Severity Classification Using Dynamic Time Warping-Based Voice Biomarkers for Patients With COVID-19: Feasibility Cross-Sectional Study.

Background: In Japan, individuals with mild COVID-19 illness previously required to be monitored in designated areas and were hospitalized only if their condition worsened to moderate illness or worse. Daily monitoring using a pulse oximeter was a crucial indicator for hospitalization. However, a drastic increase in the number of patients resulted in a shortage of pulse oximeters for monitoring. Therefore, an alternative and cost-effective method for monitoring patients with mild illness was required. Previous studies have shown that voice biomarkers for Parkinson disease or Alzheimer disease are useful for classifying or monitoring symptoms; thus, we tried to adapt voice biomarkers for classifying the severity of COVID-19 using a dynamic time warping (DTW) algorithm where voice wavelets can be treated as 2D features; the differences between wavelet features are calculated as scores. Objective: This feasibility study aimed to test whether DTW-based indices can generate voice biomarkers for a binary classification model using COVID-19 patients' voices to distinguish moderate illness from mild illness at a significant level. Methods: We conducted a cross-sectional study using voice samples of COVID-19 patients. Three kinds of long vowels were processed into 10-cycle waveforms with standardized power and time axes. The DTW-based indices were generated by all pairs of waveforms and tested with the Mann-Whitney U test (α<.01) and verified with a linear discrimination analysis and confusion matrix to determine which indices were better for binary classification of disease severity. A binary classification model was generated based on a generalized linear model (GLM) using the most promising indices as predictors. The receiver operating characteristic curve/area under the curve (ROC/AUC) validated the model performance, and the confusion matrix calculated the model accuracy. Results: Participants in this study (n=295) were infected with COVID-19 between June 2021 and March 2022, were aged 20 years or older, and recuperated in Kanagawa prefecture. Voice samples (n=110) were selected from the participants' attribution matrix based on age group, sex, time of infection, and whether they had mild illness (n=61) or moderate illness (n=49). The DTW-based variance indices were found to be significant (P<.001, except for 1 of 6 indices), with a balanced accuracy in the range between 79% and 88.6% for the /a/, /e/, and /u/ vowel sounds. The GLM achieved a high balance accuracy of 86.3% (for /a/), 80.2% (for /e/), and 88% (for /u/) and ROC/AUC of 94.8% (95% CI 90.6%-94.8%) for /a/, 86.5% (95% CI 79.8%-86.5%) for /e/, and 95.6% (95% CI 92.1%-95.6%) for /u/. Conclusions: The proposed model can be a voice biomarker for an alternative and cost-effective method of monitoring the progress of COVID-19 patients in care.

Discrimination of mild cognitive impairment based on involuntary changes caused in voice elements.

In this study, the technique associated with the capturing involuntary changes in voice elements caused by diseases is applied to diagnose them and a voice index is proposed to discriminate mild cognitive impairments. The participants in this study included 399 elderly people aged 65 years or older living in Matsumoto City, Nagano Prefecture, Japan. The participants were categorized into healthy and mild cognitive impairment groups based on clinical evaluation. It was hypothesized that as dementia progressed, task performance would become more challenging, and the effects on vocal cords and prosody would change significantly. In the study, voice samples of the participants were recorded while they were engaged in mental calculational tasks and during the reading of the results of the calculations written on paper. The change in prosody during the calculation from that during reading was expressed based on the difference in the acoustics. Principal component analysis was used to aggregate groups of voice features with similar characteristics of feature differences into several principal components. These principal components were combined with logistic regression analysis to propose a voice index to discriminate different mild cognitive impairment types. Discrimination accuracies of 90% and 65% were obtained for discriminations using the proposed index on the training and verification data (obtained from a population different from the training data), respectively. Therefore, it is suggested that the proposed index may be utilized as a means for discriminating mild cognitive impairments.

Estimating Depressive Symptom Class from Voice.

Voice-based depression detection methods have been studied worldwide as an objective and easy method to detect depression. Conventional studies estimate the presence or severity of depression. However, an estimation of symptoms is a necessary technique not only to treat depression, but also to relieve patients' distress. Hence, we studied a method for clustering symptoms from HAM-D scores of depressed patients and by estimating patients in different symptom groups based on acoustic features of their speech. We could separate different symptom groups with an accuracy of 79%. The results suggest that voice from speech can estimate the symptoms associated with depression.

Distinguish the Severity of Illness Associated with Novel Coronavirus (COVID-19) Infection via Sustained Vowel Speech Features.

The authors are currently conducting research on methods to estimate psychiatric and neurological disorders from a voice by focusing on the features of speech. It is empirically known that numerous psychosomatic symptoms appear in voice biomarkers; in this study, we examined the effectiveness of distinguishing changes in the symptoms associated with novel coronavirus infection using speech features. Multiple speech features were extracted from the voice recordings, and, as a countermeasure against overfitting, we selected features using statistical analysis and feature selection methods utilizing pseudo data and built and verified machine learning algorithm models using LightGBM. Applying 5-fold cross-validation, and using three types of sustained vowel sounds of /Ah/, /Eh/, and /Uh/, we achieved a high performance (accuracy and AUC) of over 88% in distinguishing "asymptomatic or mild illness (symptoms)" and "moderate illness 1 (symptoms)". Accordingly, the results suggest that the proposed index using voice (speech features) can likely be used in distinguishing the symptoms associated with novel coronavirus infection.

The relationship between performance feedback from supervisors and subordinates' work engagement among employees in elderly care facilities: structural equation modeling.

Despite the increasing need for nursing care services, the turnover rate of care workers is high in Japan. Since the most common reason for quitting nursing care jobs was problems with interpersonal relationships at work, creating psychosocially safe working environments is urgent. This study aimed to investigate the mediating effects of trust in supervisors (TS) on the association between positive feedback (PF)/negative feedback (NF) and work engagement (WE) based on the job demands-resources theory and conservation of resources theory. We conducted anonymous cross-sectional surveys of 469 employees at elderly care facilities in Japan. Structural equation modeling was used to investigate the causal relationships between the variables. The results showed that PF had significant positive effects on WE, directly and indirectly through TS. By contrast, NF had a nonsignificant positive effect on TS or WE. Tucker-Lewis Index [TLI] was 0.917, Comparative Fit Index [CFI] was 0.927, Root Mean Squared Error of Approximation [RMSEA] was 0.096, and Standardized Root Mean squared Residual [SRMR] was 0.042. The study results indicate that sufficient PF is needed to improve subordinates' WE through TS in elderly care facilities.

Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach.

In general, it is common knowledge that people's feelings are reflected in their voice and facial expressions. This research work focuses on developing techniques for diagnosing depression based on acoustic properties of the voice. In this study, we developed a composite index of vocal acoustic properties that can be used for depression detection. Voice recordings were collected from patients undergoing outpatient treatment for major depressive disorder at a hospital or clinic following a physician's diagnosis. Numerous features were extracted from the collected audio data using openSMILE software. Furthermore, qualitatively similar features were combined using principal component analysis. The resulting components were incorporated as parameters in a logistic regression based classifier, which achieved a diagnostic accuracy of ~90% on the training set and ~80% on the test set. Lastly, the proposed metric could serve as a new measure for evaluation of major depressive disorder.

Relationship between Psychological Stress Determined by Voice Analysis and Periodontal Status: A Cohort Study.

In modern society, evaluation and management of psychological stress may be important for the prevention of periodontal disease. The purpose of this study was to examine the relationship between psychological stress (vitality and mental activity) evaluated by Mind Monitoring System (MIMOSYS) and periodontal status. Forty students of Okayama University underwent the oral examination and self-reported questionnaire on the first day (baseline) and the 14th day (follow-up). Voice recording was performed every day with the MIMOSYS app during the whole study period. The participants completed the Patient Health Questionnaire (PHQ)-9 and Beck Depression Inventory (BDI) at baseline and at follow-up. Spearman's rank correlation coefficient was used to determine the significance of correlations among variables. The PHQ-9 and BDI scores were negatively correlated with vitality in the morning. Change in vitality in the morning was significantly correlated with changes in periodontal inflammation. Mental activity was significantly correlated with change in mean probing pocket depth. This result shows that measurement of psychological stress using a voice-based tool to assess mental health may contribute to the early detection of periodontal disease.

Evaluation of emotional arousal level and depression severity using voice-derived sound pressure change acceleration.

In this research, we propose a new index of emotional arousal level using sound pressure change acceleration, called the emotional arousal level voice index (EALVI), and investigate the relationship between this index and depression severity. First, EALVI values were calculated from various speech recordings in the interactive emotional dyadic motion capture database, and the correlation with the emotional arousal level of each voice was examined. The resulting correlation coefficient was 0.52 (n = 10,039, p < 2.2 × 10-16). We collected a total of 178 datasets comprising 10 speech phrases and the Hamilton Rating Scale for Depression (HAM-D) score of outpatients with major depression at the Ginza Taimei Clinic (GTC) and the National Defense Medical College (NDMC) Hospital. The correlation coefficients between the EALVI and HAM-D scores were - 0.33 (n = 88, p = 1.8 × 10-3) and - 0.43 (n = 90, p = 2.2 × 10-5) at the GTC and NDMC, respectively. Next, the dataset was divided into "no depression" (HAM-D < 8) and "depression" groups (HAM-D ≥ 8) according to the HAM-D score. The number of patients in the "no depression" and "depression" groups were 10 and 78 in the GTC data, and 65 and 25 in the NDMC data, respectively. There was a significant difference in the mean EALVI values between the two groups in both the GTC and NDMC data (p = 8.9 × 10-3, Cliff's delta = 0.51 and p = 1.6 × 10-3; Cliff's delta = 0.43, respectively). The area under the curve of the receiver operating characteristic curve when discriminating both groups by EALVI was 0.76 in GTC data and 0.72 in NDMC data. Indirectly, the data suggest that there is some relationship between emotional arousal level and depression severity.

Depressive Mood Assessment Method Based on Emotion Level Derived from Voice: Comparison of Voice Features of Individuals with Major Depressive Disorders and Healthy Controls.

BACKGROUND: In many developed countries, mood disorders have become problematic, and the economic loss due to treatment costs and interference with work is immeasurable. Therefore, a simple technique to determine individuals' depressive state and stress level is desired. METHODS: We developed a method to assess specific the psychological issues of individuals with major depressive disorders using emotional components contained in their voice. We propose two indices: vitality, a short-term index, and mental activity, a long-term index capturing trends in vitality. To evaluate our method, we used the voices of healthy individuals (n = 14) and patients with major depression (n = 30). The patients were also assessed by specialists using the Hamilton Rating Scale for Depression (HAM-D). RESULTS: A significant negative correlation existed between the vitality extracted from the voices and HAM-D scores (r = -0.33, p < 0.05). Furthermore, we could discriminate the voice data of healthy individuals and patients with depression with a high accuracy using the vitality indicator (p = 0.0085, area under the curve of the receiver operating characteristic curve = 0.76).

Computational identification of variables in neonatal vocalizations predictive for postpubertal social behaviors in a mouse model of 16p11.2 deletion.

Autism spectrum disorder (ASD) is often signaled by atypical cries during infancy. Copy number variants (CNVs) provide genetically identifiable cases of ASD, but how early atypical cries predict a later onset of ASD among CNV carriers is not understood in humans. Genetic mouse models of CNVs have provided a reliable tool to experimentally isolate the impact of CNVs and identify early predictors for later abnormalities in behaviors relevant to ASD. However, many technical issues have confounded the phenotypic characterization of such mouse models, including systematically biased genetic backgrounds and weak or absent behavioral phenotypes. To address these issues, we developed a coisogenic mouse model of human proximal 16p11.2 hemizygous deletion and applied computational approaches to identify hidden variables within neonatal vocalizations that have predictive power for postpubertal dimensions relevant to ASD. After variables of neonatal vocalizations were selected by least absolute shrinkage and selection operator (Lasso), random forest, and Markov model, regression models were constructed to predict postpubertal dimensions relevant to ASD. While the average scores of many standard behavioral assays designed to model dimensions did not differentiate a model of 16p11.2 hemizygous deletion and wild-type littermates, specific call types and call sequences of neonatal vocalizations predicted individual variability of postpubertal reciprocal social interaction and olfactory responses to a social cue in a genotype-specific manner. Deep-phenotyping and computational analyses identified hidden variables within neonatal social communication that are predictive of postpubertal behaviors.

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.

Evaluation of the Severity of Major Depression Using a Voice Index for Emotional Arousal.

Recently, the relationship between emotional arousal and depression has been studied. Focusing on this relationship, we first developed an arousal level voice index (ALVI) to measure arousal levels using the Interactive Emotional Dyadic Motion Capture database. Then, we calculated ALVI from the voices of depressed patients from two hospitals (Ginza Taimei Clinic (H1) and National Defense Medical College hospital (H2)) and compared them with the severity of depression as measured by the Hamilton Rating Scale for Depression (HAM-D). Depending on the HAM-D score, the datasets were classified into a no depression (HAM-D < 8) and a depression group (HAM-D ≥ 8) for each hospital. A comparison of the mean ALVI between the groups was performed using the Wilcoxon rank-sum test and a significant difference at the level of 10% (p = 0.094) at H1 and 1% (p = 0.0038) at H2 was determined. The area under the curve (AUC) of the receiver operating characteristic was 0.66 when categorizing between the two groups for H1, and the AUC for H2 was 0.70. The relationship between arousal level and depression severity was indirectly suggested via the ALVI.

A web visualization tool using T cell subsets as the predictor to evaluate COVID-19 patient's severity.

Wuhan, China was the epicenter of the 2019 coronavirus outbreak. As a designated hospital for COVID-19, Wuhan Pulmonary Hospital has received over 700 COVID-19 patients. With the COVID-19 becoming a pandemic all over the world, we aim to share our epidemiological and clinical findings with the global community. We studied 340 confirmed COVID-19 patients with clear clinical outcomes from Wuhan Pulmonary Hospital, including 310 discharged cases and 30 death cases. We analyzed their demographic, epidemiological, clinical and laboratory data and implemented our findings into an interactive, free access web application to evaluate COVID-19 patient's severity level. Our results show that baseline T cell subsets results differed significantly between the discharged cases and the death cases in Mann Whitney U test: Total T cells (p < 0.001), Helper T cells (p <0.001), Suppressor T cells (p <0.001), and TH/TSC (Helper/Suppressor ratio, p<0.001). Multivariate logistic regression model with death or discharge as the outcome resulted in the following significant predictors: age (OR 1.05, 95% CI, 1.00 to 1.10), underlying disease status (OR 3.42, 95% CI, 1.30 to 9.95), Helper T cells on the log scale (OR 0.22, 95% CI, 0.12 to 0.40), and TH/TSC on the log scale (OR 4.80, 95% CI, 2.12 to 11.86). The AUC for the logistic regression model is 0.90 (95% CI, 0.84 to 0.95), suggesting the model has a very good predictive power. Our findings suggest that while age and underlying diseases are known risk factors for poor prognosis, patients with a less damaged immune system at the time of hospitalization had higher chance of recovery. Close monitoring of the T cell subsets might provide valuable information of the patient's condition change during the treatment process. Our web visualization application can be used as a supplementary tool for the evaluation.

Association Between Electroencephalogram-Derived Sleep Measures and the Change of Emotional Status Analyzed Using Voice Patterns: Observational Pilot Study.

BACKGROUND: Measuring emotional status objectively is challenging, but voice pattern analysis has been reported to be useful in the study of emotion. OBJECTIVE: The purpose of this pilot study was to investigate the association between specific sleep measures and the change of emotional status based on voice patterns measured before and after nighttime sleep. METHODS: A total of 20 volunteers were recruited. Their objective sleep measures were obtained using a portable single-channel electroencephalogram system, and their emotional status was assessed using MIMOSYS, a smartphone app analyzing voice patterns. The study analyzed 73 sleep episodes from 18 participants for the association between the change of emotional status following nighttime sleep (Δvitality) and specific sleep measures. RESULTS: A significant association was identified between total sleep time and Δvitality (regression coefficient: 0.036, P=.008). A significant inverse association was also found between sleep onset latency and Δvitality (regression coefficient: -0.026, P=.001). There was no significant association between Δvitality and sleep efficiency or number of awakenings. CONCLUSIONS: Total sleep time and sleep onset latency are significantly associated with Δvitality, which indicates a change of emotional status following nighttime sleep. This is the first study to report the association between the emotional status assessed using voice pattern and specific sleep measures.

Effectiveness of a Voice-Based Mental Health Evaluation System for Mobile Devices: Prospective Study.

BACKGROUND: We developed a system for monitoring mental health using voice data from daily phone calls, termed Mind Monitoring System (MIMOSYS), by implementing a method for estimating mental health status from voice data. OBJECTIVE: The objective of this study was to evaluate the potential of this system for detecting depressive states and monitoring stress-induced mental changes. METHODS: We opened our system to the public in the form of a prospective study in which data were collected over 2 years from a large, unspecified sample of users. We used these data to analyze the relationships between the rate of continued use, the men-to-women ratio, and existing psychological tests for this system over the study duration. Moreover, we analyzed changes in mental data over time under stress from particular life events. RESULTS: The system had a high rate of continued use. Voice indicators showed that women have more depressive tendencies than men, matching the rate of depression in Japan. The system's voice indicators and the scores on classical psychological tests were correlated. We confirmed deteriorating mental health for users in areas affected by major earthquakes in Japan around the time of the earthquakes. CONCLUSIONS: The results suggest that although this system is insufficient for detecting depression, it may be effective for monitoring changes in mental health due to stress. The greatest feature of our system is mental health monitoring, which is most effectively accomplished by performing long-term time-series analysis of the acquired data considering the user's life events. Such a system can improve the implementation of patient interventions by evaluating objective data along with life events.

Effects of long- and short-term experiences on stress during identification works of dead bodies: Rapid stress level measurement using voice.

OBJECTIVE: The mental health issues of personnel dealing with the deceased at times of disasters is a problem and techniques are needed that allow for real-time, easy-to-use stress checks. We have studied techniques for measuring mental state using voice analysis which has the benefit of being non-invasive, easy-to-use, and can be performed in real-time. For this study, we used voice measurement to determine the stress experienced during body identification training workshops for dentists. We studied whether or not stress levels were affected by having previous experience with body identification either in actual disaster settings or during training. DESIGN: Since participants training using actual dead bodies in particular are expected to suffer higher stress exposure, we also assessed their mental state pre- and post-training using actual dead bodies. RESULTS: The results confirmed marked differences in the mental state between before and after training in participants without any actual experience, between participants who engaged in training using manikins before actual dead bodies and participants who did not. CONCLUSIONS: These results suggest that, in body identification training, the level of stress when coming into contact with dead bodies varies depending on participants' experience and the training sequence. Moreover, it is believed that voice-based stress assessment can be conducted in the limited time during training sessions and that it can be usefully implemented in actual disaster response settings.

A validation study of a consumer wearable sleep tracker compared to a portable EEG system in naturalistic conditions.

OBJECTIVE: To compare a wearable device, the Fitbit Versa (FV), to a validated portable single-channel EEG system across multiple nights in a naturalistic environment. METHODS: Twenty participants (10 men and 10 women) aged 25-67 years were recruited for the present study. Study duration was 14 days during which participants were asked to wear the FV daily and nightly. The study intended to reproduce free-living conditions; thus, no guidelines for sleep or activity were imposed on the participants. A total of 138 person-nights, equivalent to 76,539 epochs, were used in the validation process. Sleep measures were compared between the FV and portable EEG using Bland-Altman plots, paired t-tests and epoch-by-epoch (EBE) analyses. RESULTS: The FV showed no significant bias with the EEG for the global sleep measures time in bed (TIB) and total sleep time (TST), and for calculated sleep efficiency (cSE = [TST/TIB] x 100). The FV had 92.1% sensitivity, 54.1% specificity, and 88.5% accuracy with a Cohen's kappa of 0.41, but a prevalence- and bias adjusted kappa of 0.77. The predictive values for sleep (PVS; positive predictive value) and wakefulness (PVW; negative predictive value) were 95.0% and 42.0%, respectively. The FV showed significant bias compared to the portable EEG for time spent in specific sleep stages, for SE as provided by FV, for sleep onset latency, sleep period time, and wake after sleep onset. CONCLUSIONS: The consumer sleep tracker could be a useful tool for measuring sleep duration in longitudinal epidemiologic naturalistic studies albeit with some limitations in specificity.

Molecular Hydrogen Prevents Social Deficits and Depression-Like Behaviors Induced by Low-Intensity Blast in Mice.

Detonation of explosive devices creates blast waves, which can injure brains even in the absence of external injuries. Among these, blast-induced mild traumatic brain injury (bmTBI) is increasing in military populations, such as in the wars in Afghanistan, Iraq, and Syria. Although the clinical presentation of bmTBI is not precisely defined, it is frequently associated with psycho-neurological deficits and usually manifests in the form of poly-trauma including psychiatric morbidity and cognitive disruption. Although the underlying mechanisms of bmTBI are largely unknown, some studies suggested that bmTBI is associated with blood-brain barrier disruption, oxidative stress, and edema in the brain. The present study investigated the effects of novel antioxidant, molecular hydrogen gas, on bmTBI using a laboratory-scale shock tube model in mice. Hydrogen gas has a strong prospect for clinical use due to easy preparation, low-cost, and no side effects. The administration of hydrogen gas significantly attenuated the behavioral deficits observed in our bmTBI model, suggesting that hydrogen application might be a strong therapeutic method for treatment of bmTBI.

Disaster Victim Identification: Psychological Distress and Posttraumatic Stress in Dentists After the 2011 Fukushima Disaster.

OBJECTIVE: To evaluate the correlates of psychological responses in dentists who conducted disaster victim identification (DVI) in Fukushima following the 2011 earthquake/tsunami/nuclear disaster. METHOD: Self-report questionnaires were administered to 49 male dentists six to nine months after the disaster. Psychological distress and posttraumatic stress were measured using the General Health Questionnaire-30 (GHQ-30) and Impact of Event Scale-Revised (IES-R), respectively. Independent variables included sociodemographic characteristics, participant disaster exposures, DVI-related exposures, and fear of radiation exposure during DVI. Hierarchical multiple regression analysis was performed to examine independent-dependent variable relations. RESULTS: Thirty-eight participants (77.6%) had examined ≥ 40 corpses, 20 (40.8%) reported ≥ 4 corpse-related exposures, and six (12.2%) reported ≥ 5 gruesome corpse exposures. Mean (SD) GHQ-30 and IES-R scores were 5.08 (5.31) and 9.90 (10.1), respectively. Higher levels of psychological distress were associated with younger age (adjusted ß = -0.29), extensive property loss (ß = 0.34), and anxiety for the future (ß = 0.33). Higher levels of posttraumatic stress were associated with extensive property loss (adjusted R2 = 17.7%, ß = 0.30). Neither outcome was associated with DVI-related exposures or fear of radiation exposure during DVI (p < 0.05). CONCLUSIONS: Dentists' psychological burden was associated with disaster, but not DVI, exposures.