EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review
Mental disorders represent critical public health challenges as they are leading contributors
to the global burden of disease and intensely influence social and financial welfare of …
to the global burden of disease and intensely influence social and financial welfare of …
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 …
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 …
EEG-based mild depressive detection using feature selection methods and classifiers
X Li, B Hu, S Sun, H Cai - Computer methods and programs in biomedicine, 2016 - Elsevier
Background and objective Depression has become a major health burden worldwide, and
effectively detection of such disorder is a great challenge which requires latest technological …
effectively detection of such disorder is a great challenge which requires latest technological …
Exploration of EEG-based depression biomarkers identification techniques and their applications: a systematic review
Depression is the most common mental illness, which has become the major cause of fear
and suicidal mortality or tendencies. Currently, about 10% of the world population has been …
and suicidal mortality or tendencies. Currently, about 10% of the world population has been …
Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal
Diagnosing depression in the early curable stages is very important and may even save the
life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating …
life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating …
[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 …
Depression biomarkers using non-invasive EEG: A review
FS de Aguiar Neto, JLG Rosa - Neuroscience & Biobehavioral Reviews, 2019 - Elsevier
Depression is a serious neurological disorder characterized by strong loss of interest,
possibly leading to suicide. According to the World Health Organization, more than 300 …
possibly leading to suicide. According to the World Health Organization, more than 300 …
Major depressive disorder classification based on different convolutional neural network models: Deep learning approach
The human brain is characterized by complex structural, functional connections that
integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation …
integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation …