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 …
Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A …
S Yasin, A Othmani, I Raza, SA Hussain - Computers in Biology and …, 2023 - Elsevier
Mental disorders are rapidly increasing each year and have become a major challenge
affecting the social and financial well-being of individuals. There is a need for phenotypic …
affecting the social and financial well-being of individuals. There is a need for phenotypic …
DeprNet: A deep convolution neural network framework for detecting depression using EEG
Depression is a common reason for an increase in suicide cases worldwide. Thus, to
mitigate the effects of depression, accurate diagnosis and treatment are needed. An …
mitigate the effects of depression, accurate diagnosis and treatment are needed. An …
AMGCN-L: an adaptive multi-time-window graph convolutional network with long-short-term memory for depression detection
HG Wang, QH Meng, LC Jin… - Journal of Neural …, 2023 - iopscience.iop.org
Objective. Depression is a common chronic mental disorder characterized by high rates of
prevalence, recurrence, suicide, and disability as well as heavy disease burden. An …
prevalence, recurrence, suicide, and disability as well as heavy disease burden. An …
Depression identification using eeg signals via a hybrid of lstm and spiking neural networks
Depression severity can be classified into distinct phases based on the Beck depression
inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of …
inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of …
Machine learning, deep learning and data preprocessing techniques for detection, prediction, and monitoring of stress and stress-related mental disorders: a scoping …
This comprehensive review systematically evaluates Machine Learning (ML) methodologies
employed in the detection, prediction, and analysis of mental stress and its consequent …
employed in the detection, prediction, and analysis of mental stress and its consequent …
Classifying and scoring major depressive disorders by residual neural networks on specific frequencies and brain regions
Major Depressive Disorder (MDD)-can be evaluated by advanced neurocomputing and
traditional machine learning techniques. This study aims to develop an automatic system …
traditional machine learning techniques. This study aims to develop an automatic system …
Sequence modeling of passive sensing data for treatment response prediction in major depressive disorder
Major depressive disorder (MDD) is a prevalent mental health condition and has become a
pressing societal challenge. Early prediction of treatment response may aid in the …
pressing societal challenge. Early prediction of treatment response may aid in the …
Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram
N Lutes, VSS Nadendla, K Krishnamurthy - Scientific Reports, 2024 - nature.com
Spiking neural networks (SNNs) are receiving increased attention because they mimic
synaptic connections in biological systems and produce spike trains, which can be …
synaptic connections in biological systems and produce spike trains, which can be …
Advancements in Affective Disorder Detection: Using Multimodal Physiological Signals and Neuromorphic Computing Based on SNNs
Currently, the integration of artificial intelligence (AI) techniques with multimodal
physiological signals represents a pivotal approach to detect affective disorders (ADs). With …
physiological signals represents a pivotal approach to detect affective disorders (ADs). With …