Cargando…
Expert and deep learning model identification of iEEG seizures and seizure onset times
Hundreds of 90-s iEEG records are typically captured from each NeuroPace RNS System patient between clinic visits. While these records provide invaluable information about the patient’s electrographic seizure and interictal activity patterns, manually classifying them into electrographic seizure/non...
Autores principales: | Arcot Desai, Sharanya, Afzal, Muhammad Furqan, Barry, Wade, Kuo, Jonathan, Benard, Shawna, Traner, Christopher, Tcheng, Thomas, Seale, Cairn, Morrell, Martha |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10354337/ https://www.ncbi.nlm.nih.gov/pubmed/37476840 http://dx.doi.org/10.3389/fnins.2023.1156838 |
Ejemplares similares
-
Non-linear Embedding Methods for Identifying Similar Brain Activity in 1 Million iEEG Records Captured From 256 RNS System Patients
por: Arcot Desai, Sharanya, et al.
Publicado: (2022) -
A High Accuracy Electrographic Seizure Classifier Trained Using Semi-Supervised Labeling Applied to a Large Spectrogram Dataset
por: Barry, Wade, et al.
Publicado: (2021) -
A Primer on Hyperdimensional Computing for iEEG Seizure Detection
por: Schindler, Kaspar A., et al.
Publicado: (2021) -
Online Prediction of Lead Seizures from iEEG Data
por: Chen, Hsiang-Han, et al.
Publicado: (2021) -
Network analysis of preictal iEEG reveals changes in network structure preceding seizure onset
por: Sumsky, Stefan, et al.
Publicado: (2022)