Interpreting Depression From Question-wise Long-term Video Recording of SDS Evaluation

Adoption of the cardiopulmonary exercise test in the exercise ability and cardiopulmonary function rehabilitation of coronary artery disease (CAD) patients

BACKGROUND

This study aimed to explore the application of cardiopulmonary exercise testing in coronary artery disease (CAD) patients, evaluate its impact on exercise ability and cardiopulmonary function in patients with coronary heart disease (CHD), and promote the application of cardiopulmonary exercise testing in CAD management.

METHODS

Fifty CHD patients after percutaneous coronary intervention (PCI) were recruited and randomly enrolled into the control (Ctrl) group and intervention (Int) group. Routine health education and health education combined with RT training were carried out for the two groups. Blood lipid levels and lung function were compared between the two groups after intervention. Cardiac function was evaluated by Doppler ultrasonography, and cardiopulmonary fitness and exercise ability were evaluated by a cardiopulmonary exercise test (CPET). The self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were employed to evaluate negative emotions. The 36-item short-form (SF-36) was adopted to evaluate quality of life.

RESULT

Compared with those in the Ctrl group, the levels of serum total cholesterol (TC), triglycerides (TGs), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) decreased in the Int group, while the levels of high-density lipoprotein increased (P < 0.05). The quantitative load results showed that compared with the Ctrl group, the heart rate (HR) and self-perceived fatigue degree of the Int group decreased, and the ST segment increased (P < 0.05). Compared with the Ctrl group, the left ventricular ejection fraction (LVEF), forced expiratory volume at 1 s (FEV1), ratio of forced expiratory volume to forced vital volume (FEV1/FVC%), and maximum chase volume (MVV) increased in the Int group, while the left ventricular end diastolic diameter and left ventricular end contractile diameter decreased (P < 0.05). The results of the CPET showed that compared with the Ctrl group, minute ventilation/carbon dioxide production slope, VE/VCO2 - Peak, anaerobic threshold (AT), peak oxygen pulse (VO2/HR peak), oxygen uptake efficiency platform (OUEP), increasing power exercise time (IPEt), HR recovery 1 min after exercise, peak load power (Watt peak), and value metabolic equivalent (Watt peak) increased in the Int group (P < 0.05). Compared with the Ctrl group, the SAS and SDS scores in the Int group decreased (P < 0.05). The results of the quality of life evaluation showed that compared with the Ctrl group, the score of the SF-36 dimensions increased in the Int group (P < 0.05).

CONCLUSION

RT training can reduce postoperative blood lipid and quantitative load levels in CAD patients and improve adverse mood. Furthermore, it can improve patients' cardiopulmonary function, cardiopulmonary fitness, exercise ability, and quality of life.

Issues and Solutions in Psychiatric Clinical Trial with Case Studies

The coronavirus disease-2019 pandemic resulted in a major increase in depression and anxiety disorders worldwide, which increased the demand for mental health services. However, clinical interventions for treating mental disorders are currently insufficient to meet this growing demand. There is an urgent need to conduct scientific and standardized clinical research that are consistent with the features of mental disorders in order to deliver more effective and safer therapies in the clinic. Our study aimed to expose the challenges, complexities of study design, ethical issues, sample selection, and efficacy evaluation in clinical research for mental disorders. The reliance on subjective symptom presentation and rating scales for diagnosing mental diseases was discovered, emphasizing the lack of clear biological standards, which hampers the construction of rigorous research criteria. We underlined the possibility of psychotherapy in efficacy evaluation alongside medication treatment, proposing for a multidisciplinary approach comprising psychiatrists, neuroscientists, and statisticians. To comprehend mental disorders progression, we recommend the development of artificial intelligence integrated evaluation tools, the use of precise biomarkers, and the strengthening of longitudinal designs. In addition, we advocate for international collaboration to diversity samples and increase the dependability of findings, with the goal of improving clinical research quality in mental disorders through sample representativeness, accurate medical history gathering, and adherence to ethical principles.

Multimodal fusion diagnosis of depression and anxiety based on CNN-LSTM model

BACKGROUND

In recent years, more and more people suffer from depression and anxiety. These symptoms are hard to be spotted and can be very dangerous. Currently, the Self-Reported Anxiety Scale (SAS) and Self-Reported Depression Scale (SDS) are commonly used for initial screening for depression and anxiety disorders. However, the information contained in these two scales is limited, while the symptoms of subjects are various and complex, which results in the inconsistency between the questionnaire evaluation results and the clinician's diagnosis results. To fully mine the scale data, we propose a method to extract the features from the facial expression and movements, which are generated from the video recorded simultaneously when subjects fill in the scale. Then we collect the facial expression, movements and scale information to establish a multimodal framework for improving the accuracy and robustness of the diagnosis of depression and anxiety.

METHODS

We collect the scale results of the subjects and the videos when filling in the scales. Given the two scales, SAS and SDS, we construct a model with two branches, where each branch processes the multimodal data of SAS and SDS, respectively. In the branch, we first build a convolutional neural network (CNN) to extracts the facial expression features in each frame of images. Secondly, we establish a long short-term memory (LSTM) network to further embedding the facial expression feature and build the connections between various frames, so that the movement feature in the video can be generated. Thirdly, we transform the scale scores into one-hot format, and feed them into the corresponding branch of the network to further mining the information of the multimodal data. Finally, we fuse the embeddings of these two branches to generate inference results of depression and anxiety.

RESULTS AND CONCLUSIONS

Based on the score results of SAS and SDS, our multimodal model further mines the video information, and can reach the accuracy of 0.946 in diagnosing depression and anxiety. This study demonstrates the feasibility of using our CNN-LSTM-based multimodal model for initial screening and diagnosis of depression and anxiety disorders with high diagnostic performance.

Non-Contact Negative Mood State Detection Using Reliability-Focused Multi-Modal Fusion Model

Negative mood states include tension, depression, anger, fatigue, and confusion, which represent the weak internal emotions of a human. Negative mood states exert adverse impact on individuals' ability to make rational decisions, which entails the practicable method of negative mood state detection. The most commonly used negative mood state detection methods are based on the psychological scale, which requires additional work and brings inconvenience to the subject in the application scenarios. To overcome this challenge, this paper proposes a novel non-contact negative mood state detection method according to the knowledge of affective computing. The POMS-net model is used to extract temporal-spatial features from visible and infrared thermal videos, and the negative mood state detection is realized using data reliability-focused multi-modal fusion. The proposed method is verified using the HDT-BR dataset collected in the aerospace medicine experiment "Earth-Star II" and the VIRI public dataset. The experimental results on the datasets verify that our method outperforms the comparison methods.

Deep 3D-CNN for Depression Diagnosis with Facial Video Recording of Self-Rating Depression Scale Questionnaire

The Self-Rating Depression Scale (SDS) questionnaire is commonly utilized for effective depression preliminary screening. The uncontrolled self-administered measure, on the other hand, maybe readily influenced by insouciant or dishonest responses, yielding different findings from the clinician-administered diagnostic. Facial expression (FE) and behaviors are important in clinician-administered assessments, but they are underappreciated in self-administered evaluations. We use a new dataset of 200 participants to demonstrate the validity of self-rating questionnaires and their accompanying question-by-question video recordings in this study. We offer an end-to-end system to handle the face video recording that is conditioned on the questionnaire answers and the responding time to automatically interpret sadness from the SDS assessment and the associated video. We modified a 3D-CNN for temporal feature extraction and compared various state-of-the-art temporal modeling techniques. The superior performance of our system shows the validity of combining facial video recording with the SDS score for more accurate self-diagnose.