Cargando…
EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine
Epilepsy is one of the most serious nervous system diseases; it can be diagnosed accurately by video electroencephalogram. In this study, we analyzed microstate epileptic electroencephalogram (EEG) to aid in the diagnosis and identification of epilepsy. We recruited patients with focal epilepsy and...
Autores principales: | Yang, Li, He, Jiaxiu, Liu, Ding, Zheng, Wen, Song, Zhi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775561/ https://www.ncbi.nlm.nih.gov/pubmed/36552190 http://dx.doi.org/10.3390/brainsci12121731 |
Ejemplares similares
-
Automatic Recognition of High-Density Epileptic EEG Using Support Vector Machine and Gradient-Boosting Decision Tree
por: He, Jiaxiu, et al.
Publicado: (2022) -
EEG microstate features for schizophrenia classification
por: Kim, Kyungwon, et al.
Publicado: (2021) -
Feature extraction based on microstate sequences for EEG–based emotion recognition
por: Chen, Jing, et al.
Publicado: (2022) -
EEG microstates of dreams
por: Bréchet, Lucie, et al.
Publicado: (2020) -
EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features
por: Ahmadi, Negar, et al.
Publicado: (2020)