[HTML][HTML] Depression diagnosis by deep learning using EEG signals: A systematic review
A Safayari, H Bolhasani - Medicine in Novel Technology and Devices, 2021 - Elsevier
Depression is considered by WHO as the main contributor to global disability and it poses
dangerous threats to approximately all aspects of human life, in particular public and private …
dangerous threats to approximately all aspects of human life, in particular public and private …
Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis
A Khosla, P Khandnor, T Chand - Biocybernetics and Biomedical …, 2022 - Elsevier
Depression is one of the significant contributors to the global burden disease, affecting
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …
nearly 264 million people worldwide along with the increasing rate of suicidal deaths …
Automated EEG-based screening of depression using deep convolutional neural network
In recent years, advanced neurocomputing and machine learning techniques have been
used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In …
used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In …
Performance analysis of deep learning CNN in classification of depression EEG signals
P Sandheep, S Vineeth, M Poulose… - TENCON 2019-2019 …, 2019 - ieeexplore.ieee.org
With the advent of greater computing power each year, computer-based disease/condition
diagnosis have been gaining significant importance recently. In this paper, an extensive …
diagnosis have been gaining significant importance recently. In this paper, an extensive …
EEG-based deep learning model for the automatic detection of clinical depression
PP Thoduparambil, A Dominic… - Physical and Engineering …, 2020 - Springer
Clinical depression is a neurological disorder that can be identified by analyzing the
Electroencephalography (EEG) signals. However, the major drawback in using EEG to …
Electroencephalography (EEG) signals. However, the major drawback in using EEG to …
Cognitive depression detection cyber-medical system based on EEG analysis and deep learning approaches
HS Chiang, MY Chen, LS Liao - IEEE Journal of Biomedical …, 2022 - ieeexplore.ieee.org
Long-term depression and negative emotional cycles affect life quality and work productivity.
However, depression is not easy to detect, with current methods mostly relying on scales …
However, depression is not easy to detect, with current methods mostly relying on scales …
Automated depression detection using deep representation and sequence learning with EEG signals
Depression affects large number of people across the world today and it is considered as
the global problem. It is a mood disorder which can be detected using …
the global problem. It is a mood disorder which can be detected using …
Electroencephalogram (EEG) signal analysis for diagnosis of major depressive disorder (MDD): a review
Abstract Depression or Major Depressive Disorder (MDD) is a psychiatric disorder. It is the
major contributor to overall global burden of disease. Any deterioration in brain functioning …
major contributor to overall global burden of disease. Any deterioration in brain functioning …
Machine learning algorithms for depression: diagnosis, insights, and research directions
Over the years, stress, anxiety, and modern-day fast-paced lifestyles have had immense
psychological effects on people's minds worldwide. The global technological development …
psychological effects on people's minds worldwide. The global technological development …
EEG-based depression detection using convolutional neural network with demographic attention mechanism
Electroencephalography (EEG)-based depression detection has become a hot topic in the
development of biomedical engineering. However, the complexity and nonstationarity of …
development of biomedical engineering. However, the complexity and nonstationarity of …