[HTML][HTML] Machine learning and graph signal processing applied to healthcare: A review
Signal processing is a very useful field of study in the interpretation of signals in many
everyday applications. In the case of applications with time-varying signals, one possibility is …
everyday applications. In the case of applications with time-varying signals, one possibility is …
[PDF][PDF] Machine learning based framework for classification of children with ADHD and healthy controls
Electrophysiological (EEG) signals provide good temporal resolution and can be effectively
used to assess and diagnose children with Attention Deficit Hyperactivity Disorder (ADHD) …
used to assess and diagnose children with Attention Deficit Hyperactivity Disorder (ADHD) …
[HTML][HTML] Neural decoding of imagined speech from EEG signals using the fusion of graph signal processing and graph learning techniques
Imagined Speech (IS) is the imagination of speech without using the tongue or muscles. In
recent studies, IS tasks are increasingly investigated for the Brain-Computer Interface (BCI) …
recent studies, IS tasks are increasingly investigated for the Brain-Computer Interface (BCI) …
Detection of attention deficit hyperactivity disorder based on EEG signals using Least Square Support Vector Machine (LS-SVM)
A Tayeh Swadi, F Sabar Miften - Computer Methods in …, 2023 - Taylor & Francis
ABSTRACT Attention Deficit Hyperactivity Disorder (ADHD) is a mental disorder affecting
children in their early stages. Detection of ADHD is considered a challenging task for experts …
children in their early stages. Detection of ADHD is considered a challenging task for experts …
Computer-aided diagnosis framework for ADHD detection using quantitative EEG
R Holker, S Susan - International Conference on Brain Informatics, 2022 - Springer
Abstract Attention Deficit Hyperactivity Disorder (ADHD) is a mental disorder that is marked
by abnormally high levels of impulsivity, hyperactivity and inattention. One of the methods to …
by abnormally high levels of impulsivity, hyperactivity and inattention. One of the methods to …
Anatomy of various biomarkers for diagnosis of socio-behavioral disorders
M Mengi, D Malhotra - Recent Innovations in Computing: Proceedings of …, 2022 - Springer
Socio-behavioral disorders, a subcategory of neurodevelopmental disorders typically
accompanied by behavioral impairments, are the most common disability, specifically found …
accompanied by behavioral impairments, are the most common disability, specifically found …
Understanding Concepts in Graph Signal Processing for Neurophysiological Signal Analysis
S Goerttler, M Wu, F He - Machine Learning Applications in Medicine and …, 2024 - Springer
Multivariate signals measured simultaneously over time by sensor networks are becoming
increasingly common. The emerging field of graph signal processing (GSP) promises to …
increasingly common. The emerging field of graph signal processing (GSP) promises to …
Prediction of Neurodevelopmental Disorder by Combining Data-Driven ROI Extraction and Frequency Specific Brain Functional Connectivity Approach
S Bandyopadhyay, M Sarma… - 2024 15th International …, 2024 - ieeexplore.ieee.org
Neurodevelopmental Disorders (NDDs) such as Attention-Deficit/Hyperactivity Disorder
(ADHD) and Autism spectrum disorder (ASD) have a profound impact on children and …
(ADHD) and Autism spectrum disorder (ASD) have a profound impact on children and …
[PDF][PDF] Inter-class Correlation-based EEG Channel Selection for ADHD Classification.
V Joshi, N Nanavati - IAENG International Journal of Computer Science, 2024 - iaeng.org
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition that
affects millions of children. In this manuscript, we propose methods to classify two groups of …
affects millions of children. In this manuscript, we propose methods to classify two groups of …
Detection of attention deficit hyperactivity disorder using electroencephalogram signals: a review
JN Kiro, BB Sinha, M Kumari… - Artificial Intelligence: A …, 2024 - iopscience.iop.org
Chapter 4 discusses how EEG signals can be used for the detection of attention deficit
hyperactivity disorder (ADHD) using machine learning and/or deep learning techniques, as …
hyperactivity disorder (ADHD) using machine learning and/or deep learning techniques, as …