Cargando…
Machine Learning Techniques Reveal Aberrated Multidimensional EEG Characteristics in Patients with Depression
Depression has become one of the most common mental illnesses, causing serious physical and mental harm. However, there remain unclear and uniform physiological indicators to support the diagnosis of clinical depression. This study aimed to use machine learning techniques to investigate the abnormal...
Autores principales: | Li, Gang, Zhong, Hongyang, Wang, Jie, Yang, Yixin, Li, Huayun, Wang, Sujie, Sun, Yu, Qi, Xuchen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046105/ https://www.ncbi.nlm.nih.gov/pubmed/36979194 http://dx.doi.org/10.3390/brainsci13030384 |
Ejemplares similares
-
Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework
por: Shen, Zhongxia, et al.
Publicado: (2022) -
Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder
por: Wang, Jie, et al.
Publicado: (2022) -
Brain network analysis reveals convergent and divergent aberrations between mild stroke patients with cortical and subcortical infarcts during cognitive task performing
por: Xu, Mengru, et al.
Publicado: (2023) -
Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning
por: Xu, Yanting, et al.
Publicado: (2023) -
Multidimensional Feature in Emotion Recognition Based on Multi-Channel EEG Signals
por: Li, Qi, et al.
Publicado: (2022)