Depression Diagnosis by Deep Learning Using EEG Signals: A Systematic Review

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 health. This mental disorder is usually characterized by considerable changes in feelings, routines, or thoughts. With respect to the fact that early diagnosis of this illness would be of critical importance ineffective treatment, some development has occurred in the purpose of depression detection. EEG signals reflect the working status of the human brain by which are considered the most proper tools for a depression diagnosis. Deep learning algorithms have the capacity of pattern discovery and extracting features from the raw data which is fed into them. Owing to this significant characteristic of deep learning, recently, these methods have intensely utilized in the diverse field of researches, specifically medicine and healthcare. Thereby, in this article, we aimed to review all papers concentrated on using deep learning to detect or predict depressive subjects with the help of EEG signals as input data. Regarding the adopted search method, we finally evaluated 22 articles between 2016 and 2021. This article which is organized according to the systematic literature review (SLR) method, provides complete summaries of all exploited studies and compares the noticeable aspects of them. Moreover, some statistical analysis performs to gain a depth perception of the general ideas of the latest researches in this area. A pattern of a five-step procedure was also established by which almost all reviewed articles fulfilled the goal of depression detection. Finally, open issues and challenges in this way of depression diagnosis or prediction and suggested works as the future directions discussed.

Regional Differences in Various Risk Factors for Postpartum Depression: Applying Mixed Models to the PRAMS Dataset

Purpose: The purpose of this study was to assess the association between various risk factors with postpartum depression severity using a large dataset that included variables such as previous mental health status, social factors, societal factors, health care access, and other state-wide or region-specific variables.Methods: We obtained the most recently available (2016–2017) dataset from the Pregnancy Risk Assessment Monitoring System (PRAMS), which is a dataset compiled by the Centers for Disease Control (CDC) that collects state-specific, population-based data on maternal attitudes and experiences before, during, and shortly after pregnancy from over 73,000 women in 39 states. We utilized a hierarchical linear model to analyze the data across various levels, with a symptom severity scale (0–8) as the dependent variable.Results: Of the 21 variables included in the final model, nine variables were statistically significant predictors of symptom severity. Statistically significant predictors of increased postpartum depression symptom severity included previous depression diagnosis and depression symptoms during pregnancy, baby not residing with mother, unintentional pregnancy, women with less than a high school degree and more than a college degree, Women Infants Children (WIC) enrollment, and married women. In contrast to these other factors, attendance at a postpartum follow up appointment was associated with significantly increased symptom severity. Age revealed an inverted curve in predicting postpartum symptom severity.Conclusions: There was no significant difference in symptom severity scores across the 39 participating states. Most notably, postpartum depression symptom severity was associated with previous depression diagnosis and previous symptom severity, but our results also reveal novel social and education factors that contribute to the support and well-being of the mother and child.

DNA methylation of the KLK8 gene in depression symptomatology

Background Depression is a common, complex, and debilitating mental disorder estimated to be under-diagnosed and insufficiently treated in society. Liability to depression is influenced by both genetic and environmental risk factors, which are both capable of impacting DNA methylation (DNAm). Accordingly, numerous studies have researched for DNAm signatures of this disorder. Recently, an epigenome-wide association study of monozygotic twins identified an association between DNAm status in the KLK8 (neuropsin) promoter region and severity of depression symptomatology. Methods In this study, we aimed to investigate: (i) if blood DNAm levels, quantified by pyrosequencing, at two CpG sites in the KLK8 promoter are associated with depression symptomatology and depression diagnosis in an independent clinical cohort and (ii) if KLK8 DNAm levels are associated with depression, postpartum depression, and depression symptomatology in four independent methylomic cohorts, with blood and brain DNAm quantified by either MBD-seq or 450 k methylation array. Results DNAm levels in KLK8 were not significantly different between depression cases and controls, and were not significantly associated with any of the depression symptomatology scores after correction for multiple testing (minimum p value for KLK8 CpG1 = 0.12 for ‘Depressed mood,’ and for CpG2 = 0.03 for ‘Loss of self-confidence with other people’). However, investigation of the link between KLK8 promoter DNAm levels and depression-related phenotypes collected from four methylomic cohorts identified significant association (p value < 0.05) between severity of depression symptomatology and blood DNAm levels at seven CpG sites. Conclusions Our findings suggest that variance in blood DNAm levels in KLK8 promoter region is associated with severity of depression symptoms, but not depression diagnosis.

Epidemiology of undiagnosed depression in people with diabetes mellitus: a comparative analysis of Ireland, England and the USA

ObjectivesImproving detection of depression in people with diabetes is recommended. However, little is known about how different health systems compare in depression detection. We estimated and compared the (1) prevalence of depression detection in people with and without diabetes, and (2) association between diabetes and undiagnosed depression across three health systems.DesignCross-sectional analysis of three nationally representative studies: The Irish Longitudinal Study on Ageing, the English Longitudinal Study on Ageing and the Health and Retirement Study.SettingCommunity-dwelling adults in Ireland, England and the USA.ParticipantsAdults aged ≥50 years.Primary and secondary outcome measuresThe primary outcome was depression diagnosis. The secondary outcome was any depression. Any depression was defined by the presence of self-reported doctor-diagnosed depression or current depression symptoms on the Centre for Epidemiological Studies-Depression scale. Depression diagnosis was categorised as: undiagnosed, symptomatic and diagnosed, and asymptomatic and diagnosed. We estimated age-standardised prevalence of depression diagnosis by country and diabetes status. Anyone who self-reported having ever received a doctor diagnosis of diabetes was classified as having diabetes. Among respondents with depression, we estimated the association between diabetes and undiagnosed depression by country using multivariable logistic regression.ResultsThe prevalence of depression (diagnosed and undiagnosed) was higher in people with diabetes in each country with absolute rates varying by country; undiagnosed prevalence (Ireland: diabetes 10.1% (95% CI 7.5% to 12.8%) vs no diabetes 7.5% (95% CI 6.8% to 8.2%), England: diabetes 19.3% (95% CI 16.5% to 22.2%) vs no diabetes 11.8% (95% CI 11.0% to 12.6%), USA: diabetes 7.4% (95% CI 6.4% to 8.4%) vs no diabetes 6.1% (95% CI 5.7% to 6.6%)). In the fully adjusted model, there was no clear pattern of association between diabetes status and undiagnosed depression; Ireland: OR=0.82 (95% CI 0.5 to 1.3), England: OR=1.47 (95% CI 1.0 to 2.1), USA: OR=0.80 (95% CI 0.7 to 1.0).ConclusionsAlthough undiagnosed depression was more prevalent among people with diabetes, the relationship between diabetes and undiagnosed depression differed by country. Targeted efforts are needed to improve depression detection among community-dwelling older adults, particularly those with diabetes.

Loneliness and Depressive Symptoms among Pregnant Black Women during the COVID-19 Pandemic

We explored the associations among perceived stress, depressive symptoms, loneliness, and social support during the COVID-19 pandemic; and differences in perceived stress, depressive symptoms, and social support prior to the pandemic and during the pandemic among pregnant Black women. A sample of 33 pregnant Black women who participated in the Biosocial Impact on Black Births (BIBB) and were still pregnant in May–June 2020 were invited to complete an online survey about their experiences during the pandemic. Fifteen women responded very much or somewhat to experiencing stress and anxiety because of the COVID-19 pandemic. Eight women had CES-D scores ≥23, which have been correlated with depression diagnosis. Women who reported higher levels of loneliness during the COVID-19 pandemic also reported higher levels of perceived stress and depressive symptoms and lower levels of social support during the pandemic. Women who reported lower levels of social support during the pandemic also reported higher levels of perceived stress and depressive symptoms during the pandemic. There were no changes in perceived stress, depressive symptoms, or social support prior to the pandemic and during the pandemic. Clinicians should assess for signs of loneliness and depressive symptoms for pregnant women and offer recommendations for therapy and support groups.