Evaluation of a new portable corneal topography system for self-measurement using smartphones: a pilot study.

PURPOSE: Herein, we propose the use of the "KeraVio Ring", which is a portable, selfie-based, smartphone-attached corneal topography system that is based on the Placido ring videokeratoscope. The goal of this study was to evaluate and compare corneal parameters between KeraVio Ring and conventional corneal tomography images. METHODS: We designed the KeraVio Ring as a device comprising 3D-printed LED rings for generating Placido rings that can be attached to a smartphone. Two LED rings are attached to a cone-shaped device, and both corneas are illuminated. Selfies were taken using the KeraVio Ring attached to the smartphone without assistance from any of the examiners. Captured Placido rings on the cornea were analysed by intelligent software to calculate corneal parameters. Patients with normal, keratoconus, or LASIK-treated eyes were included. Anterior segment optical coherence tomography (AS-OCT) was also performed for each subject. RESULTS: We found highly significant correlations between the steepest and flattest keratometry, corneal astigmatism, and vector components obtained with the KeraVio Ring and AS-OCT. In subjects with normal, keratoconus, and LASIK-treated eyes, the mean difference in corneal astigmatism between the two devices was -0.8 ± 1.4 diopters (D) (95% limits of agreement (LoA), -3.6 to 2.0), -1.8 ± 3.7 D (95% LoA, -9.1 to 5.5), and -1.5 ± 1.3 D (95% LoA, -4.0 to 1.1), respectively. CONCLUSIONS: The experimental results showed that the corneal parameters obtained by the KeraVio Ring were correlated with those obtained with AS-OCT. The KeraVio Ring has the potential to address an unmet need by providing a tool for portable selfie-based corneal topography.

Contactless continuous heart rate monitoring system using ballistocardiography.

Cardiovascular disease is the number one cause of death in the world and is a serious problem. In the case of cardiopulmonary arrest due to myocardial infarction, the survival rate is as low as 13.3% one month after resuscitation, which birthed the need for continuous heart monitoring. In this study, we develop a Ballistocardiogram (BCG) measurement system using a load cell installed on a chair and a heart rate estimation algorithm that is robust to waveform changes, with the aim of constructing a non-contact heart rate acquisition system. The proposed system was evaluated by utilizing data obtained from 13 healthy subjects and 1 subject with abnormal ECG who were simultaneously measured with ECG. The output of the BCG system was confirmed to change with the same period as the ECG data obtained as the correct answer, and the synchronization of the R-peak positions was confirmed for all cases. As a result of comparing the heart rate intervals estimated from BCG and those obtained from ECG, it was confirmed that the same heart rate variability (HRV) features could be obtained even for abnormal ECG subject.

Frontotemporal EEG as potential biomarker for early MCI: a case-control study.

BACKGROUND: Previous studies using EEG (electroencephalography) as biomarker for dementia have attempted to research, but results have been inconsistent. Most of the studies have extremely small number of samples (average N = 15) and studies with large number of data do not have control group. We identified EEG features that may be biomarkers for dementia with 120 subjects (dementia 10, MCI 33, against control 77). METHODS: We recorded EEG from 120 patients with dementia as they stayed in relaxed state using a single-channel EEG device while conducting real-time noise reduction and compared them to healthy subjects. Differences in EEG between patients and controls, as well as differences in patients' severity, were examined using the ratio of power spectrum at each frequency. RESULTS: In comparing healthy controls and dementia patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals. CONCLUSIONS: A study with a larger sample size should be conducted to gauge reproducibility, but the results implied the effectiveness of EEG in clinical practice as a biomarker of MCI (mild cognitive impairment) and/or dementia.

A Study on Legume-Based Noodles as Staple Food for Office Workers.

This study aims to verify the effects of "legume-based noodles" as a staple food for lunch, specifically: blood glucose, cognitive function tests, Kansei value, work questionnaires, typing, and body weight. The experiment is divided into two groups: the intervention group (legumes-based noodle) and the control group (regular lunch). Both groups have similar menu except the staple food. The intervention group resulted in a statistically significant lower blood glucose area under the curve (AUC) and lower maximum blood glucose levels during the afternoon work hours on weekdays. In addition, the Kansei value "concentration" decreased at the end of the workday in the control group compared to before and after lunch but did not decrease in the intervention group. Furthermore, the number of typing accuracy was higher in the intervention group than in the control group, and the questionnaire responses for "work efficiency" and "motivation" were more positive. These results suggest that eating legume-based noodles may lead to improved performance of office workers.

Characteristics of single-channel electroencephalogram in depression during conversation with noise reduction technology.

BACKGROUND: Previous studies have attempted to characterize depression using electroencephalography (EEG), but results have been inconsistent. New noise reduction technology allows EEG acquisition during conversation. METHODS: We recorded EEG from 40 patients with depression as they engaged in conversation using a single-channel EEG device while conducting real-time noise reduction and compared them to those of 40 healthy subjects. Differences in EEG between patients and controls, as well as differences in patients' depression severity, were examined using the ratio of the power spectrum at each frequency. In addition, the effects of medications were examined in a similar way. RESULTS: In comparing healthy controls and depression patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In the patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals. In addition, significant differences were observed at multiple frequencies when comparing patients who did and did not take antidepressants, antipsychotics, and/or benzodiazepines. However, the power spectra still remained significantly different between non-medicated patients and healthy individuals. LIMITATIONS: The small sample size may have caused Type II error. Patients' demographic characteristics varied. Moreover, most patients were taking various medications, and cannot be compared to the non-medicated control group. CONCLUSION: A study with a larger sample size should be conducted to gauge reproducibility, but the methods used in this study could be useful in clinical practice as a biomarker of depression.

Online improvisation training, hybrid improvisation training and on-site improvisation training; are they the same?

BACKGROUND: Since the outbreak of COVID-19 in Wuhan in December 2019, lifestyle has been changing to an online-based learning and working environment rather than on-site, and improvisation training is no exception. However, no research compares the efficacy of online versus on-site training. Although we believed that the most effective way to learn improvisation is an on-site format, it is important to explore how format differences can affect learners. METHOD: We offer three types of training such as on-site training (n = 6) (Consisting of 1 female with age ≥40 and <50, and 5 males with ages ≥20 and <50), hybrid training (Instructor participates from online and learners participate on-site) (n = 120) (Consisting of 55 female with age ≥15 and <20, and 65 males with ages ≥15 and <50), and online training (n = 20) (Consisting of 4 female with age ≥20 and <30, and 16 males with ages ≥20 and <50) We collected pretest, test, and posttest data by using the Kansei Analyzer, a simplified electroencephalograph (EEG) and Profile of Mood States (POMS) questionnaire. RESULTS: All formats of training displayed an increase in vigor and a decrease in depression, confusion, tension, anger, and fatigue. The online training displayed better results than the on-site training. Regardless of the format, all training displayed an increase in stress during the activities and a decrease in stress after the activity without changes in other indexes. Additionally, on-site training displayed an increase in sleepiness and stress during the activities. Some participants were tested twice but no significant differences were found between the initial results and the secondary results. CONCLUSION: In this study, we found evidence that online improvisation can lead to the prevention of depressive symptoms and can function as a method for the reduction of stress in conjunction with the increase of individual vigor. However, a future study is required due to the low number of participants and the absence of POMS data for the on-site training. Any future studies should account for these factors while examining other data such as blood pressure, blood sugar, and pulse.

Finding of the factors affecting the severity of COVID-19 based on mathematical models.

Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.

Evaluation of olive oil effects on human stress response by measuring cerebral blood flow.

In this paper, we evaluated the effects of olive oil on human's stress level. In recent years, mental stress from harsh working environment have been causing serious problems to human health, both mentally and physically. Symptoms of stress may include feelings of worthlessness, agitation, anxiety, lethargy, insomnia, and behavioral changes. Additionally, the harsh working environments may cause the workers to adopt unhealthy dietary habits, contributing to the health issue. On the other hand, olive oil has been known to provide stress-relieving effects both by ingestion and by inhaling the scent. Here, we examined the effects of extravirgin olive oil ingestion for mitigating stress from deskwork. Three best-selling extravirgin olive oil in Japan were tested, and typing task was selected to emulate deskwork situation. Near-infrared spectroscopy (NIRS) is utilized in this study to visualize the response in brain via cerebral blood flow analysis and to measure participants' stress level. Statistical analysis showed that the stress levels were lower during the olive oil ingestion experiment compared to no-oil experiment, even when measured one hour after the ingestion.

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors.

Ballistocardiogram (BCG) is a graphical representation of the subtle oscillations in body movements caused by cardiovascular activity. Although BCGs cause less burden to the user, electrocardiograms (ECGs) are still commonly used in the clinical scene due to BCG sensors' noise sensitivity. In this paper, a robust method for sleep time BCG measurement and a mathematical model for predicting sleep stages using BCG are described. The novel BCG measurement algorithm can be described in three steps: preprocessing, creation of heartbeat signal template, and template matching for heart rate variability detection. The effectiveness of this algorithm was validated with 99 datasets from 36 subjects, with photoplethysmography (PPG) to compute ground truth heart rate variability (HRV). On average, 86.9% of the inter-beat intervals were detected and the mean error was 8.5ms. This shows that our method successfully extracted beat-to-beat intervals from BCG during sleep, making its usability comparable to those of clinical ECGs. Consequently, compared to other conventional BCG systems, even more accurate sleep heart rate monitoring with a smaller burden to the patient is available. Moreover, the accuracy of the sleep stages mathematical model, validated with 100 datasets from 25 subjects, is 80%, which is higher than conventional five-stage sleep classification algorithms (max: 69%). Although, in this paper, we applied the mathematical model to heart rate interval features from BCG, theoretically, this sleep stage prediction algorithm can also be applied to ECG-extracted heart rate intervals.

The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology.

INTRODUCTION: Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep. METHODS: Major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms. DISCUSSION: The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity. TRIAL REGISTRATION: UMIN000021396, University Hospital Medical Information Network (UMIN).

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients.

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one's cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.

Evaluating the severity of depressive symptoms using upper body motion captured by RGB-depth sensors and machine learning in a clinical interview setting: A preliminary study.

BACKGROUND: Mood disorders have long been known to affect motor function. While methods to objectively assess such symptoms have been used in experiments, those same methods have not yet been applied in clinical practice because the methods are time-consuming, labor-intensive, or invasive. METHODS: We videotaped the upper body of each subject using a Red-Green-Blue-Depth (RGB-D) sensor during a clinical interview setting. We then examined the relationship between depressive symptoms and body motion by comparing the head motion of patients with major depressive disorders (MDD) and bipolar disorders (BD) to the motion of healthy controls (HC). Furthermore, we attempted to predict the severity of depressive symptoms by using machine learning. RESULTS: A total of 47 participants (HC, n = 16; MDD, n = 17; BD, n = 14) participated in the study, contributing to 144 data sets. It was found that patients with depression move significantly slower compared to HC in the 5th percentile and 50th percentile of motion speed. In addition, Hamilton Depression Rating Scale (HAMD)-17 scores correlated with 5th percentile, 50th percentile, and mean speed of motion. Moreover, using machine learning, the presence and/or severity of depressive symptoms based on HAMD-17 scores were distinguished by a kappa coefficient of 0.37 to 0.43. LIMITATIONS: Limitations include the small number of subjects, especially the number of severe cases and young people. CONCLUSIONS: The RGB-D sensor captured some differences in upper body motion between depressed patients and controls. If much larger samples are accumulated, machine learning may be useful in identifying objective measures for depression in the future.