Details of a Researcher - Mitsukura, Yasue

Papers - Mitsukura, Yasue

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Noise-Induced Stress Assessment ─On the Difference between Questionnaire-Based and EEG Measurement-Based Evaluations─

Susumu Hara, Yasue Mitsukura, Hiroko Kamide

Technical Journal of Advanced Mobility ( Japan UAS Industrial Development Association) Vol. 3 ( Issue 6 ) 81 - 90 2022

In recent years, flying car (urban air mobility, UAM) technologies have been actively studied in Japan and other countries. However, the social acceptability of flying vehicles such as UAM and drones (unmanned aircraft systems, UAS) has not been extensively investigated. Social issues often arise when social acceptance is neglected. Thus, novel issues are expected to arise on the advent of the so-called Aerial Industrial Revolution because of the industrial use of drones and commercialization of flying cars. To overcome these issues, herein, the social acceptability of this revolution was evaluated by two different approaches: sociopsychological evaluation using a questionnaire and real-time evaluation by electroencephalograph (EEG) measurement using a Kansei analyzer. The authors experimentally evaluated the applicability of the Kansei analyzer for various sounds including stationary rotor sound and nonstationary metallic sound in the previous study. Different from such an experiment, this study conducted experiments based on the industrial drone sound with four levels of volume. Subsequently, the difference between the stress levels was investigated using the questionnaire and the Kansei analyzer. The result may impact the formulation of the social acceptability survey methods in future.

Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning

Kimura A., Mitsukura Y., Oya A., Matsumoto M., Nakamura M., Kanaji A., Miyamoto T.

Scientific Reports (Scientific Reports) 11 ( 1 ) 3192 2021.12

Accepted, ISSN 2045-2322

Pain is an undesirable sensory experience that can induce depression and limit individuals’ activities of daily living, in turn negatively impacting the labor force. Affected people frequently feel pain during activity; however, pain is subjective and difficult to judge objectively, particularly during activity. Here, we developed a system to objectively judge pain levels in walking subjects by recording their quantitative electroencephalography (qEEG) and analyzing data by machine learning. To do so, we enrolled 23 patients who had undergone total hip replacement for pain, and recorded their qEEG during a five-minute walk via a wearable device with a single electrode placed over the Fp1 region, based on the 10–20 Electrode Placement System, before and three months after surgery. We also assessed subject hip pain using a numerical rating scale. Brain wave amplitude differed significantly among subjects with different levels of hip pain at frequencies ranging from 1 to 35 Hz. qEEG data were also analyzed by a support vector machine using the Radial Basis Functional Kernel, a function used in machine learning. That approach showed that an individual’s hip pain during walking can be recognized and subdivided into pain quartiles with 79.6% recognition Accuracy. Overall, we have devised an objective and non-invasive tool to monitor an individual’s pain during walking.

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Deep neural network-based classification of cardiotocograms outperformed conventional algorithms

Ogasawara J., Ikenoue S., Yamamoto H., Sato M., Kasuga Y., Mitsukura Y., Ikegaya Y., Yasui M., Tanaka M., Ochiai D.

Scientific Reports (Scientific Reports) 11 ( 1 ) 13367 2021.12

Accepted, ISSN 2045-2322

Cardiotocography records fetal heart rates and their temporal relationship to uterine contractions. To identify high risk fetuses, obstetricians inspect cardiotocograms (CTGs) by eye. Therefore, CTG traces are often interpreted differently among obstetricians, resulting in inappropriate interventions. However, few studies have focused on quantitative and nonbiased algorithms for CTG evaluation. In this study, we propose a newly constructed deep neural network model (CTG-net) to detect compromised fetal status. CTG-net consists of three convolutional layers that extract temporal patterns and interrelationships between fetal heart rate and uterine contraction signals. We aimed to classify the abnormal group (umbilical artery pH < 7.20 or Apgar score at 1 min < 7) and the normal group from CTG data. We evaluated the performance of the CTG-net with the F1 score and compared it with conventional algorithms, namely, support vector machine and k-means clustering, and another deep neural network model, long short-term memory. CTG-net showed the area under the receiver operating characteristic curve of 0.73 ± 0.04, which was significantly higher than that of long short-term memory. CTG-net, a quantitative and automated diagnostic aid system, enables early intervention for putatively abnormal fetuses, resulting in a reduction in the number of cases of hypoxic injury.

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

Qu J., Sumali B., Lee H., Terai H., Ishii M., Fukunaga K., Mitsukura Y., Nishimura T.

Scientific Reports (Scientific Reports) 11 ( 1 ) 24224 2021.12

ISSN 2045-2322

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.

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Relationship between Hormone Level Fluctuation and EEG

Kosugi Wataru, Brian Sumali, Mimura Masaru, Mitsukura Yasue

IEEJ Transactions on Electronics, Information and Systems (The Institute of Electrical Engineers of Japan) 141 ( 9 ) 992 - 998 2021.09

Accepted, ISSN 03854221

<p>In this research, we aim to extract features of menstrual cycle and mood using features of electroencephalogram (EEG). The menstrual cycle is classified into four categories: luteal phase, menstrual phase, follicular phase, and ovulation phase. We extract characteristic EEG of these periods. In addition, this study examines the extent of the difference in emotional state at the four stages of the menstrual cycle and quantitatively shows the period during which mental disorders are likely to occur.</p>

住宅の断熱性能による温熱環境の違いが生理学的反応・在宅作業成績に及ぼす影響に関する被験者実験

河本紗弥, 伊香賀俊治, 満倉靖恵, 古溝洋明, 吉田吏志, 池田知之, 明内勝裕

空気調和・衛生工学会大会　学術講演論文集 6 2021.09

循環器疾患やCOVID-19重症化に繋がるとされる高血圧の予防が急務となっている。寒冷環境に曝されると血圧が上昇し、末梢の血行も悪化する。また、COVID-19感染拡大防止のため在宅勤務を行う人が増加しており、住宅における知的生産性の向上が求められている。そこで、本研究では断熱性能の異なるモデルルームで被験者実験を行い、住宅の断熱性能による温熱環境の違いが生理学的反応・在宅勤務を模擬した作業成績に及ぼす影響の検討を行う。

THE EFFECT OF INTERVENTION OF MEDICAL STAFF ON THE ADHERENCE OF CPAP AND HEART RATE IN PATIENTS WITH OBSTRUCTIVE SLEEP APNEA

Shirahama Ryutaro, Ishimaru Rie, Takagiwa Jun, Mitsukura Yasue

SLEEP 44 A182 - A183 2021.05

ISSN 0161-8105

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

Mitsukura Y., Sumali B., Nara R., Watanabe K., Inoue M., Ishida K., Nishiwaki M., Mimura M.

Food Science and Nutrition (Food Science and Nutrition) 9 ( 4 ) 1851 - 1859 2021.04

Accepted, ISSN 2048-7177

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.

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Establishing Robust Feature Point Detection and Tracking Methods for Face Orientation

Sumali Brian, Hamada Nozomu, Mitsukura Yasue

IEEJ Transactions on Electronics, Information and Systems (The Institute of Electrical Engineers of Japan) 141 ( 3 ) 367 - 372 2021.03

Accepted, ISSN 03854221

<p>Recently, there has been an increasing need of a face alignment or facial feature point tracking for applying face recognition, facial expression estimation, face attributes prediction, and clinical face observation etc. This study proposes a robust facial feature tracking method against camera shake and face orientation changes. The method applies a cascaded composed learning (CCL) based facial feature point tracking method by incorporating optical flow for improving tracking accuracy and robustness. Experiments are conducted to show that efficient tracking is achieved by performing CCL combined with initial shape estimation via the optical flow.</p>

住宅の断熱性能が血圧・血流・皮膚温に及ぼす影響に関する被験者実験

河本紗弥，伊香賀俊治，満倉靖恵，古溝洋明，吉田吏志，池田知之，明内勝裕

日本建築学会関東支部研究報告集 (日本建築学会) ( 91 ) 79 - 82 2021.03

Robust Heartbeat Interval Estimation Method against Various Postures on Bed Using Contactless Measurement

Sato T., Nakaigawa T., Hamada N., Mitsukura Y.

2021 IEEE/SICE International Symposium on System Integration, SII 2021 (2021 IEEE/SICE International Symposium on System Integration, SII 2021) 524 - 530 2021.01

Accepted, ISSN 9781728176581

The purpose of this thesis is to use contactless measurement, to propose a robust system for heartbeats interval estimation that enables easy and stable health monitoring in the daily life. In recent years, cardiovascular disease is a major cause of death. Ballisto cardiogram (BCG), which records the mechanical activity of the heart, has been studied as a method provided an unobtrusive measurement. This technique provides the possibility of observing health status without causing any discomfort however, the signal quality can highly vary due to artifacts associated with breathing or postures of a user. Therefore, this study assesses the robust algorithm to estimate heartbeats interval from BCG signal measured by high-sensitive load sensors that mounted to bed legs. Three healthy subjects participated in the experiments that they lied on the bed with various postures. As a result, mean beat-to-beat interval errors were less than about 50 ms when subjects held in decubitus.

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Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain-Computer Interface

Ogino M., Kanoga S., Ito S.I., Mitsukura Y.

IEEE Access (IEEE Access) 9 47008 - 47023 2021

Accepted, ISSN 2169-3536

A brain-computer interface (BCI) is a communication tool that analyzes neural activity and relays the translated commands to carry out actions. In recent years, semi-supervised learning (SSL) has attracted attention for visual event-related potential (ERP)-based BCIs and motor-imagery BCIs as an effective technique that can adapt to the variations in patterns among subjects and trials. The applications of the SSL techniques are expected to improve the performance of auditory ERP-based BCIs as well. However, there is no conclusive evidence supporting the positive effect of SSL techniques on auditory ERP-based BCIs. If the positive effect could be verified, it will be helpful for the BCI community. In this study, we assessed the effects of SSL techniques on two public auditory BCI datasets - AMUSE and PASS2D - using the following machine learning algorithms: step-wise linear discriminant analysis, shrinkage linear discriminant analysis, spatial temporal discriminant analysis, and least-squares support vector machine. These backbone classifiers were firstly trained by labeled data and incrementally updated by unlabeled data in every trial of testing data based on SSL approach. Although a few data of the datasets were negatively affected, most data were apparently improved by SSL in all cases. The overall accuracy was logarithmically increased with every additional unlabeled data. This study supports the positive effect of SSL techniques and encourages future researchers to apply them to auditory ERP-based BCIs.

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Applicability of Kansei Analyzer for Noise-Induced Stress Assessment ─Toward Social Acceptability Survey of Flying Cars─

Hara Susumu, Hayashi Yusuke, Mitsukura Yasue, Kamide Hiroko

Technical Journal of Advanced Mobility (Japan UAS Industrial Development Association) 2 ( 3 ) 31 - 41 2021

Research paper (other academic), Joint Work, Accepted

In recent years, flying car (Urban Air Mobility, UAM) technologies have been actively studied in Japan and other countries. However, the social acceptability of flying vehicles such as UAM and drones (Unmanned Aircraft Systems, UAS) has not been extensively investigated. Social issues often arise when social acceptance is neglected. Thus, novel issues are expected to arise upon the advent of the so-called Aerial Industrial Revolution because of the industrial use of drones and the commercialization of flying cars. To overcome these issues, herein, the social acceptability of this revolution may be evaluated by two different approaches: real-time evaluation by electroencephalograph (EEG) measurement using a Kansei analyzer and social-psychological evaluation by using a questionnaire. However, at this stage, the applicability of the Kansei analyzer for the evaluation has not yet been fully addressed. Thus, in this study, an experiment was conducted to verify the applicability of the Kansei analyzer. In this experiment, subjects answered a questionnaire after hearing some noises. The effects of four types of sounds and three levels of volume on the noise-induced stress levels were investigated. This study described the results and consideration about the experiment to evaluate noise-induced stress among participants and confirmed that the Kansei analyzer is applicable for such stress detection. Moreover, the differences of results in both approaches were revealed.

Predicting COVID-19 Severe Patients and Evaluation Method of 3 Stages Severe Level by Machine Learning

Qu J., Sumali B., Mitsukura Y.

2021 IEEE 4th International Conference on Electronics and Communication Engineering, ICECE 2021 (2021 IEEE 4th International Conference on Electronics and Communication Engineering, ICECE 2021) 277 - 281 2021

Since the outbreak COVID-19 in Wuhan, China in December 2019, a large number of patients have been seen worldwide, and the number of infections continues to show an increasing trend. The vast majority of COVID-19 patients will have fever, headache, and mild respiratory symptoms, but a small number of severely ill patients will experience respiratory distress and related complications, which seriously endanger their lives. The large number of patients also puts the healthcare system to the test. To maximize the protection of patients' lives and the effective use of medical resources, this study collected blood data from 313 patients by machine learning, used 7 blood test items as the feature quantity, established an effective linear SVM prediction model for severe/non-severe disease (recall: 93.55%, specificity: 93.22%), and for 3 stages evaluation of the degree of severe level in severe patients was developed for patients with critical illness. The abnormal increase in Ferritin values was also found to be closely related to the development of severity.

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騒音に対するストレス評価のための感性アナライザ適用可能性の検討

原進, 林裕介, 満倉靖恵, 上出寛子

ロボティクス・メカトロニクス講演会講演概要集 2021

In recent years, flying cars have been actively researched both in Japan and abroad. However, little research has been carried out on the social acceptance of flying vehicles. Social problems often arise when neglecting the social acceptance. It is expected that problems will arise in the future when “Aerial Industrial Revolution” occurs due to industrial use of drones and commercialization of flying cars. In this study, the social acceptance is evaluated by two approaches. One is real-time evaluation by Kansei-analyzer and the other is social psychological evaluation by questionnaire. But the applicability of Kansei-analyzer to the evaluation has not been fully addressed yet. Then this study conducts two kinds of experiments to clarify the applicability of Kansei-analyzer. This paper describes the results and consideration about the experiments to evaluate stress on noise.

Applicability of Kansei analyzer for noise-induced stress assessment─ toward social acceptability survey of flying cars─

Susumu Hara, Yusuke Hayashi, Yasue Mitsukura, Hiroko Kamide

Technical Journal of Advanced Mobility (Japan UAS Industrial Development Association) Vol. 2 ( Issue 3 ) 31 - 41 2021

【感覚の可視化】「脳」を見える化する　感情の可視化と脳を用いた研究　脳機能および心電計測を用いた定量評価によるストレスに対する脂質摂取の影響評価

満倉靖恵

オレオサイエンス ((公社)日本油化学会) 20 ( 11 ) 499 - 505 2020.11

Accepted, ISSN 1345-8949

A mobile auditory brain-computer interface system with sequential auditory feedback

Ogino M., Mitsukura Y.

2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020 (2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020) 590 - 593 2020.10

Accepted, ISSN 9781728198026

Auditory brain-computer interface (BCI) is a necessary communication tool for disabled people who can hardly communicate. However, general EEG systems are difficult to be handled. Moreover, the accuracy of the auditory BCI is less than a visual BCI because auditory stimuli cannot be presented rapidly, so the users can hardly feel to control the BCI. In this paper, we propose a comfortable BCI using a headband type device and an auditory feedback. The mobile system is more comfortable, and the auditory feedback improved the accuracy. Moreover, it enables users understand how to control the auditory BCI more easily.

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Sleep stage estimation from bed leg ballistocardiogram sensors

Mitsukura Y., Sumali B., Nagura M., Fukunaga K., Yasui M.

Sensors (Switzerland) (Sensors (Switzerland)) 20 ( 19 ) 1 - 21 2020.10

Accepted, ISSN 14248220

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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.

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

Kishimoto T., Takamiya A., Liang K.c., Funaki K., Fujita T., Kitazawa M., Yoshimura M., Tazawa Y., Horigome T., Eguchi Y., Kikuchi T., Tomita M., Bun S., Murakami J., Sumali B., Warnita T., Kishi A., Yotsui M., Toyoshiba H., Mitsukura Y., Shinoda K., Sakakibara Y., Mimura M.

Contemporary Clinical Trials Communications (Contemporary Clinical Trials Communications) 19 100649 2020.09

Accepted, ISSN 24518654

© 2020 The Authors 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).

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