[HTML][HTML] Methodological and quality flaws in the use of artificial intelligence in mental health research: systematic review

R Tornero-Costa, A Martinez-Millana… - JMIR Mental …, 2023 - mental.jmir.org
Background: Artificial intelligence (AI) is giving rise to a revolution in medicine and health
care. Mental health conditions are highly prevalent in many countries, and the COVID-19 …

Heart rate variability for medical decision support systems: A review

O Faust, W Hong, HW Loh, S Xu, RS Tan… - Computers in biology …, 2022 - Elsevier
Abstract Heart Rate Variability (HRV) is a good predictor of human health because the heart
rhythm is modulated by a wide range of physiological processes. This statement embodies …

Depression recognition using remote photoplethysmography from facial videos

CÁ Casado, ML Cañellas… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Depression is a mental illness that may be harmful to an individual's health. The detection of
mental health disorders in the early stages and a precise diagnosis are critical to avoid …

An insight into diagnosis of depression using machine learning techniques: a systematic review

S Bhadra, CJ Kumar - Current medical research and opinion, 2022 - Taylor & Francis
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 …

How emotions impact sleep: A quantitative review of experiments

Z Krizan, NA Boehm, CB Strauel - Sleep Medicine Reviews, 2024 - Elsevier
Although sleep and emotional processes are recognized as mutually dependent, the causal
impact of emotions on sleep has been comparatively neglected. To appraise evidence for …

Enhanced depression detection from speech using quantum whale optimization algorithm for feature selection

B Kaur, S Rathi, RK Agrawal - Computers in Biology and Medicine, 2022 - Elsevier
There is an urgent need to detect depression using a non-intrusive approach that is reliable
and accurate. In this paper, a simple and efficient unimodal depression detection approach …

Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression

S Soni, A Seal, A Yazidi, O Krejcar - Computers in Biology and Medicine, 2022 - Elsevier
Depression is a major depressive disorder characterized by persistent sadness and a sense
of worthlessness, as well as a loss of interest in pleasurable activities, which leads to a …

[Retracted] Predicting the Risk of Depression Based on ECG Using RNN

ST Noor, ST Asad, MM Khan, GS Gaba… - Computational …, 2021 - Wiley Online Library
This paper presents a model to predict the risk of depression based on electrocardiogram
(ECG). This proposed model uses a Recurrent Neural Network (RNN) and Long Short‐Term …

End-to-end depression recognition based on a one-dimensional convolution neural network model using two-lead ECG signal

X Zang, B Li, L Zhao, D Yan, L Yang - Journal of Medical and Biological …, 2022 - Springer
Purpose Depression is a common mental illness worldwide and has become an important
public health problem. The current clinical diagnosis of depression mainly relies on the …

Physiological monitoring of stress and major depression: A review of the current monitoring techniques and considerations for the future

T Ahmed, M Qassem, PA Kyriacou - Biomedical Signal Processing and …, 2022 - Elsevier
Mental illnesses such as clinical and major depression have taken its toll on the global
burden of disease, leading to unprecedented cases worldwide. Commonly, the diagnostic …