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.

Low-temperature and elastic properties of the 1/1 Tsai-type quasicrystal approximant GdCd6investigated by ultrasonic measurements.

Low-temperature and elastic properties of the quasicrystal approximant GdCd6have been investigated by means of an ultrasonic measurement. Salient elastic anomalies were observed in the temperature and magnetic field dependence of the principal elastic constants, most probably being associated with the successive multiple magnetic phase transitions showing up at low temperatures. Based on the experimental data, a magnetic field vs temperature phase diagram was constructed. In a zero magnetic field, at least four phases appear to exist. However, the phase diagram both for the magnetic field applied forH//⟨100⟩andH//⟨110⟩is richer, and interestingly some phase boundaries correspond to the development of field-induced phases, leading to the intricate magnetic phase diagram possibly with multiple ordered phases. We discuss the elastic and electronic properties at low temperatures, and also the nature of the multiple magnetic phase transitions of GdCd6.

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.

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.

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.

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.

Mental status assessment of disaster relief personnel by vocal affect display based on voice emotion recognition.

BACKGROUND: Disaster relief personnel tend to be exposed to excessive stress, which can be a cause of mental disorders. To prevent from mental disorders, frequent assessment of mental status is important. This pilot study aimed to examine feasibility of stress assessment using vocal affect display (VAD) indices as calculated by our proposed algorithms in a situation of comparison between different durations of stay in stricken area as disaster relief operation, which is an environment highly likely to induce stress. METHODS: We used Sensibility Technology (ST) software to analyze VAD from voices of participants exposed to extreme stress for either long or short durations, and we proposed algorithms for indices of low VAD (VAD-L), high VAD (VAD-H), and VAD ratio (VAD-R), calculated from the intensity of emotions as measured by voice emotion analysis. As a preliminary validation, 12 members of Japan Self-Defense Forces dispatched overseas for long (3 months or more) or short (about a week) durations were asked to record their voices saying 11 phrases repeatedly across 6 days during their dispatch. RESULTS: In the validation, the two groups showed an inverse relationship in VAD-L and VAD-H, in that long durations in disaster zones resulted in higher values of both VAD-L and VAD-R, and lower values of VAD-H, compared with short durations. Interestingly, phrases produced varied results in terms of group differences and VAD indices, demonstrating the sensitivity of the ST. CONCLUSIONS: A comparison of the values obtained for the different groups of subjects clarified that there were tendencies of the VAD-L, VAD-H, and VAD-R indices observed for each group of participants. The results suggest the possibility of using ST software in the measurement of affective aspects related to mental health from vocal behavior.

Cloning of agarase gene from non-marine agarolytic bacterium Cellvibrio sp.

Agarase genes of non-marine agarolytic bacterium Cellvibrio sp. were cloned into Escherichia coli and one of the genes obtained using HindIII was sequenced. From nucleotide and putative amino acid sequences (713 aa, molecular mass; 78,771 Da) of the gene, designated as agarase AgaA, the gene was found to have closest homology to the Saccharophagus degradans (formerly, Microbulbifer degradans) 2-40 aga86 gene, belonging to glycoside hydrolase family 86 (GH86). The putative protein appears to be a non-secreted protein because of the absence of a signal sequence. The recombinant protein was purified with anion exchange and gel filtration columns after ammonium sulfate precipitation and the molecular mass (79 kDa) determined by SDS-PAGE and subsequent enzymography agreed with the estimated value, suggesting that the enzyme is monomeric. The optimal pH and temperature for enzymatic hydrolysis of agarose were 6.5 and 42.5 degrees C, and the enzyme was stable under 40 degrees C. LC-MS and NMR analyses revealed production of a neoagarobiose and a neoagarotetraose with a small amount of a neoagarohexaose during hydrolysis of agarose, indicating that the enzyme is a beta-agarase.

Esophagitis dissecans superficialis and autoimmune bullous dermatoses: A review.

Esophagitis dissecans superficialis (EDS) is a rare and severe endoscopic finding characterized by sloughing of large fragments of esophageal mucosal lining. Although EDS has been reported in association with serious illnesses and certain medications, the pathophysiological association of autoimmune bullous dermatoses with EDS has gained remarkable attention. Among these dermatoses, pemphigus vulgaris and pemphigoid frequently present with various types of esophageal involvement including EDS. We review the pathophysiology and clinical features of this involvement with the presentation of our experiences. The importance of endoscopic evaluation of this entity is discussed.