Cargando…
Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures
INTRODUCTION: The early diagnosis and classification of social anxiety disorder (SAD) are crucial clinical support tasks for medical practitioners in designing patient treatment programs to better supervise the progression and development of SAD. This paper proposes an effective method to classify t...
Autores principales: | Al-Ezzi, Abdulhakim, Kamel, Nidal, Al-Shargabi, Amal A., Al-Shargie, Fares, Al-Shargabi, Alaa, Yahya, Norashikin, Al-Hiyali, Mohammed Isam |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10226190/ https://www.ncbi.nlm.nih.gov/pubmed/37255678 http://dx.doi.org/10.3389/fpsyt.2023.1155812 |
Ejemplares similares
-
Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures
por: Al-Ezzi, Abdulhakim, et al.
Publicado: (2023) -
Review of EEG, ERP, and Brain Connectivity Estimators as Predictive Biomarkers of Social Anxiety Disorder
por: Al-Ezzi, Abdulhakim, et al.
Publicado: (2020) -
Analysis of Default Mode Network in Social Anxiety Disorder: EEG Resting-State Effective Connectivity Study
por: Al-Ezzi, Abdulhakim, et al.
Publicado: (2021) -
Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network
por: Al-Hiyali, Mohammed Isam, et al.
Publicado: (2021) -
The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings
por: Ibrahim, Dina M., et al.
Publicado: (2022)