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 …

[HTML][HTML] A multimodal computer-aided diagnostic system for depression relapse prediction using audiovisual cues: A proof of concept

A Othmani, AO Zeghina - Healthcare Analytics, 2022 - Elsevier
Major depressive disorder (MDD), also known as depression, is a common and serious
mental disorder. It is characterized by a high rate of relapse or recurrence where a person …

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 …

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 …

[HTML][HTML] Machine learning algorithms for depression: diagnosis, insights, and research directions

S Aleem, N Huda, R Amin, S Khalid, SS Alshamrani… - Electronics, 2022 - mdpi.com
Over the years, stress, anxiety, and modern-day fast-paced lifestyles have had immense
psychological effects on people's minds worldwide. The global technological development …

[HTML][HTML] Scoping review on the multimodal classification of depression and experimental study on existing multimodal models

U Arioz, U Smrke, N Plohl, I Mlakar - Diagnostics, 2022 - mdpi.com
Depression is a prevalent comorbidity in patients with severe physical disorders, such as
cancer, stroke, and coronary diseases. Although it can significantly impact the course of the …

[HTML][HTML] Machine learning approaches for diagnosing depression using EEG: A review

Y Liu, C Pu, S Xia, D Deng, X Wang… - Translational Neuroscience, 2022 - degruyter.com
Depression has become one of the most crucial public health issues, threatening the quality
of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of …

Benchmarks for machine learning in depression discrimination using electroencephalography signals

A Seal, R Bajpai, M Karnati, J Agnihotri, A Yazidi… - Applied …, 2023 - Springer
Diagnosis of depression using electroencephalography (EEG) is an emerging field of study.
When mental health facilities are unavailable, the use of EEG as an objective measure for …

[HTML][HTML] Data mining EEG signals in depression for their diagnostic value

M Mohammadi, F Al-Azab, B Raahemi… - BMC medical informatics …, 2015 - Springer
Background Quantitative electroencephalogram (EEG) is one neuroimaging technique that
has been shown to differentiate patients with major depressive disorder (MDD) and non …

Deep learning for depression recognition with audiovisual cues: A review

L He, M Niu, P Tiwari, P Marttinen, R Su, J Jiang… - Information …, 2022 - Elsevier
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 …