[HTML][HTML] Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade
Mental health is a basic need for a sustainable and developing society. The prevalence and
financial burden of mental illness have increased globally, and especially in response to …
financial burden of mental illness have increased globally, and especially in response to …
Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023
Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional,
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …
Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal
Detection of mental disorders such as schizophrenia (SZ) through investigating brain
activities recorded via Electroencephalogram (EEG) signals is a promising field in …
activities recorded via Electroencephalogram (EEG) signals is a promising field in …
Automated steel surface defect detection and classification using a new deep learning-based approach
In this study, a new deep learning-based approach has been developed that detects and
classifies surface defects that occur in the steel production process. The proposed …
classifies surface defects that occur in the steel production process. The proposed …
An effective and robust approach based on r-cnn+ lstm model and ncar feature selection for ophthalmological disease detection from fundus images
Changes in and around anatomical structures such as blood vessels, optic disc, fovea, and
macula can lead to ophthalmological diseases such as diabetic retinopathy, glaucoma, age …
macula can lead to ophthalmological diseases such as diabetic retinopathy, glaucoma, age …
Automated EEG sentence classification using novel dynamic-sized binary pattern and multilevel discrete wavelet transform techniques with TSEEG database
Electroencephalography (EEG) signal is an important physiological signal commonly used
in machine learning to decode brain activities, including imagined words and sentences. We …
in machine learning to decode brain activities, including imagined words and sentences. We …
Depression signal correlation identification from different EEG channels based on CNN feature extraction
B Wang, Y Kang, D Huo, D Chen, W Song… - Psychiatry Research …, 2023 - Elsevier
Depression is a mental illness and can even lead to suicide if not be diagnosed and treated.
Electroencephalograph (EEG) is used to diagnose depression and it is more complexity to …
Electroencephalograph (EEG) is used to diagnose depression and it is more complexity to …
CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals
Objective. Schizophrenia (SZ) is a severe, chronic psychiatric-cognitive disorder. The
primary objective of this work is to present a handcrafted model using state-of-the-art …
primary objective of this work is to present a handcrafted model using state-of-the-art …
Improved sparse representation based robust hybrid feature extraction models with transfer and deep learning for EEG classification
SK Prabhakar, SW Lee - Expert Systems with Applications, 2022 - Elsevier
Numerous studies in the field of cognitive research is dependent on
Electroencephalography (EEG) as it apprehends the neural correspondences of various …
Electroencephalography (EEG) as it apprehends the neural correspondences of various …
A new one-dimensional testosterone pattern-based EEG sentence classification method
Electroencephalography (EEG) signals are crucial data to understand brain activities. Thus,
many papers have been proposed about EEG signals. In particular, machine learning …
many papers have been proposed about EEG signals. In particular, machine learning …