Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review
Abstract Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective
rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) …
rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) …
Evaluation of risk of bias in neuroimaging-based artificial intelligence models for psychiatric diagnosis: a systematic review
Z Chen, X Liu, Q Yang, YJ Wang, K Miao… - JAMA network …, 2023 - jamanetwork.com
Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
Automated ASD detection using hybrid deep lightweight features extracted from EEG signals
Background Autism spectrum disorder is a common group of conditions affecting about one
in 54 children. Electroencephalogram (EEG) signals from children with autism have a …
in 54 children. Electroencephalogram (EEG) signals from children with autism have a …
Early detection of Alzheimer's disease from EEG signals using Hjorth parameters
Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the
brain that ultimately results in the death of neurons and dementia. The prevalence of the …
brain that ultimately results in the death of neurons and dementia. The prevalence of the …
A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG
Autism spectrum disorder (ASD) is a developmental disability characterized by persistent
impairments in social interaction, speech and nonverbal communication, and restricted or …
impairments in social interaction, speech and nonverbal communication, and restricted or …
EEG signal processing for Alzheimer's disorders using discrete wavelet transform and machine learning approaches
The most common neurological brain issue is Alzheimer's disease, which can be diagnosed
using a variety of clinical methods. However, the electroencephalogram (EEG) is shown to …
using a variety of clinical methods. However, the electroencephalogram (EEG) is shown to …
Accurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature map** and convolutional neural network techniques with EEG signals
Abstract Autism Spectrum Disorders (ASD) is a collection of complicated neurological
disorders that first show in early childhood. Electroencephalogram (EEG) signals are widely …
disorders that first show in early childhood. Electroencephalogram (EEG) signals are widely …
Multichannel deep attention neural networks for the classification of autism spectrum disorder using neuroimaging and personal characteristic data
Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of
children aged 8 across the United States. It is characterized by impairments in social …
children aged 8 across the United States. It is characterized by impairments in social …
Autism spectrum disorder diagnostic system using HOS bispectrum with EEG signals
Autistic individuals often have difficulties expressing or controlling emotions and have poor
eye contact, among other symptoms. The prevalence of autism is increasing globally, posing …
eye contact, among other symptoms. The prevalence of autism is increasing globally, posing …
A robust method for early diagnosis of autism spectrum disorder from EEG signals based on feature selection and DBSCAN method
Electroencephalogram (EEG) is one of the most important signals for diagnosis of Autism
Spectrum Disorder (ASD). There are different challenges such as feature selection and the …
Spectrum Disorder (ASD). There are different challenges such as feature selection and the …