Journal Article10.1016/j.compbiomed.2022.105791
Investigating the discrimination of linear and nonlinear effective connectivity patterns of EEG signals in children with Attention-Deficit/Hyperactivity Disorder and Typically Developing children
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TL;DR: In this article , a nonlinear Causal Relationship Estimation by Artificial Neural Network (nCREANN) method was proposed for estimating linear and nonlinear components of effective connectivity, and provided novel findings about effective connectivity of EEG signals of children with autism.
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About: This article is published in Computers in Biology and Medicine. The article was published on 01 Jul 2022. The article focuses on the topics: Medicine & Computer science.
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Citations
Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade
TL;DR: In this article , a systematic review of nine developmental and mental disorders (Autism spectrum disorder, Attention deficit hyperactivity disorder, Schizophrenia, Anxiety, Depression, Dyslexia, Post-traumatic stress disorder, Tourette syndrome, and Obsessive-compulsive disorder) prominent in children and adolescents is presented.
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An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals
Smith K. Khare,U. Rajendra Acharya +1 more
- 01 Feb 2023
TL;DR: The model show that the interpretability and explainability of frontal region is highest compared to pre-frontal, central, parietal, occipital, and temporal regions and is highly reliable, robust, interpretable, and explainable for the clinicians to detect ADHD in children.
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Enhanced electroencephalography effective connectivity in frontal low‐gamma band correlates of emotional regulation after mindfulness training
Hei-Yin Ng,Changwei W. Wu,Feng-Ying Huang,Chih-Mao Huang,Chia-Fen Hsu,Yi-Ping Chao,Tzyy-Ping Jung,C-H Chuang +7 more
TL;DR: In this paper , the authors analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training-related effective connectivity changes in the frontal area.
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A Novel Approach to Identify the Brain Regions that Best Classify ADHD by means of EEG and Deep Learning
Javier Sanchis,Sandra García-Ponsoda,Miguel A. Teruel,J.C. Trujillo,Il-Yeol Song +4 more
TL;DR: A novel approach to identify the brain regions that best classify ADHD using EEG and Deep Learning and the results hold significant value for physicians in the quest to better understand the underlying causes of ADHD.
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Efficient Deep Learning Approach for Diagnosis of Attention-Deficit/Hyperactivity Disorder in Children Based on EEG Signals
Hamid R. Jahani,Ali Asghar Safaei +1 more
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