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 …
An insight into diagnosis of depression using machine learning techniques: a systematic review
Background In this modern era, depression is one of the most prevalent mental disorders
from which millions of individuals are affected today. The symptoms of depression are …
from which millions of individuals are affected today. The symptoms of depression are …
Resting-state EEG signal for major depressive disorder detection: A systematic validation on a large and diverse dataset
Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes
of disability. Machine learning combined with non-invasive electroencephalography (EEG) …
of disability. Machine learning combined with non-invasive electroencephalography (EEG) …
Benchmarks for machine learning in depression discrimination using electroencephalography signals
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 …
When mental health facilities are unavailable, the use of EEG as an objective measure for …
Discriminative power of EEG-based biomarkers in major depressive disorder: A systematic review
Currently, the diagnosis of major depressive disorder (MDD) and its subtypes is mainly
based on subjective assessments and self-reported measures. However, objective criteria …
based on subjective assessments and self-reported measures. However, objective criteria …
[HTML][HTML] When heart beats differently in depression: review of nonlinear heart rate variability measures
Background: Disturbed heart dynamics in depression seriously increases mortality risk.
Heart rate variability (HRV) is a rich source of information for studying this dynamics. This …
Heart rate variability (HRV) is a rich source of information for studying this dynamics. This …
[HTML][HTML] Using voice biomarkers to classify suicide risk in adult telehealth callers: retrospective observational study
Background: Artificial intelligence has the potential to innovate current practices used to
detect the imminent risk of suicide and to address shortcomings in traditional assessment …
detect the imminent risk of suicide and to address shortcomings in traditional assessment …
Depression recognition using high-order generalized multilayer brain functional network fused with EEG multi-domain information
Abstract Major Depressive Disorder (MDD) is a serious and highly heterogeneous
psychological disorder. According to the network hypothesis, depression originates from …
psychological disorder. According to the network hypothesis, depression originates from …
Personalized characterization of emotional states in patients with bipolar disorder
There is strong clinical evidence from the current literature that certain psychological and
physiological indicators are closely related to mood changes. However, patients with mental …
physiological indicators are closely related to mood changes. However, patients with mental …
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 …
of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of …