Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models.

The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart rate variability (HRV) data collected to understand the balance of the autonomic nervous system on statistical analysis methods to perform classification through statistical analysis. In this research, three mood disorder severity or subtype classification algorithms are presented through multimodal analysis of data on the collected heart-related data variables and hidden features from the variables of time and frequency domain of HRV. Comparing the classification performance of the statistical analysis widely used in existing major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD) classification studies and the multimodality deep neural network analysis newly proposed in this study, it was confirmed that the severity or subtype classification accuracy performance of each disease improved by 0.118, 0.231, and 0.125 on average. Through the study, it was confirmed that deep learning analysis of biomarker data such as HRV can be applied as a primary identification and diagnosis aid for mental diseases, and that it can help to objectively diagnose psychiatrists in that it can confirm not only the diagnosed disease but also the current mood status.

Deep breathing in your hands: designing and assessing a DTx mobile app.

Digital Therapeutics (DTx) are experiencing rapid advancements within mobile and mental healthcare sectors, with their ubiquity and enhanced accessibility setting them apart as uniquely effective solutions. In this evolving context, our research focuses on deep breathing, a vital technique in mental health management, aiming to optimize its application in DTx mobile platforms. Based on well-founded theories, we introduced a gamified and affordance-driven design, facilitating intuitive breath control. To enhance user engagement, we deployed the Mel Frequency Cepstral Coefficient (MFCC)-driven personalized machine learning method for accurate biofeedback visualization. To assess our design, we enlisted 70 participants, segregating them into a control and an intervention group. We evaluated Heart Rate Variability (HRV) metrics and collated user experience feedback. A key finding of our research is the stabilization of the Standard Deviation of the NN Interval (SDNN) within Heart Rate Variability (HRV), which is critical for stress reduction and overall health improvement. Our intervention group observed a pronounced stabilization in SDNN, indicating significant stress alleviation compared to the control group. This finding underscores the practical impact of our DTx solution in managing stress and promoting mental health. Furthermore, in the assessment of our intervention cohort, we observed a significant increase in perceived enjoyment, with a notable 22% higher score and 10.69% increase in positive attitudes toward the application compared to the control group. These metrics underscore our DTx solution's effectiveness in improving user engagement and fostering a positive disposition toward digital therapeutic efficacy. Although current technology poses challenges in seamlessly incorporating machine learning into mobile platforms, our model demonstrated superior effectiveness and user experience compared to existing solutions. We believe this result demonstrates the potential of our user-centric machine learning techniques, such as gamified and affordance-based approaches with MFCC, which could contribute significantly to the field of mobile mental healthcare.