Performance Evaluation of a Voice-Based Depression Assessment System Considering the Number and Type of Input Utterances

It is empirically known that mood changes affect facial expressions and voices. In this study, the authors have focused on the voice to develop a method for estimating depression in individuals from their voices. A short input voice is ideal for applying the proposed method to a wide range of applications. Therefore, we evaluated this method using multiple input utterances while assuming a unit utterance input. The experimental results revealed that depressive states could be estimated with sufficient accuracy using the smallest number of utterances when positive utterances were included in three to four input utterances.

Dimensionality of the Chinese CES-D: Is It Stable across Gender, Time, and Samples?

Depression is a common mental illness among Chinese adolescents. Although the Epidemiological Studies Depression Scale (CES-D) has been widely used in diverse populations, the reported factor structures are inconsistent, and its longitudinal invariance is under-researched. This study examined the psychometric properties and factorial invariance across gender and time of the CES-D among Chinese adolescents. Adolescents aged above 11 years from five schools in Chengdu responded to a questionnaire at Wave 1 (n = 5690). Among them, 4981 participants completed the same questionnaire after six months (Wave 2). The matched sample was composed of 4922 students (51.5% were girls; mean age = 13.15 years) at Wave 1. We used exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to examine the factor structure and performed multi-group CFA to test the factorial invariance across gender and time. A three-factor solution was identified, including “positive affect”, “somatic complaints”, and “depressed affect”. Results of multi-group CFA comparisons supported the factorial invariance of the resultant three-factor solution. Using a new sample of Chinese adolescents in Southwestern China, the present study reproduced earlier findings on adolescents in other areas in China. This study has implications for depression assessment and research in Chinese adolescents.

Depression Scale Recognition over Fusion of Visual and Vocal Expression using Artificial Intellectual Method

Now a day’s supported visual and audio cues for automatic depression assessment may be a fast emerging research subject. This comprehensive evaluation of existing methodologies focuses on machine learning (ML) algorithm and image processing (IP) algorithm, as documented in over sixty articles over the last ten years. There is a visual indicator of depression, several data collection procedures are used, and finally examined the previous year or existing datasets. In this article describes techniques and algorithms as well as methods for dimensionality reduction, visual feature extraction, regression approaches, and classification decision procedures, and also various fusion tactics. A significant meta-analysis of published data is given, based on performance indicators that are robust to chance, to identify general trends and important pressing concerns for further research using visual and verbal cues alone or in combination with signals for automated depression evaluation The suggested work also used deep learning and natural language processing to estimate depression levels based on current video data.

Problems of sleep, depression, quality of life in adolescents with TMD diagnosis

Aim: To verify the relationship between temporal-mandibular dysfunction (TMD) with depression, sleep, sleepiness and quality of life in adolescents aged 13 to 18 years old. Methods: Thirty-eight adolescents being seen at the UFAL Dental Clinic (Federal University of Alagoas), for TMD, and qualifying according to the Research Diagnostic Criteria for Temporomandibular Disorders (RDC / TMD), participated in the study. Two instruments were used to investigate sleep quality: the Pittsburgh Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS); the Oral Health Impact Profile (OHIP-14) for quality-of-life assessment; and the Beck Depression Inventory (BDI-II) for depression assessment. Pearson’s correlation coefficient was used for the relationship of numerical variables. For the means tests, the Student t test was applied (using, when necessary, the Welch correction). For the analyses, the Bonferroni correction was considered. Results: After calculation, αBonferroni correction was applied equal to 0,0005. Of the total number of participants (56% female and 44% male), with a mean age of 14.7). In all comparisons between groups (with and without TMD), there were statistically significant indices for adolescents with TMD in relation to: depression (p=5.6∙10-11), quality of life (p=4.3∙10-12), sleep quality (p=5.0∙10-10), and somnolence (p=0.0002). From the correlation matrix, it was observed that all correlations were significantly positive and moderate. Conclusions: Adolescents with a diagnosis of TMD presented an increase of depression and somnolence, as well as impairment of sleep quality and quality of life, and these same variables can influence on the onset of TMD.

Transformation of Electronic Health Records and Questionnaire Data to OMOP CDM: A Feasibility Study Using SG_T2DM Dataset

Background Diabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats. Objective The study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept. Methods We used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard. Results The content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks. Conclusion Adoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.

Effects of Aqueous Extract of Tulsi and Cardamom on Elderly Depressive Subjects - A Preliminary Clinical Study

An aqueous extract of <em>Ocimum sanctum</em> (Tulsi) and <em>Elettaria cardamomum</em> (Cardamom) was administered to elderly subjects suffering from depression living in selected old age home. Geriatric Depression Assessment Scale was used to assess the level of depression; based on the scale, 40 subjects were selected for the study. The subjects were divided into two groups of twenty each. Experimental group received aqueous extracts of<em> Ocimum sanctum</em> and <em>cardamom</em> for eight weeks, similarly, control group received aqueous extract of <em>green tea leaves.</em> Post assessment was done after eight weeks of treatment in both the control and experimental groups. Aqueous extracts of <em>Ocimum sanctum</em> and <em>cardamom</em> were found to have a significant anti depressive effect on experimental group after 8 weeks, while control group did not show any significant change. Preliminary data of the study showed a significant antidepressive activity of <em>Ocimum sanctum</em> and <em>cardamom</em> extracts.

Prevalence and Course of Depression During the First Year After Mild to Moderate Stroke

Background This study examined the prevalence and longitudinal course of depression during the first year after mild to moderate stroke. Methods and Results We identified patients with mild to moderate ischemic stroke or intracerebral hemorrhage (National Institutes of Health Stroke Scale score <16) and at least 1 depression assessment at 3, 6, or 12 months after stroke (n=648, 542, and 533, respectively) from the Brain Attack Surveillance in Corpus Christi project (2014–2016). Latent transition analysis was used to examine temporal profiles of depressive symptoms assessed by the 8‐item Patient Health Questionnaire between 3 and 12 months after stroke. Mean age was 65.6 years, 49.4% were women, and 56.7% were Mexican Americans. The prevalence of depression after stroke was 35.3% at 3 months, decreased to 24.9% at 6 months, and remained stable at 25.7% at 12 months. Approximately half of the participants classified as having depression at 3 or 6 months showed clinical improvement at the next assessment. Subgroups with distinct patterns of depressive symptoms were identified, including mild/no symptoms, predominant sleep disturbance and fatigue symptoms, affective symptoms, and severe/all symptoms. A majority of participants with mild/no symptoms retained this symptom pattern over time. The probability of transitioning to mild/no symptoms was higher before 6 months compared with the later period, and severe symptoms were more likely to persist after 6 months compared with the earlier period. Conclusions The observed dynamics of depressive symptoms suggest that depression after stroke tends to persist after 6 months among patients with mild to moderate stroke and should be continually monitored and appropriately managed.