[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 …
Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review
J Chaki, M Woźniak - Biomedical Signal Processing and Control, 2023 - Elsevier
A neurodegenerative disorder, such as Parkinson's, Alzheimer's, epilepsy, stroke, and
others, is a type of disease in which central nervous system cells stop working or die …
others, is a type of disease in which central nervous system cells stop working or die …
Fusion of multivariate EEG signals for schizophrenia detection using CNN and machine learning techniques
Schizophrenia is a severe mental disorder that has adverse effects on the behavior of an
individual such as disorganized speech and delusions. Electroencephalography (EEG) …
individual such as disorganized speech and delusions. Electroencephalography (EEG) …
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 …
Schizophrenia detection technique using multivariate iterative filtering and multichannel EEG signals
K Das, RB Pachori - Biomedical Signal Processing and Control, 2021 - Elsevier
A new approach for extension of univariate iterative filtering (IF) for decomposing a signal
into intrinsic mode functions (IMFs) or oscillatory modes is proposed for multivariate multi …
into intrinsic mode functions (IMFs) or oscillatory modes is proposed for multivariate multi …
A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals
This study presents a method with high accuracy performance that aims to automatically
detect schizophrenia (SZ) from electroencephalography (EEG) records. Unlike related …
detect schizophrenia (SZ) from electroencephalography (EEG) records. Unlike related …
Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features
Schizophrenia is a mental disorder that causes adverse effects on the mental capacity of a
person, emotional inclinations, and quality of personal and social life. The official statistics …
person, emotional inclinations, and quality of personal and social life. The official statistics …
SchizoNET: a robust and accurate Margenau–Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals
Objective. Schizophrenia (SZ) is a severe chronic illness characterized by delusions,
cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking …
cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking …
A deep learning based model using RNN-LSTM for the Detection of Schizophrenia from EEG data
R Supakar, P Satvaya, P Chakrabarti - Computers in Biology and Medicine, 2022 - Elsevier
Normal life can be ensured for schizophrenic patients if diagnosed early.
Electroencephalogram (EEG) carries information about the brain network connectivity which …
Electroencephalogram (EEG) carries information about the brain network connectivity which …
Deep-learning detection of mild cognitive impairment from sleep electroencephalography for patients with Parkinson's disease
Parkinson's disease which is the second most prevalent neurodegenerative disorder in the
United States is a serious and complex disease that may progress to mild cognitive …
United States is a serious and complex disease that may progress to mild cognitive …