Papers - Mitsukura, Yasue

Analysis of Steroid-Induced Sleep Structure Changes in Mice

Gento Okawa, Yasue Mitsukura

 2022.03

Feature Extraction of Brain Activity in Prefrontal Region of Depressed Patients Using Bipolar Electroencephalography

Keita Kawashima, Yasue Mitsukura

 2022.03

Analysis of Pain Onset using Sustained Body Temperature

Kyoko Oka, Yasue Mitsukura

 2022.03

新型コロナウイルス感染症 (COVID-19) の重症化を早期に予測する重症化予測システムの確立

石井誠, 福永興壱, 満倉靖恵

大和証券ヘルス財団研究業績集 45   185 - 188 2022.03

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

Tsubota M., Sumali B., Kai M., Mitsukura Y.

Science Progress (Science Progress)  105 ( 1 ) 368504221080673 2022.02

ISSN  00368504

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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.

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Identification of sexually dimorphic effects of oxytocin administration on electroencephalography of B6J mice

Clarissa G. Gregory, Yasue Mitsukura

 2022.02

Prefrontal power asymmetry feature extraction for depression severity in a clinical study

Gengtao Lin, Yasue Mitsukura

 2022.02

Automatic Deceleration Detection System from Fetal Heart Rate obtained by CTG

Yamamoto Hiroko, Mitsukura Yasue

2022 IEEE 17TH INTERNATIONAL CONFERENCE ON ADVANCED MOTION CONTROL (AMC)    346 - 351 2022

ISSN  1943-6572

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Analysis of the Relationship Between Calcium Ion Concentration Ratio and Behavior in Neural Activity of the Brain

Sunami Ryota, Mitsukura Yasue

2022 IEEE 17TH INTERNATIONAL CONFERENCE ON ADVANCED MOTION CONTROL (AMC)    341 - 345 2022

ISSN  1943-6572

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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

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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

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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

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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

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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

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<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>

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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

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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

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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

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<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>

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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

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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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