Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach
Deep learning techniques have recently made considerable advances in the field of artificial
intelligence. These methodologies can assist psychologists in early diagnosis of mental …
intelligence. These methodologies can assist psychologists in early diagnosis of mental …
Automated diagnosis of major depressive disorder using brain effective connectivity and 3D convolutional neural network
Major depressive disorder (MDD), which is also known as unipolar depression, is one of the
leading sources of functional frailty. MDD is mostly a chronic disorder that requires a long …
leading sources of functional frailty. MDD is mostly a chronic disorder that requires a long …
A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)
Major depressive disorder (MDD), a debilitating mental illness, could cause functional
disabilities and could become a social problem. An accurate and early diagnosis for …
disabilities and could become a social problem. An accurate and early diagnosis for …
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 …
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 …
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 …
A deep learning approach for mild depression recognition based on functional connectivity using electroencephalography
X Li, R La, Y Wang, B Hu, X Zhang - Frontiers in neuroscience, 2020 - frontiersin.org
Early detection remains a significant challenge for the treatment of depression. In our work,
we proposed a novel approach to mild depression recognition using …
we proposed a novel approach to mild depression recognition using …
Development of wavelet coherence EEG as a biomarker for diagnosis of major depressive disorder
DM Khan, K Masroor, MFM Jailani… - IEEE Sensors …, 2022 - ieeexplore.ieee.org
Major depressive disorder (MDD) contributes the most to human's functional frailty
worldwide. Therefore, its timely diagnosis and treatment is of utmost importance …
worldwide. Therefore, its timely diagnosis and treatment is of utmost importance …
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 …
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 …