Deep learning for depression recognition with audiovisual cues: A review
With the acceleration of the pace of work and life, people are facing more and more
pressure, which increases the probability of suffering from depression. However, many …
pressure, which increases the probability of suffering from depression. However, many …
Automatic depression recognition by intelligent speech signal processing: A systematic survey
Depression has become one of the most common mental illnesses in the world. For better
prediction and diagnosis, methods of automatic depression recognition based on speech …
prediction and diagnosis, methods of automatic depression recognition based on speech …
DepMSTAT: Multimodal spatio-temporal attentional transformer for depression detection
Y Tao, M Yang, H Li, Y Wu, B Hu - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Depression is one of the most common mental illnesses, but few of the currently proposed in-
depth models based on social media data take into account both temporal and spatial …
depth models based on social media data take into account both temporal and spatial …
Depression level prediction using deep spatiotemporal features and multilayer bi-ltsm
Depression is a serious psychiatric disorder that restricts an individuals ability to work
properly in both their daily and professional lives. Usually, the diagnosis of depression often …
properly in both their daily and professional lives. Usually, the diagnosis of depression often …
Multimodal temporal machine learning for Bipolar Disorder and Depression Recognition
F Ceccarelli, M Mahmoud - Pattern Analysis and Applications, 2022 - Springer
Mental disorder is a serious public health concern that affects the life of millions of people
throughout the world. Early diagnosis is essential to ensure timely treatment and to improve …
throughout the world. Early diagnosis is essential to ensure timely treatment and to improve …
A review of detection techniques for depression and bipolar disorder
D Highland, G Zhou - Smart Health, 2022 - Elsevier
Depression and bipolar disorder are mood disorders affecting millions of people worldwide
that can have severe impacts on one's quality of life. Our ability to detect these illnesses is …
that can have severe impacts on one's quality of life. Our ability to detect these illnesses is …
Multimodal deep learning framework for mental disorder recognition
Current methods for mental disorder recognition mostly depend on clinical interviews and
self-reported scores that can be highly subjective. Building an automatic recognition system …
self-reported scores that can be highly subjective. Building an automatic recognition system …
Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling
AZ Antosik-Wójcińska, M Dominiak… - International journal of …, 2020 - Elsevier
Background Bipolar disorder (BD) is a chronic illness with a high recurrence rate.
Smartphones can be a useful tool for detecting prodromal symptoms of episode recurrence …
Smartphones can be a useful tool for detecting prodromal symptoms of episode recurrence …
A multimodal approach for mania level prediction in bipolar disorder
Bipolar disorder is a mental health disorder that causes mood swings that range from
depression to mania. Clinical diagnosis of bipolar disorder is based on patient interviews …
depression to mania. Clinical diagnosis of bipolar disorder is based on patient interviews …
Portable technologies for digital phenotyping of bipolar disorder: A systematic review
LF Saccaro, G Amatori, A Cappelli, R Mazziotti… - Journal of affective …, 2021 - Elsevier
Background Bias-prone psychiatric interviews remain the mainstay of bipolar disorder (BD)
assessment. The development of digital phenotyping promises to improve BD management …
assessment. The development of digital phenotyping promises to improve BD management …