[HTML][HTML] Wearable artificial intelligence for anxiety and depression: scoping review
Background Anxiety and depression are the most common mental disorders worldwide.
Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence …
Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence …
Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression
Given the limitations of traditional approaches, wearable artificial intelligence (AI) is one of
the technologies that have been exploited to detect or predict depression. The current …
the technologies that have been exploited to detect or predict depression. The current …
Automated ASD detection using hybrid deep lightweight features extracted from EEG signals
Background Autism spectrum disorder is a common group of conditions affecting about one
in 54 children. Electroencephalogram (EEG) signals from children with autism have a …
in 54 children. Electroencephalogram (EEG) signals from children with autism have a …
Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals
Abstract The number of Major Depressive Disorder (MDD) patients is rising rapidly these
days following the incidence of COVID‐19 pandemic. It is challenging to detect MDD …
days following the incidence of COVID‐19 pandemic. It is challenging to detect MDD …
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 …
[HTML][HTML] A deep learning-based comparative study to track mental depression from EEG data
A Sarkar, A Singh, R Chakraborty - Neuroscience Informatics, 2022 - Elsevier
Background Modern day's society is engaged in commitment-based and time-bound jobs.
This invites tension and mental depression among many people who are not able to cope …
This invites tension and mental depression among many people who are not able to cope …
[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 …
[HTML][HTML] The role of machine learning in diagnosing bipolar disorder: scoping review
Background Bipolar disorder (BD) is the 10th most common cause of frailty in young
individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life …
individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life …
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 …
A model of normality inspired deep learning framework for depression relapse prediction using audiovisual data
A Othmani, AO Zeghina, M Muzammel - Computer Methods and Programs …, 2022 - Elsevier
Abstract Background: Depression (Major Depressive Disorder) is one of the most common
mental illnesses. According to the World Health Organization, more than 300 million people …
mental illnesses. According to the World Health Organization, more than 300 million people …