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Eeg signal processing for medical diagnosis, healthcare, and monitoring: A comprehensive review
EEG is a common and safe test that uses small electrodes to record electrical signals from
the brain. It has a broad range of applications in medical diagnosis, including diagnosis of …
the brain. It has a broad range of applications in medical diagnosis, including diagnosis of …
Exploring new horizons in neuroscience disease detection through innovative visual signal analysis
Brain disorders pose a substantial global health challenge, persisting as a leading cause of
mortality worldwide. Electroencephalogram (EEG) analysis is crucial for diagnosing brain …
mortality worldwide. Electroencephalogram (EEG) analysis is crucial for diagnosing brain …
The emergence of artificial intelligence in autism spectrum disorder research: A review of neuro imaging and behavioral applications
ID KB, DRV PM - Computer Science Review, 2025 - Elsevier
The quest to find reliable biomarkers in autism spectrum disorders (ASD) is an ongoing
endeavour to identify both underlying causes and measurable indicators of this …
endeavour to identify both underlying causes and measurable indicators of this …
Autism spectrum disorder diagnosis using fractal and non-fractal-based functional connectivity analysis and machine learning methods
Autism spectrum disorder (ASD) is a neurological condition characterized by impaired
functional connectivity (FC) networks in the brain. There are several brain networks …
functional connectivity (FC) networks in the brain. There are several brain networks …
Elevating recommender systems: Cutting-edge transfer learning and embedding solutions
In today's information age and connected economy, Recommender Systems (RS) plays a
vital role in managing information overload and delivering personalized suggestions to …
vital role in managing information overload and delivering personalized suggestions to …
DML‐GNN: ASD Diagnosis Based on Dual‐Atlas Multi‐Feature Learning Graph Neural Network
S Liu, C Sun, J Li, S Wang… - International Journal of …, 2025 - Wiley Online Library
To better automate the diagnosis of autism spectrum disorder (ASD) and improve diagnostic
accuracy, a graph neural network via dual‐atlas multi‐feature learning (DML‐GNN) model …
accuracy, a graph neural network via dual‐atlas multi‐feature learning (DML‐GNN) model …
Advancing ASD diagnostic classification with features of continuous wavelet transform of fMRIand machine learning algorithms
MB Ingle, CT Rakshe… - 2023 IEEE 5th …, 2023 - ieeexplore.ieee.org
In this study, we aimed to develop a diagnostic classification model for autism spectrum
disorder (ASD) using scalograms of functional magnetic resonance imaging (fMRI) data …
disorder (ASD) using scalograms of functional magnetic resonance imaging (fMRI) data …
Transcranial Acoustoelectric of Functional Brain Activity Extraction based on Interet of Things
Understanding the mind in its normal state is gaining more and more attention. The
widespread adoption of practical brain screens, such as functional cerebral oxygenation, or …
widespread adoption of practical brain screens, such as functional cerebral oxygenation, or …
[PDF][PDF] EEG Signal Processing for Medical Diagnosis, Healthcare, and Monitoring: A Comprehensive
NS AMER, SB BELHAOUARI - 2023 - manara.qnl.qa
ABSTRACT EEG is a common and safe test that uses small electrodes to record electrical
signals from the brain. It has a broad range of applications in medical diagnosis, including …
signals from the brain. It has a broad range of applications in medical diagnosis, including …
Advancing ASD diagnostic classification using time-frequency spectrograms of fMRI BOLD signals and machine learning
T Tikaram, U Raj, R Ratnaik, JFA Ronickom - 2024 - researchsquare.com
In this study, our goal was to develop a diagnostic framework for autism spectrum disorder
(ASD) by analyzing time-frequency spectrograms generated from BOLD signals in functional …
(ASD) by analyzing time-frequency spectrograms generated from BOLD signals in functional …