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 …
Transfer learning for non-image data in clinical research: a scoping review
A Ebbehoj, MØ Thunbo, OE Andersen… - PLOS Digital …, 2022 - journals.plos.org
Background Transfer learning is a form of machine learning where a pre-trained model
trained on a specific task is reused as a starting point and tailored to another task in a …
trained on a specific task is reused as a starting point and tailored to another task in a …
Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models
Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals
in the brain, the function of some brain regions is out of balance, leading to the lack of …
in the brain, the function of some brain regions is out of balance, leading to the lack of …
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) …
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 …
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 …
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 …
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 …
Single-source to single-target cross-subject motor imagery classification based on multisubdomain adaptation network
In the electroencephalography (EEG) based cross-subject motor imagery (MI) classification
task, the device and subject problems can cause the time-related data distribution shift …
task, the device and subject problems can cause the time-related data distribution shift …
[HTML][HTML] Deep learning applied to electroencephalogram data in mental disorders: A systematic review
In recent medical research, tremendous progress has been made in the application of deep
learning (DL) techniques. This article systematically reviews how DL techniques have been …
learning (DL) techniques. This article systematically reviews how DL techniques have been …