Automatic recognition of depression based on audio and video: A review.

Depression is a common mental health disorder. With current depression detection methods, specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment. Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized. Specialized physicians usually require extensive training and experience to capture changes in these features. Advancements in deep learning technology have provided technical support for capturing non-biological markers. Several researchers have proposed automatic depression estimation (ADE) systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening. This article summarizes commonly used public datasets and recent research on audio- and video-based ADE based on three perspectives: Datasets, deficiencies in existing research, and future development directions.

TSFFM: Depression detection based on latent association of facial and body expressions.

Depression is a prevalent mental disorder worldwide. Early screening and treatment are crucial in preventing the progression of the illness. Existing emotion-based depression recognition methods primarily rely on facial expressions, while body expressions as a means of emotional expression have been overlooked. To aid in the identification of depression, we recruited 156 participants for an emotional stimulation experiment, gathering data on facial and body expressions. Our analysis revealed notable distinctions in facial and body expressions between the case group and the control group and a synergistic relationship between these variables. Hence, we propose a two-stream feature fusion model (TSFFM) that integrates facial and body features. The central component of TSFFM is the Fusion and Extraction (FE) module. In contrast to conventional methods such as feature concatenation and decision fusion, our approach, FE, places a greater emphasis on in-depth analysis during the feature extraction and fusion processes. Firstly, within FE, we carry out local enhancement of facial and body features, employing an embedded attention mechanism, eliminating the need for original image segmentation and the use of multiple feature extractors. Secondly, FE conducts the extraction of temporal features to better capture the dynamic aspects of expression patterns. Finally, we retain and fuse informative data from different temporal and spatial features to support the ultimate decision. TSFFM achieves an Accuracy and F1-score of 0.896 and 0.896 on the depression emotional stimulus dataset, respectively. On the AVEC2014 dataset, TSFFM achieves MAE and RMSE values of 5.749 and 7.909, respectively. Furthermore, TSFFM has undergone testing on additional public datasets to showcase the effectiveness of the FE module.

Peony-Glycyrrhiza Decoction for Antipsychotic-Related Hyperprolactinemia in Patients with Schizophrenia: A Randomized Controlled Trial.

Background: Most antipsychotic drugs are dopamine receptor antagonists that usually lead to abnormal increases in prolactin concentrations and the development of hyperprolactinemia (HPRL), which in turn causes sexual dysfunction in patients. Peony-Glycyrrhiza Decoction (PGD) enhanced dopamine D2 receptors (DRD2) and dopamine transporter (DAT) and significantly reversed the expression of DRD2 and DAT. Therefore, we hypothesized that PGD might effectively improve hyperprolactinemia and alleviate sexual dysfunction in patients. Methods: We performed an 8-week randomized controlled study on 62 subjects with schizophrenia who were randomized into two groups. The experimental group was treated with the PGD intervention, and the control group did not receive treatment. The primary outcome indicators were the levels of sex hormones and the total Arizona Sexual Experience Scale (ASEX) score. Results: There was a significant difference in PRL levels between the two groups at weeks 4 and 8. From the beginning to the end of the experiment, there was a significant increase in PRL levels in the control group, while there was no significant change in the experimental group. The ASEX scale assessed sexual function in both groups, and patients in the experimental group showed an improvement in sexual function at week 8. During the experiment, the two groups found no differences between Positive and Negative Syndrome Scale (PANSS) scores and Treatment Emergent Symptom Scale (TESS) scores. Conclusion: PGD significantly improved the patient's sexual function but was less effective in reducing prolactin levels and may prevent further increases in prolactin levels.

Analysis and Recognition of Voluntary Facial Expression Mimicry Based on Depressed Patients.

Many clinical studies have shown that facial expression recognition and cognitive function are impaired in depressed patients. Different from spontaneous facial expression mimicry (SFEM), 164 subjects (82 in a case group and 82 in a control group) participated in our voluntary facial expression mimicry (VFEM) experiment using expressions of neutrality, anger, disgust, fear, happiness, sadness and surprise. Our research is as follows. First, we collected a large amount of subject data for VFEM. Second, we extracted the geometric features of subject facial expression images for VFEM and used Spearman correlation analysis, a random forest, and logistic regression-based recursive feature elimination (LR-RFE) to perform feature selection. The features selected revealed the difference between the case group and the control group. Third, we combined geometric features with the original images and improved the advanced deep learning facial expression recognition (FER) algorithms in different systems. We propose the E-ViT and E-ResNet based on VFEM. The accuracies and F1 scores were higher than those of the baseline models, respectively. Our research proved that it is effective to use feature selection to screen geometric features and combine them with a deep learning model for depression facial expression recognition.

A method for diagnosing depression: Facial expression mimicry is evaluated by facial expression recognition.

BACKGROUND: Considerable evidence has shown that facial expression mimicry is impaired in patients with depression. We aimed to evaluate voluntary expression mimicry by facial expression recognition for diagnosing depression. METHODS: A total of 168 participants performed voluntary expression mimicry task, posing anger, disgust, fear, happiness, neutrality, sadness, and surprise. 9 healthy raters performed facial expression recognition task through the observer scoring method, and evaluated seven expressions imitated by participants. Emotional scores were calculated to measure any differences between two groups of participants and provided a basis for clinical diagnosis of depression. RESULTS: Compared with the control group, the depression group had lower accuracy in imitating happiness. Compared with the control group, the depression group imitated a higher neutrality bias for sadness, surprise, happiness and disgust, while sadness and surprise had a lower happiness bias; for imitating happiness, the depression group showed higher anger, disgust, fear, neutrality, and surprise bias; for imitating neutrality, the depression group showed higher sadness bias, and lower happiness bias. Compared with the control group, the raters had a higher reaction time to recognize the happiness imitated by depression group, and it was positively correlated with severity of depression. The severity of depression was also negatively correlated with accuracy in imitating happiness, and positively correlated with neutrality bias of imitating surprise. LIMITATIONS: The ecological effectiveness of static stimulus materials is lower than that of dynamic stimuli. Without synchronized functional imaging, there is no way to link brain activation patterns. CONCLUSION: The ability of patients with depression to voluntarily imitate facial expressions declines, which is mainly reflected in accuracy, bias and recognizability. Our experiment has discovered deficits in these aspects of patients with depression, which will be used as a method for diagnosising depression.

Research progress in the correlation between SREBP/PCSK9 pathway and lipid metabolism disorders induced by antipsychotics. / SREBP/PCSK9é€šè·¯ä¸ŽæŠ—ç²¾ç¥žç— è¯ç‰©æ‰€è‡´è„‚ä»£è°¢ç´Šä¹±ç›¸å ³æ€§çš„ç ”ç©¶è¿›å±•.

Antipsychotic medications are commonly used to treat schizophrenia, but they can have negative effects on lipid metabolism, leading to an increased risk of cardiovascular diseases, reduced life expectancy, and difficulties with treatment adherence. The specific mechanisms by which antipsychotics disrupt lipid metabolism are not well understood. Sterol regulatory element-binding proteins (SREBPs) are important transcriptional factors that regulate lipid metabolism. Proprotein convertase subtilisin/kexin type 9 (PCSK9), a gene regulated by SREBPs, plays a critical role in controlling levels of low-density lipoprotein cholesterol (LDL-C) and has become a focus of research on lipid-lowering drugs. Recent studies have shown that antipsychotic drugs can affect lipid metabolism through the SREBP/PCSK9 pathway. A deep understanding of the mechanism for this pathway in antipsychotic drug-related metabolic abnormalities will promote the prevention of lipid metabolism disorders in patients with schizophrenia and the development and application of new drugs.

Depression and Severity Detection Based on Body Kinematic Features: Using Kinect Recorded Skeleton Data of Simple Action.

Relative limb movement is an important feature in assessing depression. In this study, we looked into whether a skeleton-mimetic task using natural stimuli may help people recognize depression. We innovatively used Kinect V2 to collect participant data. Sequential skeletal data was directly extracted from the original Kinect-3D and tetrad coordinates of the participant's 25 body joints. Two constructed skeletal datasets of whole-body joints (including binary classification and multi classification) were input into the proposed model for depression recognition after data preparation. We improved the temporal convolution network (TCN), creating novel spatial attention dilated TCN (SATCN) network that included a hierarchy of temporal convolution groups with different dilated convolution scales to capture important skeletal features and a spatial attention block for final result prediction. The depression and non-depression groups can be classified automatically with a maximum accuracy of 75.8% in the binary classification task, and 64.3% accuracy in the multi classification dataset to recognize more fine-grained identification of depression severity, according to experimental results. Our experiments and methods based on Kinect V2 can not only identify and screen depression patients but also effectively observe the recovery level of depression patients during the recovery process. For example, in the change from severe depression to moderate or mild depression multi classification dataset.

Multi-modal depression detection based on emotional audio and evaluation text.

BACKGROUND: Early detection of depression is very important for the treatment of patients. In view of the current inefficient screening methods for depression, the research of depression identification technology is a complex problem with application value. METHODS: Our research propose a new experimental method for depression detection based on audio and text. 160 Chinese subjects are investigated in this study. It is worth noting that we propose a text reading experiment to make subjects emotions change rapidly. It will be called Segmental Emotional Speech Experiment (SESE) below. We extract 384-dimensional Low-level audio features to find the differences of different emotional change in SESE. At the same time, our research propose a multi-modal fusion method based on DeepSpectrum features and word vector features to detect depression by using deep learning. RESULTS: Our experiment proved that SESE can improve the recognition accuracy of depression and found differences in Low-level audio features. Case group and Control group, gender and age are grouped for verification. It is also satisfactory that the multi-modal fusion model achieves accuracy of 0.912 and F1 score of 0.906. CONCLUSIONS: Our contribution is twofold. First, we propose and verify SESE, which can provide a new experimental idea for the follow-up researchers. Secondly, a new efficient multi-modal depression recognition model is proposed.