Cargando…
Topological Pattern Recognition of Severe Alzheimer's Disease via Regularized Supervised Learning of EEG Complexity
Alzheimer's disease (AD) is a progressive brain disorder with gradual memory loss that correlates to cognitive deficits in the elderly population. Recent studies have shown the potentials of machine learning algorithms to identify biomarkers and functional brain activity patterns across various...
Autores principales: | Fan, Miaolin, Yang, Albert C., Fuh, Jong-Ling, Chou, Chun-An |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6180281/ https://www.ncbi.nlm.nih.gov/pubmed/30337850 http://dx.doi.org/10.3389/fnins.2018.00685 |
Ejemplares similares
-
Phenotyping Neuropsychiatric Symptoms Profiles of Alzheimer’s Disease Using Cluster Analysis on EEG Power
por: Liu, Friedrich, et al.
Publicado: (2021) -
A self-supervised COVID-19 CT recognition system with multiple regularizations
por: Lu, Han, et al.
Publicado: (2022) -
Combining EEG signal processing with supervised methods for Alzheimer’s patients classification
por: Fiscon, Giulia, et al.
Publicado: (2018) -
Possibilistic Clustering-Promoting Semi-Supervised Learning for EEG-Based Emotion Recognition
por: Dan, Yufang, et al.
Publicado: (2021) -
Nuclear Norm Regularized Deep Neural Network for EEG-Based Emotion Recognition
por: Liang, Shuang, et al.
Publicado: (2022)