isabell, Mirmomeni, Mahtab, Mummert, Todd, Braz, Alan, Tsay, Jason, Tang, Jianbin, Asif, Umar, Schaffter, Thomas, Ahsen, Mehmet Eren, Iwamori, Toshiya, Yanagisawa, Hiroki, Poonawala, Hasan, Madan, Piyush, Qin, Yong, Picone, Joseph, Obeid, Iyad, Marques, Bruno De Assis, Maetschke, Stefan, Khalaf, Rania, Rosen-Zvi, Michal, Stolovitzky, Gustavo, Harrer, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105505/
https://www.ncbi.nlm.nih.gov/pubmed/33745882
http://dx.doi.org/10.1016/j.ebiom.2021.103275

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105505/
https://www.ncbi.nlm.nih.gov/pubmed/33745882
http://dx.doi.org/10.1016/j.ebiom.2021.103275

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