Updated on 2024/03/19

写真a

 
FUJIWARA Koichi
 
Organization
Graduate School of Engineering Materials Process Engineering 1 Associate professor
Graduate School
Graduate School of Engineering
Undergraduate School
School of Engineering Materials Science and Engineering
Title
Associate professor

Degree 1

  1. 博士(工学) ( 2009.3   京都大学 ) 

Research Interests 9

  1. 医療AI

  2. 循環器内科学

  3. Epileptology

  4. Biomedical Engineering

  5. Process Systems Engineering

  6. Sleep Medicine

  7. Biosignal Processing

  8. Machine Learning

  9. 生体計測

Research Areas 4

  1. Informatics / Life, health and medical informatics

  2. Life Science / Medical systems

  3. Informatics / Statistical science

  4. Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Chemical reaction and process system engineering

Current Research Project and SDGs 6

  1. てんかん発作予知AIの開発

  2. 認知症の早期診断

  3. 睡眠障害の診断

  4. 熱中症アラームの開発

  5. 生産プロセスの異常検知・診断AIの開発

  6. 心不全の早期検知

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Research History 8

  1. Japan Agency for Medical Research and Development   PO

    2022.7

  2. Nagoya University   Department of Material Process Engineering   Associate professor

    2018.11

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    Country:Japan

  3. JST

    2018.10

  4. Kyoto University   Department of Systems Science   Assistant Professor

    2012.7 - 2018.11

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    Country:Japan

  5. NTT Communication Science Laboratories

    2010.4 - 2012.6

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    Country:Japan

  6. Research Fellowships for Young Scientists   PD

    2009.4 - 2010.3

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    Country:Japan

  7. Research Fellowships for Young Scientists   DC2

    2008.4 - 2009.3

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    Country:Japan

  8. Toyota Motor Corporation

    2006.4 - 2007.3

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    Country:Japan

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Education 3

  1. Kyoto University   Graduate School, Division of Engineering   Department of Chemical Engineering

    2007.4 - 2009.3

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    Country: Japan

  2. Kyoto University   Graduate School, Division of Engineering   Department of Chemical Engineering

    2004.4 - 2006.3

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    Country: Japan

  3. Kyoto University   Faculty of Engineering

    2000.4 - 2004.3

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    Country: Japan

Professional Memberships 9

  1. IEEE

  2. THE JAPANESE SOCIETY FOR ARTIFICIAL INTELLIGENCE

  3. THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS

  4. JAPAN EPILEPSY SOCIETY

  5. THE JAPANESE SOCIETY OF SLEEP RESEARCH

  6. THE SOCIETY OF CHEMICAL ENGINEERS, JAPAN

  7. APSIPA

  8. 日本機械学会

  9. APSIPA

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Committee Memberships 7

  1. IEC/TC62   国際エキスパート  

    2023.11   

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    Committee type:Other

  2. 日本睡眠学会   評議員  

    2023.7   

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    Committee type:Academic society

  3.   APSIPA BioSips Technical Committee Chair  

    2021.1 - 2022.12   

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    Committee type:Academic society

  4. 日本睡眠学会   若手の会幹事  

    2017.1   

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    Committee type:Academic society

  5. APSIPA BioSips   Technical Committee  

    2013.10   

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    Committee type:Academic society

  6. 日本学術振興会   第143委員会  

    2012.10   

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    Committee type:Academic society

  7. APSIPA Transactions on Signal and Information Processing   Associate Editor  

       

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    Committee type:Academic society

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Awards 20

  1. 著述賞

    2022.9   計測自動制御学会  

  2. テレコムシステム技術賞

    2021.3   電気通信普及財団  

  3. 全国大会優秀賞

    2018.11   人工知能学会  

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    Award type:Award from Japanese society, conference, symposium, etc. 

  4. 論文賞

    2017.9   計測自動制御学会  

    藤原 幸一

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    Award type:Honored in official journal of a scientific society, scientific journal 

  5. 市村学術賞 功績賞

    2017.4   新技術開発財団  

    藤原 幸一

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    Award type:International academic award (Japan or overseas) 

  6. 支部賞

    2021.1   計測自動制御学会 中部支部  

  7. 学術年会 優秀研究発表賞

    2020.6   日本毒性学会  

  8. システム・情報部門 学術講演会 2018 優秀論文賞

    2018.11   計測自動制御学会  

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  9. システム・情報部門 学術講演会 2018 優秀発表賞

    2018.11   計測自動制御学会  

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  10. システム・情報部門 学術講演会 2018 優秀論文賞

    2018.11   計測自動制御学会  

    藤原 幸一

  11. システム・情報部門 学術講演会 2018 優秀発表賞

    2018.11   計測自動制御学会  

    藤原 幸一

  12. BRAVE 2017 Winter 優秀賞

    2017.12   Beyond Next Ventures  

    藤原 幸一

  13. システム・情報部門 学術講演会 2017 最優秀論文賞

    2017.11   計測自動制御学会  

    藤原 幸一

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    Award type:Award from Japanese society, conference, symposium, etc. 

  14. バイオテックグランプリ・サントリー賞

    2017.9   リバネス  

    藤原 幸一

  15. 技術賞

    2016.9   計測自動制御学会  

    藤原 幸一

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    Award type:Honored in official journal of a scientific society, scientific journal 

  16. システム・情報部門 学術講演会 2015 優秀論文賞

    2015.11   計測自動制御学会  

    藤原 幸一

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    Award type:Award from Japanese society, conference, symposium, etc. 

  17. 第1回制御部門マルチシンポジウム 部門大会賞

    2015.3   計測自動制御学会  

    藤原 幸一

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    Award type:Award from Japanese society, conference, symposium, etc. 

  18. システム・情報部門 学術講演会 2014 奨励賞

    2014.11   計測自動制御学会  

    藤原 幸一

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    Award type:Award from Japanese society, conference, symposium, etc. 

  19. システム・情報部門 学術講演会 2014 優秀論文賞

    2014.11   計測自動制御学会  

    藤原 幸一

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    Award type:Award from Japanese society, conference, symposium, etc. 

  20. 奨励賞

    2012.10   計測自動制御学会関西支部  

    藤原 幸一

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Papers 95

  1. Heat Illness Detection with Heart Rate Variability Analysis and Anomaly Detection Algorithm Reviewed

    K. Fujiwara, K. Ota, S. Saeda, T. Yamakawa, T. Kubo, A. Yamamoto, Y. Maruno, M. Kano

    Biomedical Signal Processing and Control     2023.9

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: https://doi.org/10.1016/j.bspc.2023.105520

  2. Driver Drowsiness Detection Using R-R Interval of Electrocardiogram and Self-Attention Autoencoder Reviewed

    Koichi Fujiwara, Hiroki Iwamoto, Kentaro Hori, Manabu Kano

    IEEE Transactions on Intelligent Vehicles     page: 1 - 10   2023.8

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Institute of Electrical and Electronics Engineers (IEEE)  

    DOI: 10.1109/tiv.2023.3308575

  3. Auditory Feedback of False Heart Rate for Video Game Experience Improvement Reviewed International journal

    Sayaka Ogawa, Koichi Fujiwara, Manabu Kano

    IEEE Transactions on Affective Computing   Vol. 14 ( 1 ) page: 487 - 497   2023.1

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Institute of Electrical and Electronics Engineers (IEEE)  

    Changes in emotions affect our physiological responses, and perhaps vice versa. We investigate a new game interaction system that uses false heart rate (fHR) feedback to improve the player experience (PX). The fHR feedback presents false HR information to players so that they perceive changes in the presented HR as being a result of alteration in PX. We introduced auditory fHR feedback into game interaction and investigated its effects through an experiment. Participants repeated gameplay of an action game while hearing heartbeat-like sounds and answered questionnaires regarding PX. Some participants heard the heartbeat-like sounds synchronized with their actual HR, whereas others heard the heartbeat-like sounds whose tempo became gradually faster or slower than their actual HR. The results indicated that an accelerating fHR feedback pattern with +5 bpm/min was appropriate for improving PX; participants were able to maintain their motivation to continue the game. The experiment also indicated that it is necessary for participants to perceive the presented heartbeat-like sounds as reflecting their actual HR. Participants did not maintain their motivation when they were told that the presented sounds were not correlated with their actual HR. The present work provides new principles for video game interaction design based on physiological measurements.

    DOI: 10.1109/taffc.2020.3039874

    Web of Science

    Scopus

  4. 生体信号を活用した医療AI~てんかん発作予知と睡眠紡錘波検出 Reviewed

    藤原 幸一

    生体医工学   Vol. Annual61 ( Abstract ) page: 137_2 - 137_2   2023

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    Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:公益社団法人 日本生体医工学会  

    <p>医療・ヘルスケア分野での機械学習・AI技術の活用は,他の分野と同様,深層学習の登場を契機に進展してきた.しかし,実際にはCT画像やMRI画像などに対する画像診断の事例が大半であり,それに比して心電図や脳波など生体信号への活用は取り残されてきた感がある.これには,(1) 対象信号の時空間パターンが複雑・非定常的で,対象現象の表現が特定困難であること,(2) サンプルとして取得できる生体信号の量が限定的であること,(3) 一定量の計測データが得られたとしても,しばしば対象とする現象の発生頻度が低く,強い不均衡となること,(4) 様々なアーチファクトが混入すること,(5) 明確なアーチファクトを取り除けた場合においてでもなお残存するノイズによって生体信号の信号対雑音比は高くないこと,(6) 入力となる生体信号がしばしば高次元となること,(7) 個人差が強く対象者間での汎化が難しいこと,など機械学習における様々な課題が凝縮されたようなケースとなっていることによると考えられる.このような状況に対して,我々はウェアラブルセンサや専用アプリを開発し,データ取得の自動化・効率化を目指すなど,独自の取り組みを通じて生体信号を活用した医療AI開発の垣根を下げる努力をしてきた.本講演では,我々まてんかん発作予知AIおよび睡眠紡錘波検出AI開発の実例を通じて,生体信号を活用した医療AI開発について紹介する.</p>

    DOI: 10.11239/jsmbe.annual61.137_2

    CiNii Research

  5. Transoral Robotic Surgery for Laryngopharyngeal Cancer

    Sano D., Shimizu A., Tateya I., Fujiwara K., Kishimoto Y., Maruo T., Fujimoto Y., Tsukahara K., Mori T., Kato H., Oridate N.

    Nihon Kikan Shokudoka Gakkai Kaiho   Vol. 73 ( 2 ) page: 98 - 101   2022.4

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    Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:The Japan Broncho-esophagological Society  

    DOI: 10.2468/jbes.73.98

    CiNii Research

  6. R-R interval-based sleep apnea screening by a recurrent neural network in a large clinical polysomnography dataset Reviewed

    Ayako Iwasaki, Koichi Fujiwara, Chikao Nakayama, Yukiyoshi Sumi, Manabu Kano, Tetsuharu Nagamoto, Hiroshi Kadotani

    Clinical Neurophysiology   Vol. 139   page: 80 - 89   2022.4

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Elsevier BV  

    DOI: 10.1016/j.clinph.2022.04.012

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    PubMed

  7. Medical checkup data analysis method based on LiNGAM and its application to nonalcoholic fatty liver disease Reviewed International journal

    Tsuyoshi Uchida, Koichi Fujiwara, Kenichi Nishioji, Masao Kobayashi, Manabu Kano, Yuya Seko, Kanji Yamaguchi, Yoshito Itoh, Hiroshi Kadotani

    Artificial Intelligence in Medicine   Vol. 128   page: 102310 - 102310   2022.4

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Elsevier BV  

    Although medical checkup data would be useful for identifying unknown factors of disease progression, a causal relationship between checkup items should be taken into account for precise analysis. Missing values in medical checkup data must be appropriately imputed because checkup items vary from person to person, and items that have not been tested include missing values. In addition, the patients with target diseases or disorders are small in comparison with the total number of persons recorded in the data, which means medical checkup data is an imbalanced data analysis. We propose a new method for analyzing the causal relationship in medical checkup data to discover disease progression factors based on a linear non-Gaussian acyclic model (LiNGAM), a machine learning technique for causal inference. In the proposed method, specific regression coefficients calculated through LiNGAM were compared to estimate the causal strength of the checkup items on disease progression, which is referred to as LiNGAM-beta. We also propose an analysis framework consisting of LiNGAM-beta, collaborative filtering (CF), and a sampling approach for causal inference of medical checkup data. CF and the sampling approach are useful for missing value imputation and balancing of the data distribution. We applied the proposed analysis framework to medical checkup data for identifying factors of Nonalcoholic fatty liver disease (NAFLD) development. The checkup items related to metabolic syndrome and age showed high causal effects on NAFLD severity. The level of blood urea nitrogen (BUN) would have a negative effect on NAFLD severity. Snoring frequency, which is associated with obstructive sleep apnea, affected NAFLD severity, particularly in the male group. Sleep duration also affected NAFLD severity in persons over fifty years old. These analysis results are consistent with previous reports about the causes of NAFLD; for example, NAFLD and metabolic syndrome are mutual and bi-directionally related, and BUN has a negative effect on NAFLD progression. Thus, our analysis result is plausible. The proposed analysis framework including LiNGAM-beta can be applied to various medical checkup data and will contribute to discovering unknown disease factors.

    DOI: 10.1016/j.artmed.2022.102310

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    PubMed

  8. Process Fault Diagnosis Method Based on MSPC and LiNGAM and its Application to Tennessee Eastman Process Reviewed

    Uchida, Y; Fujiwara, K; Saito, T; Osaka, T

    IFAC PAPERSONLINE   Vol. 55 ( 2 ) page: 384 - 389   2022

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.ifacol.2022.04.224

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  9. 埋込サイボーグ技術の社会実装に係る技術・社会的課題 Reviewed

    藤原 幸一, 藤田 卓仙, 山川 俊貴, 久保 孝富, 日永田 智絵, 桐山 瑶子, 川島 浩誉, 川治 徹真, 野田 隼人, 田畑 淳

    人工知能   Vol. 36 ( 6 ) page: 674 - 683   2021.11

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    Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:一般社団法人 人工知能学会  

    DOI: 10.11517/jjsai.36.6_674

    CiNii Research

  10. Prediction of GABA receptor antagonist-induced convulsion in cynomolgus monkeys by combining machine learning and heart rate variability analysis Reviewed

    Nagata, S; Fujiwara, K; Kuga, K; Ozaki, H

    JOURNAL OF PHARMACOLOGICAL AND TOXICOLOGICAL METHODS   Vol. 112   page: 107127   2021.11

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.vascn.2021.107127

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  11. Screening of sleep apnea based on heart rate variability and long short-term memory Reviewed International journal

    Ayako Iwasaki, Chikao Nakayama, Koichi Fujiwara, Yukiyoshi Sumi, Masahiro Matsuo, Manabu Kano, Hiroshi Kadotani

    Sleep and Breathing   Vol. 25 ( 4 ) page: 1821 - 1829   2021.1

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    <title>Abstract</title><sec>
    <title>Purpose</title>
    Sleep apnea syndrome (SAS) is a prevalent sleep disorder in which apnea and hypopnea occur frequently during sleep and result in increase of the risk of lifestyle-related disease development as well as daytime sleepiness. Although SAS is a common sleep disorder, most patients remain undiagnosed because the gold standard test polysomnography (PSG), is high-cost and unavailable in many hospitals. Thus, an SAS screening system that can be used easily at home is needed.


    </sec><sec>
    <title>Methods</title>
    Apnea during sleep affects changes in the autonomic nervous function, which causes fluctuation of the heart rate. In this study, we propose a new SAS screening method that combines heart rate measurement and long short-term memory (LSTM) which is a type of recurrent neural network (RNN). We analyzed the data of intervals between adjacent R waves (R-R interval; RRI) on the electrocardiogram (ECG) records, and used an LSTM model whose inputs are the RRI data is trained to discriminate the respiratory condition during sleep.


    </sec><sec>
    <title>Results</title>
    The application of the proposed method to clinical data showed that it distinguished between patients with moderate-to-severe SAS with a sensitivity of 100% and specificity of 100%, results which are superior to any other existing SAS screening methods.


    </sec><sec>
    <title>Conclusion</title>
    Since the RRI data can be easily measured by means of wearable heart rate sensors, our method may prove to be useful as an SAS screening system at home.


    </sec>

    DOI: 10.1007/s11325-020-02249-0

    Web of Science

    PubMed

    Other Link: http://link.springer.com/article/10.1007/s11325-020-02249-0/fulltext.html

  12. Preliminary Study Using Autoencoder for Early Detection of Heat Illness from Heart Rate Variability Obtained with Wearable Device Reviewed

    Inatsu, N; Noguchi, A; Ota, K; Fujiwara, K; Kubo, T; Yamakawa, T

    2021 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC)     page: 1348 - 1352   2021

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    Language:English   Publishing type:Research paper (scientific journal)  

    Web of Science

  13. Real-driving-implementable drowsy driving detection method using heart rate variability based on long short-term memory and autoencoder Reviewed

    Fujiwara, K; Hori, K; Fujiwara, K; Kano, M

    IFAC PAPERSONLINE   Vol. 54 ( 15 ) page: 526 - 531   2021

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.ifacol.2021.10.310

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  14. Resting Heart Rate Variability Is Associated With Subsequent Orthostatic Hypotension: Comparison Between Healthy Older People and Patients With Rapid Eye Movement Sleep Behavior Disorder Reviewed

    Sumi, Y; Nakayama, C; Kadotani, H; Matsuo, M; Ozeki, Y; Kinoshita, T; Goto, Y; Kano, M; Yamakawa, T; Hasegawa-Ohira, M; Ogawa, K; Fujiwara, K

    FRONTIERS IN NEUROLOGY   Vol. 11   page: 567984   2020.11

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.3389/fneur.2020.567984

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    PubMed

  15. Optimal Design of Neuroprotective Focal Brain Cooling Device Using Surrogate Model Approach Reviewed International journal

    Takuto Abe, Koichi Fujiwara, Takao Inoue, Takatomi Kubo, Toshitaka Yamakawa, Sadahiro Nomura, Michiyasu Suzuki, Manabu Kano

    IEEE Transactions on Medical Robotics and Bionics   Vol. 2 ( 4 ) page: 681 - 691   2020.11

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Institute of Electrical and Electronics Engineers (IEEE)  

    DOI: 10.1109/tmrb.2020.3020687

    Web of Science

  16. Over- and Under-sampling Approach for Extremely Imbalanced and Small Minority Data Problem in Health Record Analysis Invited Reviewed International journal

    Koichi Fujiwara, Yukun Huang, Kentaro Hori, Kenichi Nishioji, Masao Kobayashi, Mai Kamaguchi, Manabu Kano

    Frontiers in Public Health   Vol. 8   page: 178   2020.5

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    © Copyright © 2020 Fujiwara, Huang, Hori, Nishioji, Kobayashi, Kamaguchi and Kano. A considerable amount of health record (HR) data has been stored due to recent advances in the digitalization of medical systems. However, it is not always easy to analyze HR data, particularly when the number of persons with a target disease is too small in comparison with the population. This situation is called the imbalanced data problem. Over-sampling and under-sampling are two approaches for redressing an imbalance between minority and majority examples, which can be combined into ensemble algorithms. However, these approaches do not function when the absolute number of minority examples is small, which is called the extremely imbalanced and small minority (EISM) data problem. The present work proposes a new algorithm called boosting combined with heuristic under-sampling and distribution-based sampling (HUSDOS-Boost) to solve the EISM data problem. To make an artificially balanced dataset from the original imbalanced datasets, HUSDOS-Boost uses both under-sampling and over-sampling to eliminate redundant majority examples based on prior boosting results and to generate artificial minority examples by following the minority class distribution. The performance and characteristics of HUSDOS-Boost were evaluated through application to eight imbalanced datasets. In addition, the algorithm was applied to original clinical HR data to detect patients with stomach cancer. These results showed that HUSDOS-Boost outperformed current imbalanced data handling methods, particularly when the data are EISM. Thus, the proposed HUSDOS-Boost is a useful methodology of HR data analysis.

    Scopus

  17. Evaluating Mental State of Drivers in Automated Driving Using Heart Rate Variability towards Feasible Request-to-Intervene Reviewed

    Garcia, FC; Kubo, T; Chang, CL; Hisada, M; Bando, T; Kato, M; Mori, M; Takenaka, K; Yamakawa, T; Fujiwara, K; Ikeda, K

    2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)     page: 3454 - 3459   2020

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1109/smc42975.2020.9283356

    Web of Science

  18. Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG. Reviewed International journal

    Koichi Fujiwara, Erika Abe, Keisuke Kamata, Chikao Nakayama, Yoko Suzuki, Toshitaka Yamakawa, Toshihiro Hiraoka, Manabu Kano, Yukiyoshi Sumi, Fumi Masuda, Masahiro Matsuo, Hiroshi Kadotani

    IEEE transactions on bio-medical engineering   Vol. 66 ( 6 ) page: 1769 - 1778   2019.6

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    OBJECTIVE: Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. METHODS: Changes in sleep condition affect the autonomic nervous system and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, which is a well known anomaly detection method. RESULT: The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 pre-N1 episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. CONCLUSION: The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. SIGNIFICANCE: The proposed method can contribute to preventing accidents caused by drowsy driving.

    DOI: 10.1109/TBME.2018.2879346

    Web of Science

    PubMed

  19. Epileptic Seizure Suppression by Focal Brain Cooling with Recirculating Coolant Cooling System: Modeling and Simulation Reviewed

    IEEE Transactions on Neural Systems & Rehabilitation Engineering   Vol. 27 ( 2 ) page: 162-171   2019.2

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

  20. Epileptic Seizure Suppression by Focal Brain Cooling with Recirculating Coolant Cooling System: Modeling and Simulation Reviewed

    Kei Hata, Koichi Fujiwara, Takao Inoue, Takuto Abe, Takatomi Kubo, Toshitaka Yamakawa, Sadahiro Nomura, Hirochika Imoto, Michiyasu Suzuki, Manabu Kano

    IEEE Transactions on Neural Systems and Rehabilitation Engineering   Vol. 27 ( 2 ) page: 162 - 171   2019.2

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    © 2001-2011 IEEE. A focal brain cooling system for treatment of refractory epilepsy that is implantable and wearable may permit patients with this condition to lead normal daily lives. We have developed such a system for cooling of the epileptic focus by delivery of cold saline to a cooling device that is implanted cranially. The outflow is pumped for circulation and cooled by a Peltier device. Here, we describe the design of the system and evaluate its feasibility by simulation. Mathematical models were constructed based on equations of fluid dynamics and data from a cat model. Computational fluid dynamics simulations gave the following results: 1) a cooling device with a complex channel structure gives a more uniform temperature in the brain; 2) a cooling period of <10 min is required to reach an average temperature of 25.0°Cat 2 mm below the brain surface, which is the target temperature for seizure suppression. This time is short enough for cooling of the brain before seizure onset after seizure prediction by an intracranial electroencephalogram-based algorithm; and 3) battery charging would be required once every several days for most patients. These results suggest that the focal brain cooling system may be clinically applicable.

    DOI: 10.1109/TNSRE.2019.2891090

    Web of Science

    Scopus

    PubMed

  21. Ischemic stroke detection by analyzing heart rate variability in rat middle cerebral artery occlusion model Reviewed International journal

    Tomonobu Kodama, Keisuke Kamata, Koichi Fujiwara, Manabu Kano, Toshitaka Yamakawa, Ichiro Yuki, Yuichi Murayama

    IEEE Transactions on Neural Systems and Rehabilitation Engineering   Vol. 26 ( 6 ) page: 1152 - 1160   2018.6

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    © 2001-2011 IEEE. Although early reperfusion therapy is effective for acute ischemic stroke, limited therapeutic time-window resulted in only 10% of patients receiving reperfusion therapy. A fast and reliable stroke detection method is desired so that patients can receive early reperfusion therapy. It has been reported that ischemic stroke affects heart rate variability (HRV), which reflects activities of the autonomic nervous function. Thus, ischemic stroke may be detected at an acute stage through monitoring HRV. This paper proposes an HRV-based ischemic stroke detection algorithm by using multivariate statistical process control (MSPC), which is a well-known anomaly detection algorithm. As a feasibility study before collecting a large amount of clinical data from human patients, this paper used the middle cerebral artery occlusion (MCAO) model in rats for collecting HRV data shortly after ischemic stroke onsets. The 11 MCAO-operated rats and 11 sham-operated rats were prepared, and HRV data of three sham-operated rats were used for model construction. The data on the other 19 rats were used for its validation. The experimental result showed that sensitivity and specificity of the proposed algorithm were 82% and 75%, respectively. Thus, the present work shows the possibility of realizing an HRV-based ischemic stroke detection system for human patients.

    DOI: 10.1109/TNSRE.2018.2834554

    Web of Science

    Scopus

    PubMed

  22. Nearest Correlation-Based Input Variable Weighting for Soft-Sensor Design Reviewed

    Fujiwara, K; Kano, M

    FRONTIERS IN CHEMISTRY   Vol. 6   2018.5

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    DOI: 10.3389/fchem.2018.00171

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    PubMed

  23. Analysis of VNS Effect on EEG Connectivity with Granger Causality and Graph Theory Reviewed

    Uchida, T; Fujiwara, K; Inoue, T; Maruta, Y; Kano, M; Suzuki, M

    2018 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC)     page: 861 - 864   2018

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    Web of Science

  24. Design of False Heart Rate Feedback System for Improving Game Experience Reviewed

    Ogawa, S; Fujiwara, K; Yamakawa, T; Abe, E; Kano, M

    2018 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)     2018

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    Web of Science

  25. ACUTE EFFECT OF CONTINUOUS POSITIVE AIRWAY PRESSURE THERAPY ON HEART RATE VARIABILITY OF SAS PATIENTS IN CONSECUTIVE NIGHTS Reviewed

    Nakayama, C; Fujiwara, K; Matsuo, M; Kano, M; Kadotani, H

    SLEEP MEDICINE   Vol. 40   page: E237 - E237   2017.12

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    DOI: 10.1016/j.sleep.2017.11.692

    Web of Science

  26. MISSING RRI INTERPOLATION ALGORITHM USING JUST-IN-TIME MODELING FRAMEWORK AND ITS APPLICATION TO HRV-BASED DROWSY DRIVING DETECTION Reviewed

    Fujiwara, K; Kinoshita, T; Kamata, K; Yamakawa, T; Kano, M

    SLEEP MEDICINE   Vol. 40   page: E101 - E101   2017.12

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.sleep.2017.11.293

    Web of Science

  27. Causal Analysis based on Non-time-series Kernel Granger Causality in a Steelmaking Process Reviewed

    Sato, R; Fujiwara, K; Tani, M; Mori, J; Ise, J; Harada, K; Kano, M

    2017 11TH ASIAN CONTROL CONFERENCE (ASCC)     page: 1778 - 1782   2017

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    Web of Science

  28. Development of Correlation-based Process Characteristics Visualization Method and Its Application to Fault Detection Reviewed

    Fujiwara, K; Kano, M

    2017 13TH IEEE INTERNATIONAL CONFERENCE ON CONTROL & AUTOMATION (ICCA)     page: 940 - 945   2017

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    Web of Science

  29. Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features Reviewed

    K. Fujiwara, M. Miyajima, T. Yamakawa, E. Abe, Y. Suzuki, Y. Sawada, M. Kano, T. Maehara, K. Ohta, T. Sasai-Sakuma, T. Sasano, M. Matsuura, and E. Matsushima

    IEEE Transactions on Biomedical Engineering   Vol. 63   page: 1321-1332   2016.6

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    DOI: 10.1109/TBME.2015.2512276

    PubMed

  30. Input variable selection for PLS modeling using nearest correlation spectral clustering Reviewed

    K. Fujiwara; H. Sawada; M. Kano

    Chemometrics and Intelligent Laboratory Systems   Vol. 118   page: 109-119   2012.8

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    DOI: 10.1016/j.chemolab.2012.08.007

    J-GLOBAL

  31. Soft-sensor development using correlation-based just-in-time modeling Reviewed

    K. Fujiwara; M. Kano; S. Hasebe; A. Takinami

    AIChE Journal   Vol. 55 ( 7 ) page: 1754-1765   2009.7

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    DOI: 10.1002/aic.11791

  32. Frustration control during driving using auditory false heart rate feedback Reviewed

    Koshi Ota, Koichi Fujiwara, Toshihiro Hiraoka

    Transportation Research Part F: Traffic Psychology and Behaviour   Vol. 101   page: 375 - 386   2024.2

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Elsevier BV  

    DOI: 10.1016/j.trf.2024.01.014

  33. Heat illness detection with heart rate variability analysis and anomaly detection algorithm Reviewed

    Koichi Fujiwara, Koshi Ota, Shota Saeda, Toshitaka Yamakawa, Takatomi Kubo, Aozora Yamamoto, Yuki Maruno, Manabu Kano

    Biomedical Signal Processing and Control   Vol. 87   page: 105520 - 105520   2024.1

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Elsevier BV  

    DOI: 10.1016/j.bspc.2023.105520

    Web of Science

  34. Predictive Modeling for Hospital Readmissions for Patients with Heart Disease: An updated review from 2012-2023 Reviewed

    Wei Zhang, Weihan Cheng, Koichi Fujiwara, Richard Evans, Chengyan Zhu

    IEEE Journal of Biomedical and Health Informatics     2024

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    DOI: 10.1109/JBHI.2023.3349353

  35. Association entre hypotension post-induction et mortalité postopératoire : une étude de cohorte rétrospective monocentrique Reviewed

    Toshiyuki Nakanishi, Tatsuya Tsuji, Yoshiki Sento, Hiroya Hashimoto, Koichi Fujiwara, Kazuya Sobue

    Canadian Journal of Anesthesia/Journal canadien d'anesthésie   Vol. 71 ( 3 ) page: 343 - 352   2023.11

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    DOI: 10.1007/s12630-023-02653-6

    PubMed

    Other Link: https://link.springer.com/article/10.1007/s12630-023-02653-6/fulltext.html

  36. Prediction Model of Postoperative Pain Exacerbation Using a Wearable Electrocardiogram Sensor Reviewed

    Toshiyuki Nakanishi, Koichi Fujiwara, Kazuya Sobue

    2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)     2023.10

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/apsipaasc58517.2023.10317498

  37. FexSplice: A LightGBM-Based Model for Predicting the Splicing Effect of a Single Nucleotide Variant Affecting the First Nucleotide G of an Exon Reviewed

    Atefeh Joudaki, Jun-ichi Takeda, Akio Masuda, Rikumo Ode, Koichi Fujiwara, Kinji Ohno

    Genes   Vol. 14 ( 9 ) page: 1765 - 1765   2023.9

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    Single nucleotide variants (SNVs) affecting the first nucleotide G of an exon (Fex-SNVs) identified in various diseases are mostly recognized as missense or nonsense variants. Their effect on pre-mRNA splicing has been seldom analyzed, and no curated database is available. We previously reported that Fex-SNVs affect splicing when the length of the polypyrimidine tract is short or degenerate. However, we cannot readily predict the splicing effects of Fex-SNVs. We here scrutinized the available literature and identified 106 splicing-affecting Fex-SNVs based on experimental evidence. We similarly identified 106 neutral Fex-SNVs in the dbSNP database with a global minor allele frequency (MAF) of more than 0.01 and less than 0.50. We extracted 115 features representing the strength of splicing cis-elements and developed machine-learning models with support vector machine, random forest, and gradient boosting to discriminate splicing-affecting and neutral Fex-SNVs. Gradient boosting-based LightGBM outperformed the other two models, and the length and nucleotide compositions of the polypyrimidine tract played critical roles in the discrimination. Recursive feature elimination showed that the LightGBM model using 15 features achieved the best performance with an accuracy of 0.80 ± 0.12 (mean and SD), a Matthews Correlation Coefficient (MCC) of 0.57 ± 0.15, an area under the curve of the receiver operating characteristics curve (AUROC) of 0.86 ± 0.08, and an area under the curve of the precision–recall curve (AUPRC) of 0.87 ± 0.09 using a 10-fold cross-validation. We developed a web service program, named FexSplice that accepts a genomic coordinate either on GRCh37/hg19 or GRCh38/hg38 and returns a predicted probability of aberrant splicing of A, C, and T variants.

    DOI: 10.3390/genes14091765

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    PubMed

  38. 医工連携によるRBD病態解明の取り組み 立ち上がる数分前に起立性低血圧を予測できるか?レム睡眠行動障害患者への心拍変動解析の応用例 Reviewed

    角 幸頼, 小枝 正汰, 藤原 幸一, 尾関 祐二, 角谷 寛

    日本睡眠学会定期学術集会・日本時間生物学会学術大会合同大会プログラム・抄録集   Vol. 45回・30回   page: 141 - 141   2023.9

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  39. 医工連携によるRBD病態解明の取り組み 心拍変動に着目したレム睡眠行動障害患者における起立性低血圧の有無を判定する機械学習モデル Reviewed

    藤原 幸一, 小枝 正汰, 角 幸頼, 今井 眞, 角谷 寛

    日本睡眠学会定期学術集会・日本時間生物学会学術大会合同大会プログラム・抄録集   Vol. 45回・30回   page: 141 - 141   2023.9

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    Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:日本睡眠学会・日本時間生物学会  

  40. Association between post-induction hypotension and postoperative mortality: A single-center retrospective cohort study Reviewed

    T. Nakanishi, T. Tsuji, Y. Sento, H. Hashimoto, K. Fujiwara, K. Sobue

    Canadian Journal of Anesthesia     2023.8

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  41. Learning curve of i-gel insertion in novices using a cumulative sum analysis Reviewed

    Toshiyuki Nakanishi, Seishi Sakamoto, Manabu Yoshimura, Koichi Fujiwara, Takashi Toriumi

    Scientific Reports   Vol. 13 ( 1 ) page: 7121   2023.5

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    Abstract

    The i-gel, a popular second-generation supraglottic airway device, has been used in a variety of airway management situations, including as an alternative to tracheal intubation for general anesthesia, rescue in difficult airway settings, and out-of-hospital cardiac arrest resuscitation. We aimed to investigate the number of experiences needed to achieve a rapid, highly successful first attempt i-gel insertion in novices with a cumulative sum analysis. We also looked at how learning affected success rates, insertion time, and bleeding and reflex (limb movement, frowning face, or coughing) incidences. This prospective observational study included 15 novice residents from March 2017 to February 2018 in a tertiary teaching hospital. Finally, 13 residents with 35 [30–42] (median [interquartile range]) cases of i-gel insertion were analyzed. The cumulative sum analysis showed that 11 of 13 participants had an acceptable failure rate after 15 [8–20] cases. With increasing experience, success rate (P = 0.004), insertion time (P &lt; 0.001), and incidence of bleeding (P = 0.006) all improved. However, the incidence of reflex did not change (P = 0.43). Based on our results, we suggest that 20 cases are preferable for novices to develop skills in using the i-gel in airway management.

    DOI: 10.1038/s41598-023-34152-5

    PubMed

    Other Link: https://www.nature.com/articles/s41598-023-34152-5

  42. Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder Invited Reviewed

    Rikumo Ode, Koichi Fujiwara, Miho Miyajima, Toshikata Yamakawa, Manabu Kano, Kazutaka Jin, Nobukazu Nakasato, Yasuko Sawai, Toru Hoshida, Masaki Iwasaki, Yoshiko Murata, Satsuki Watanabe, Yutaka Watanabe, Yoko Suzuki, Motoki Inaji, Naoto Kunii, Satoru Oshino, Hui Ming Khoo, Haruhiko Kishima, Taketoshi Maehara

    Artificial Life and Robotics   Vol. 28   page: 403 - 409   2023.5

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    Abstract

    Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.

    DOI: 10.1007/s10015-022-00832-0

    Other Link: https://link.springer.com/article/10.1007/s10015-022-00832-0/fulltext.html

  43. Causal analysis of nitrogen oxides emissions process in coal-fired power plant with LiNGAM Invited Reviewed

    Tatsuki Saito, Koichi Fujiwara

    Frontiers in Analytical Science   Vol. 3   2023.2

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    Coal has been an important energy source worldwide; however, it is the largest source of nitrogen oxide (NOx) emissions because the amount of nitrogen in coal is larger than that of other fossil fuels. Precise control of NOx emissions is required in operations of coal-fired power plants from the viewpoint of air pollution control. Although theoretical analyses of NOx generation from a coal-fired power plant have been conducted, it is difficult to precisely predict NOx generation in an actual plant. NOx generation is affected by various factors, such as furnace design and operating conditions, and there are complicated relationships among them. Thus, it is necessary to identify important operating factors that affect NOx generation in actual coal-fired power plants. A linear non-Gaussian acyclic model (LiNGAM) is an exploratory causal analysis method that identifies a causal ordering of variables and their connection strengths without any prior knowledge of causal relationships among variables. In this study, we analyzed real operation data collected from a coal-fired power plant using LiNGAM to identify factors of NOx generation. The causal relationship between process variables and NOx generation was estimated by means of LiNGAM, and the connectional strengths of the variables on NOx generation were derived. The analysis results agreed with previous reports on NOx generation mechanisms, such as combustion air temperature, steam temperature on a specific side of the furnace, and air flow rate of forced draft fans. In addition, we found the steam flow rate and the furnace pressure as new candidate factors of NOx generation through causal analysis using LiNGAM, which heretofore has not been suggested. Our analysis result should contribute to reducing NOx emissions from coal-fired power plants in the future.

    DOI: 10.3389/frans.2023.1045324

  44. AI model for predicting postoperative pain exacerbation using a wearable electrocardiogram sensor and an intravenous patient-controlled analgesia device Reviewed

    NAKANISHI Toshiyuki, FUJIWARA Koichi, SENTO Yoshiki, SOBUE Kazuya

    Proceedings of the Annual Conference of JSAI   Vol. JSAI2023 ( 0 ) page: 1L5OS18b02 - 1L5OS18b02   2023

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    There is a need to develop objective and real-time postoperative pain assessment methods in perioperative medicine. Few studies have evaluated the relationship between pain severity and temporal changes of physiological signals in actual postoperative patients. The aim of the study was to evaluate postoperative pain continuously and to predict pain exacerbation in real-time. We focused on intravenous patient-controlled analgesia (IV-PCA), a common analgesic modality utilized in post-surgical patients. We chose an electrocardiogram (ECG) as a feature to detect pain exacerbation. We developed a machine learning model which was trained from IV-PCA records and ECG of postoperative patients to predict pain exacerbation. A self-attentive autoencoder (SA-AE) model achieved 54% of sensitivity and a 1.76 times/h of false positive rate. In summary, we propose a novel pain evaluation method using an IV-PCA device. According to the current findings, ECG features may be used to predict postoperative pain exacerbation in real-time.

    DOI: 10.11517/pjsai.jsai2023.0_1l5os18b02

    CiNii Research

  45. Nearest Neighbor Search-Based Modification of RRI Data with Premature Atrial Contraction and Premature Ventricular Contraction Reviewed

    Sifeng Chen, Shota Kato, Koichi Fujiwara, Manabu Kano

    2023 SICE INTERNATIONAL SYMPOSIUM ON CONTROL SYSTEMS, SICE ISCS     page: 53 - 57   2023

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    Heart rate variability (HRV) analysis plays an essential role in healthcare. HRV features cannot be extracted accurately from the R-R interval (RRI) when RRI data contains artifacts. Previous research for modifying RRI data with artifacts considered premature atrial contraction (PAC) and premature ventricular contraction (PVC), which are the most common types of extrasystoles occurring every day in healthy persons. This research proposed three new RRI modification algorithms for PAC and PVC using nearest neighbor search (NNS) algorithms: k-nearest neighbors (KNN), clustering-KNN (CKNN), and approximate nearest neighbors (ANN). The present work demonstrated that the ANN-based RRI modification (ANN-RM) algorithm achieved lower root mean squared errors (RMSEs) than the CKNN-based RRI modification algorithm and the highest computational speed. The RMSEs of ANN-RM for PAC and PVC were 23.0 ms and 26.2 ms, respectively.

    Web of Science

  46. Development of AI for screening sleep apnea syndrome using heart rate variability analysis and neural network Reviewed

    SUMI Yukiyoshi, FUJIWARA Koichi, IWASAKI Ayako, OZEKI Yuji, KADOTANI Hiroshi

    Proceedings of the Annual Conference of JSAI   Vol. JSAI2023 ( 0 ) page: 1L4OS18a04 - 1L4OS18a04   2023

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    Sleep apnea syndrome (SAS) is a disorder in which breathing events such as apnea or hypopnea during sleep causes sleepiness and fatigue. SAS is a risk factor for coronary artery disease ( angina and myocardial infarction), atrial fibrillation, stroke, etc. The prevalence of SAS is reported to be 2-7% in adults; however, more patients with less subjective symptoms is estimated to have SAS. SAS is generally diagnosed by polysomnography (PSG) in specialized sleep institutes. However, PSG is performed only in a limited number of centers. Therefore, a screening method for SAS is needed to be developed. We focused on heart rate variability related to respiratory events and developed a screening method for SAS using neural networks. We examined a large PSG data set (N = 938) and attempted to detect SAS using long-term and short-term memory for heart rate data. Severe SAS was detected with an area under the curve (AUC) of 0.92, a sensitivity of 0.80, and a specificity of 0.84. We aim to develop a convenient screening method using a wearable device for the early detection of SAS.

    DOI: 10.11517/pjsai.jsai2023.0_1l4os18a04

    CiNii Research

  47. Causal Plot: Causal-Based Fault Diagnosis Method Based on Causal Analysis Reviewed

    Yoshiaki Uchida, Koichi Fujiwara, Tatsuki Saito, Taketsugu Osaka

    Processes   Vol. 10 ( 11 ) page: 2269   2022.11

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.3390/pr10112269

    Web of Science

  48. Effects of pleasant sound on overnight sleep condition: A crossover randomized study Reviewed

    Shota Saeda, Koichi Fujiwara, Takafumi Kinoshita, Yukiyoshi Sumi, Masahiro Matsuo, Kiyoshi Yamaki, Takahiro Kawashima, Hiroshi Kadotani

    Frontiers in Sleep   Vol. 1   2022.10

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    It is desirable to improve sleep quality since poor sleep results in decreases in work productivity and increases in risks of lifestyle-related diseases. Sleep spindles in sleep EEG are waveforms that characterize non-REM sleep Stage 2 (Stage N2). Music therapy has been adopted as a non-pharmacological therapy for sleep quality improvement; however, few studies mention the relationship between music during sleep and spindles. We conducted a crossover randomized study to investigate music's effects on spindles and sleep parameters. Polysomnography (PSG) was performed on 12 adult males with sleep difficulties over three nights, during which they were exposed to three different acoustic environments–silent, white noise, and pleasant sounds–throughout the night, in a crossover randomized setting. Half of the participants with large WASO were defined as the sleep maintenance difficulty group. We investigated whether pleasant sounds shortened sleep onset latency (SOL) and increased the number of spindles (SN) and spindle density (SD) compared to white noise, using silent as the reference. The spindles were detected using the previously reported automatic spindle detection algorithm. After one patient was excluded due to data corruption, a total of 11 participants, including the sleep maintenance difficulty group (n = 5), were analyzed. For all participants, SOL was not significantly shorter with pleasant sound than with white noise (p = 0.683); for the sleep maintenance difficulty group, SOL tended to be shorter with pleasant sound than with white noise (p = 0.060). Compared to white noise, the SN increased in pleasant sound for 7 of 11 (4 of 5 in the sleep maintenance difficulty group), and SD increased for 5 of 11 (3 of 5 in the sleep maintenance difficulty group). The results suggest that all-night background sound exposure may affect SN and SD. Future research should investigate whether background sound exposure reduces sleep-related distress, achieves sound sleep, or improves daytime psychomotor function.

    DOI: 10.3389/frsle.2022.986333

  49. Interactive system for optimal position selection of a patch-type R–R interval telemeter Invited Reviewed

    Aoi Noguchi, Tomoyuki Takano, Koichi Fujiwara, Miho Miyajima, Toshitaka Yamakawa

    Artificial Life and Robotics     2022.10

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    DOI: 10.1007/s10015-022-00815-1

    Other Link: https://link.springer.com/article/10.1007/s10015-022-00815-1/fulltext.html

  50. Sleep-EEG-based Parameters for Discriminating Fatigue and Sleepiness Reviewed

    Koichi Fujiwara, Yuki Goto, Yukiyoshi Sumi, Manabu Kano, Hiroshi Kadotani

    Frontiers in Sleep   Vol. 1   page: 975415 - 975415   2022.10

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.3389/frsle.2022.975415

  51. Wearable sensor device-based detection of decreased heart rate variability in Parkinson's disease. Reviewed International journal

    Masashi Suzuki, Tomohiko Nakamura, Masaaki Hirayama, Masamichi Ueda, Mai Hatanaka, Yumiko Harada, Masahiro Nakatochi, Daisuke Nakatsubo, Satoshi Maesawa, Ryuta Saito, Koichi Fujiwara, Masahisa Katsuno

    Journal of neural transmission (Vienna, Austria : 1996)   Vol. 129 ( 10 ) page: 1299 - 1306   2022.10

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    The evidence that heart rate variability (HRV) decreases during early Parkinson's disease (PD) largely depends on electrocardiogram data. In this study, we examined HRV in PD using wearable sensors and assessed various evaluation methods for detecting disease-related alterations. We evaluated 27 patients with PD and 23 disease controls. The wearable sensors POLAR V800 HR and POLAR H10 were used for the HRV measurements. The participants wore the two sensors for approximately 24 h, and long-term HRV data were acquired. We analyzed the standard deviation of normal R-R intervals (SDNN) and coefficient of variation of R-R intervals (CVRR) for every 100 consecutive beats. Focusing on the fluctuation of SDNN and CVRR, we extracted the minimum, first decile, first quartile, and median values of SDNN and CVRR. The area under the receiver operating characteristic curve (AUC) for each HRV parameter was calculated to differentiate PD from the disease controls. The minimum values of SDNN and CVRR had the highest AUC (SDNN: AUC 0.90, 95% confidence interval [CI] 0.78-0.96; CVRR: AUC 0.90, CI 0.76-0.96) among the evaluation methods tested. The minimum values of SDNN and CVRR were significantly decreased in PD (SDNN: 9.5 ± 4.0 ms vs. 4.4 ± 2.0 ms, p < 0.0001; CVRR: 1.15 ± 0.33% vs. 0.65 ± 0.24%, p < 0.0001). We detected decreased HRV in PD using wearable sensors. Analyzing the minimum values of the HRV parameter in long-term recordings appears to be appropriate for detecting the decrease in HRV in PD.

    DOI: 10.1007/s00702-022-02528-y

    Web of Science

    PubMed

  52. 麻酔・集中治療領域における医学と工学の異分野融合研究の経験 Reviewed

    中西 俊之, 祖父江 和哉, 藤原 幸一

    麻酔科学サマーセミナー   Vol. 18回   page: 44 - 44   2022.7

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    Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:麻酔科学サマーセミナー事務局  

  53. Prediction of GABA receptor antagonist-induced convulsion in cynomolgus monkeys by combining machine learning and heart rate variability analysis Reviewed

    Shoya Nagata, Koichi Fujiwara, Kazuhiro Kuga, HarushigeOzaki

    Journal of Pharmacological and Toxicological Methods   Vol. 112   page: 107127   2021.10

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  54. Sympathetic hyperactivity, hypertension, and tachycardia induced by stimulation of the ponto-medullary junction in humans. Reviewed International journal

    Tadashi Hamasaki, Toshitaka Yamakawa, Koichi Fujiwara, Haruki Harashima, Kota Nakamura, Yoshihiro Ikuta, Tatsuo Yamamoto, Yu Hasegawa, Tatsuya Takezaki, Akitake Mukasa

    Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology   Vol. 132 ( 6 ) page: 1264 - 1273   2021.6

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    OBJECTIVE: The purpose of this study is to investigate changes in autonomic activities and systemic circulation generated by surgical manipulation or electrical stimulation to the human brain stem. METHODS: We constructed a system that simultaneously recorded microsurgical field videos and heart rate variability (HRV) that represent autonomic activities. In 20 brain stem surgeries recorded, HRV features and sites of surgical manipulation were analyzed in 19 hypertensive epochs, defined as the periods with transient increases in the blood pressure. We analyzed the period during electrical stimulation to the ponto-medullary junction, performed for the purpose of monitoring a cranial nerve function. RESULTS: In the hypertensive epoch, HRV analysis showed that sympathetic activity predominated over the parasympathetic activity. The hypertensive epoch was more associated with surgical manipulation of the area in the caudal pons or the rostral medulla oblongata compared to controls. During the period of electrical stimulation, there were significant increases in blood pressures and heart rates, accompanied by sympathetic overdrive. CONCLUSIONS: Our results provide physiological evidence that there is an important autonomic center located adjacent to the ponto-medullary junction. SIGNIFICANCE: A large study would reveal a candidate target of neuromodulation for disorders with autonomic imbalances such as drug-resistant hypertension.

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  55. Development of Game-Like System Using Active Behavior Input for Wakefulness-Keeping Support in Driving Reviewed

    Tatsuro Ibe, Koichi Fujiwara, Toshihiro Hiraoka, Erika Abe, Toshitaka Yamakawa

    IEEE Transactions on Intelligent Vehicles   Vol. 6 ( 2 ) page: 323 - 332   2021.6

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    DOI: 10.1109/tiv.2020.3029260

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  56. Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis Reviewed

    Koichi Fujiwara, Shota Miyatani, Asuka Goda, Miho Miyajima, Tetsuo Sasano, Manabu Kano

    Sensors   Vol. 21 ( 9 ) page: 3235 - 3235   2021.5

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    Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

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  57. Sympathetic hyperactivity, hypertension, and tachycardia induced by stimulation of the ponto-medullary junction in humans Reviewed

    T. Hamasaki, T. Yamakawa, K. Fujiwara, H. Harashima, K. Nakamura, Y. Ikuta, T. Yamamoto, Y. Hasegawa, T. Takezaki, A. Mukasa

    Clinical Neurophysiology     2021.3

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  58. Work habit-related sleep debt; insights from factor identification analysis of actigraphy data Invited Reviewed International journal

    Yuki Goto, Koichi Fujiwara, Yukiyoshi Sumi, Masahiro Matsuo, Manabu Kano, Hiroshi Kadotani

    Frontiers in public health   Vol. 10   page: 630640 - 630640   2021.2

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    The present study investigates the factors of "Weekday sleep debt (WSD)" by comparing activity data collected from persons with and without WSD. Since it has been reported that the amount of sleep debt as well the difference between the social clock and the biological clock is associated with WSD, specifying the factors of WSD other than chronotype may contribute to sleep debt prevention. We recruited 324 healthy male employees working at the same company and collected their one-week wrist actigraphy data and answers to questionnaires. Because 106 participants were excluded due to measurement failure of the actigraphy data, the remaining 218 participants were included in the analysis. All participants were classified into WSD or non-WSD groups, in which persons had WDS if the difference between their weekend sleep duration and the mean weekday sleep duration was more than 120 min. We evaluated multiple measurements derived from the collected actigraphy data and trained a classifier that predicts the presence of WSD using these measurements. A support vector machine (SVM) was adopted as the classifier. In addition, to evaluate the contribution of each indicator to WSD, permutation feature importance was calculated based on the trained classifier. Our analysis results showed significant importance of the following three out of the tested 32 factors: 1) WSD was significantly related to persons with evening tendency. 2) Daily activity rhythms and sleep were less stable in the WSD group than in the non-WSD group. 3) A specific day of the week had the highest importance in our data, suggesting that work habit contributes to WSD. These findings indicate some WSD factors: evening chronotype, instability of the daily activity rhythm, and differences in work habits on the specific day of the week. Thus, it is necessary to evaluate the rhythms of diurnal activities as well as sleep conditions to identify the WSD factors. In particular, the diurnal activity rhythm influences WSD. It is suggested that proper management of activity rhythm may contribute to the prevention of sleep debt.

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  59. Resting Heart Rate Variability Is Associated With Subsequent Orthostatic Hypotension: Comparison Between Healthy Older People and Patients With Rapid Eye Movement Sleep Behavior Disorder Reviewed International journal

    Y. Sumi, C. Nakayama, H. Kadotani, M. Matsuo, Y. Ozeki, T. Kinoshita, Y. Goto, M. Kano, T. Yamakawa, M. Ohira, K. Ogawa, K. Fujiwara

    Frontiers in Neurology   Vol. 11   page: 567984   2020.11

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    DOI: 10.3389/fpubh.2020.00178

  60. Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability Reviewed International journal

    Toshitaka Yamakawa, Miho Miyajima, Koichi Fujiwara, Manabu Kano, Yoko Suzuki, Yutaka Watanabe, Satsuki Watanabe, Tohru Hoshida, Motoki Inaji, Taketoshi Maehara

    Sensors   Vol. 20 ( 14 ) page: 3987 - 3987   2020.7

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    A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R intervals derived from an electrocardiogram. A bespoke smartphone app calculates the indices of heart rate variability in real time from the R-R intervals, and the indices are monitored using multivariate statistical process control by the smartphone app. The proposed system was evaluated on seven epilepsy patients. The accuracy and reliability of the R-R interval measurement, which was examined in comparison with the reference electrocardiogram, showed sufficient performance for heart rate variability analysis. The results obtained using the proposed system were compared with those obtained using the existing video and electroencephalogram assessments; it was noted that the proposed method has a sensitivity of 85.7% in detecting heart rate variability change prior to seizures. The false positive rate of 0.62 times/h was not significantly different from the healthy controls. The prediction performance and practical advantages of portability and real-time operation are demonstrated in this study.

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  61. Regression and independence based variable importance measure Reviewed International journal

    Xinmin Zhang, Takuya Wada, Koichi Fujiwara, Manabu Kano

    Computers and Chemical Engineering   Vol. 135 ( 6 ) page: 106757   2020.4

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    © 2020 Elsevier Ltd Evaluating the importance of input (predictor) variables is of interest in many applications of statistical models. However, nonlinearity and correlation among variables make it difficult to measure variable importance accurately. In this work, a novel variable importance measure, called regression and independence based variable importance (RIVI), is proposed. RIVI is designed by integrating Gaussian process regression (GPR) and Hilbert-Schmidt independence criterion (HSIC) so that it is applicable to nonlinear systems. The results of two numerical examples demonstrate that RIVI is superior to several conventional measures including the Pearson correlation coefficient, PLS-β, PLS-VIP, Lasso, HSIC, and permutation importance with random forest in the variable identification accuracy.

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  62. Sleep Spindle Detection Using RUSBoost and Synchrosqueezed Wavelet Transform. Reviewed International journal

    Takafumi Kinoshita, Koichi Fujiwara, Manabu Kano, Keiko Ogawa, Yukiyoshi Sumi, Masahiro Matsuo, Hiroshi Kadotani

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society   Vol. 28 ( 2 ) page: 390 - 398   2020.2

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    Sleep spindles are important electroencephalographic (EEG) waveforms in sleep medicine; however, it is burdensome even for experts to detect spindles, so automatic spindle detection methodologies have been investigated. Conventional methods utilize waveforms template matching or machine learning for detecting spindles. In the former approach, it is necessary to tune thresholds for individual adaptation, while the latter approach has the problem of imbalanced data because the amount of sleep spindles is small compared with the entire EEG data. The present work proposes a sleep spindle detection method that combines wavelet synchrosqueezed transform (SST) and random under-sampling boosting (RUSBoost). SST is a time-frequency analysis method suitable for extracting features of spindle waveforms. RUSBoost is a framework for coping with the imbalanced data problem. The proposed SST-RUS can deal with the imbalanced data in spindle detection and does not require threshold tuning because RUSBoost uses majority voting of weak classifiers for discrimination. The performance of SST-RUS was validated using an open-access database called the Montreal archives of sleep studies cohort 1 (MASS-C1), which showed an F-measure of 0.70 with a sensitivity of 76.9% and a positive predictive value of 61.2%. The proposed method can reduce the burden of PSG scoring.

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  63. Trial of evaluation of emotions using heart rate variability in free moving dogs Reviewed

    MIKURU MURAYAMA, MIHO NAGASAWA, MAKI KATAYAMA, KAZUSHI IKEDA, TAKATOMI KUBO, TOSHITAKA YAMAKAWA, KOICHI FUJIWARA, TAKEFUMI KIKUSUI

    Japanese Journal of Animal Psychology   Vol. 70 ( 1 ) page: 15 - 18   2020

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    <p>Heart rate variability (HRV) is a physical and noninvasive index of the autonomic nervous system and has been used in a wide range of fields such as human medicine, veterinary and animal behavior. Measuring devices have been improved miniaturization and light-weighting and they make it possible to measure a dog's electrocardiogram (ECG) under a free moving condition. HRV has been known as an index not only of physical activity but also to evaluate an animal's emotional status. One concern is a difficulty in dissociating physical activity and emotional status in HRV parameters. In this study, we examined how the physical activity component and the emotional component affect in HRV. We measured HRV and acceleration of the dogs under two conditions, the physical activity (motion) condition and the reward condition where food treats were emotional stimuli and under the motion condition. As a result, a dog's HRV values were linearly regressed on the acceleration data. SDNN (Standard Deviation of NN intervals) affected by the composite acceleration in reward condition while rMSSD (root Mean Square of Successive Differences) affected in motion condition. These suggested that the physical activity and HRV indices distribute on regression lines and emotional stimuli influences each HRV indices differently.</p>

    DOI: 10.2502/janip.70.1.1

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  64. Obstructive sleep apnea screening by heart rate variability-based apnea/normal respiration discriminant model. Reviewed International journal

    Chikao Nakayama, Koichi Fujiwara, Yukiyoshi Sumi, Masahiro Matsuo, Manabu Kano, Hiroshi Kadotani

    Physiological measurement   Vol. 40 ( 12 ) page: 125001 - 125001   2019.12

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    OBJECTIVE: Obstructive sleep apnea (OSA) is a common sleep disorder; however, most patients are undiagnosed and untreated because it is difficult for patients themselves to notice OSA in daily living. Polysomnography (PSG), which is the gold standard test for sleep disorder diagnosis, cannot be performed in many hospitals. This fact motivates us to develop a simple system for screening OSA at home. APPROACH: The autonomic nervous system changes during apnea, and such changes affect heart rate variability (HRV). This work develops a new apnea screening method based on HRV analysis and machine learning technologies. An apnea/normal respiration (A/N) discriminant model is built for respiration condition estimation for every heart rate measurement, and an apnea/sleep ratio is introduced for final diagnosis. A random forest is adopted for the A/N discriminant model construction, which is trained with the PhysioNet apnea-ECG database. MAIN RESULTS: The screening performance of the proposed method was evaluated by applying it to clinical PSG data. Sensitivity and specificity achieved 76% and 92%, respectively, which are comparable to existing portable sleep monitoring devices used in sleep laboratories. SIGNIFICANCE: Since the proposed OSA screening method can be used more easily than existing devices, it will contribute to OSA treatment.

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  65. Views of patients with epilepsy on wearable seizure prediction system; impact of two different type of devices on sleep quality Reviewed

    M. Miyajima, T. Yamakawa, K. Fujiwara, T. Seki, T. ohno, M. Iimori, M. Inaji, H. Osoegawa, M. Kano, T. Maehara

    Sleep Medicine   Vol. 64   2019.12

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    DOI: 10.1016/j.sleep.2019.11.727

  66. 医師患者関係のトラスト構築に向けたAI活用の可能性 Reviewed

    藤田 卓仙, 江間 有沙, 近藤 諭, 藤原 幸一, 中谷内 一也, 尾藤 誠司

    医療情報学連合大会論文集   Vol. 39回   page: 126 - 128   2019.11

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  67. Emotional Contagion From Humans to Dogs Is Facilitated by Duration of Ownership Invited Reviewed

    M. Katayama, T. Kubo, T. Yamakawa, K. Fujiwara, K. Nomoto, K. Ikeda, K. Mogi, M. Nagasawa and T. Kikusui

    Frontiers in Psychology   Vol. 10   page: 1678   2019.7

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    DOI: https://doi.org/10.3389/fpsyg.2019.01678

  68. Development of a Sleep Apnea Detection Algorithm Using Long Short-Term Memory and Heart Rate Variability. Reviewed International journal

    Ayako Iwasaki, Chikao Nakayama, Koichi Fujiwara, Yukiyoshi Sumi, Masahiro Matsuo, Manabu Kano, Hiroshi Kadotani

    Annu Int Conf IEEE Eng Med Biol Soc.   Vol. 2019   page: 3964 - 3967   2019.7

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    Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with the increased risk of lifestyle diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with high sensitivity (100%) and specificity (100%).

    DOI: 10.1109/EMBC.2019.8856463

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  69. Emotional Contagion From Humans to Dogs Is Facilitated by Duration of Ownership Reviewed International journal

    M. Katayama, T. Kubo, T. Yamakawa, K. Fujiwara, K. Nomoto, K. Ikeda, K. Mogi, M. Nagasawa, T. Kikusui

    Frontiers in Psychology   Vol. 10   page: 1678 - 1678   2019.7

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    Emotional contagion is a primitive form of empathy that does not need higher psychological functions. Recent studies reported that emotional contagion exists not only between humans but also among various animal species. The dog (Canis familiaris) is a unique animal and the oldest domesticated species. Dogs have coexisted with humans for more than 30,000 years and are woven into human society as partners bonding with humans. Dogs have acquired human-like communication skills and, likely as a result of the domestication process, the ability to read human emotions; therefore, it is feasible that there may be emotional contagion between human and dogs. However, the higher time-resolution of measurement of emotional contagion between them is yet to be conducted. We assessed the emotional reactions of dogs and humans by heart rate variability (HRV), which reflects emotion, under a psychological stress condition on the owners. The correlation coefficients of heart beat (R-R) intervals (RRI), the standard deviations of all RR intervals (SDNN), and the square root of the mean of the sum of the square of differences between adjacent RR intervals (RMSSD) between dogs and owners were positively correlated with the duration of dog ownership. Dogs' sex also influenced the correlation coefficients of the RRI, SDNN, and RMSSD in the control condition; female showed stronger values. These results suggest that emotional contagion from owner to dog can occur especially in females and the time sharing the same environment is the key factor in inducing the efficacy of emotional contagion.

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  70. Missing RRI interpolation algorithm based on locally weighted partial least squares for precise heart rate variability analysis Reviewed International journal

    Keisuke Kamata, Koichi Fujiwara, Takafumi Kinoshita, Manabu Kano

    Sensors   Vol. 18 ( 11 ) page: 3870   2018.11

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    © 2018 by the authors. Licensee MDPI, Basel, Switzerland. The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV), which reflects activities of the autonomic nervous system (ANS) and has been used for various health monitoring services. Accurate R wave detection is crucial for success in HRV-based health monitoring services; however, ECG artifacts often cause missing R waves and deteriorate the accuracy of HRV analysis. The present work proposes a new missing RRI interpolation technique based on Just-In-Time (JIT) modeling. In the JIT modeling framework, a local regression model is built by weighing samples stored in the database according to the distance from a query and output is estimated only when an estimate is requested. The proposed method builds a local model and estimates missing RRI only when an RRI detection error is detected. Locally weighted partial least squares (LWPLS) is adopted for local model construction. The proposed method is referred to as LWPLS-based RRI interpolation (LWPLS-RI). The performance of the proposed LWPLS-RI was evaluated through its application to RRI data with artificial missing RRIs. We used the MIT-BIH Normal Sinus Rhythm Database for nominal RRI dataset construction. Missing RRIs were artificially introduced and they were interpolated by the proposed LWPLS-RI. In addition, MEAN that replaces the missing RRI by a mean of the past RRI data was compared as a conventional method. The result showed that the proposed LWPLS-RI improved root mean squared error (RMSE) of RRI by about 70% in comparison with MEAN. In addition, the proposed method realized precise HRV analysis. The proposed method will contribute to the realization of precise HRV-based health monitoring services.

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  71. Deniosing Autoencoder-based Modification of RRI data with Premature Ventricular Contraction for Precise Heart Rate Variability Analysis Reviewed

    Shota Miyatani, Koichi Fujiwara, Manabu Kano

    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS   Vol. 2018-July   page: 5018 - 5021   2018.10

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    © 2018 IEEE. The fluctuation of an RR interval (RRI) on an electrocardiogram (ECG) is called heart rate variability (HRV). HRV reflects the autonomic nerve activity, thus HRV analysis has been used for health monitoring such as stress estimation, drowsiness detection, epileptic seizure prediction, and cardiovascular disease diagnosis. However, RRI and HRV features are easily affected by arrhythmia, which deteriorates the health monitoring performance. Premature ventricular contraction (PVC) is common arrhythmia that many healthy persons have. Thus, a new methodology for dealing with RRI fluctuation disturbed by PVC needs to be developed for realizing precise health monitoring. To modify RRI data affected by PVC, the present work proposes a new method based on a denoising autoencoder (DAE), which reconstructs original input data from the noisy input data by using a neural network. The proposed method, referred to as DAE-based RRI modification (DAERM), aims to correct the disturbed RRI data by regarding PVC as artifacts. The present work demonstrated the usefulness of the proposed DAE-RM through its application to real RRI data with artificial PVC (PVC-RRI). The result showed that DAE-RM successfully modified PVC-RRI data. In fact, the root means squared error (RMSE) of the modified RRI was improved by 83.5% from the PVC-RRI. The proposed DAERM will contribute to realizing precise HRV-based health monitoring in the future.

    DOI: 10.1109/EMBC.2018.8513218

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  72. Is hemifacial spasm affected by changes in the heart rate? A study using heart rate variability analysis. Reviewed International journal

    Tadashi Hamasaki, Motohiro Morioka, Koichi Fujiwara, Chikao Nakayama, Miho Harada, Kiyohiko Sakata, Yu Hasegawa, Toshitaka Yamakawa, Kazumichi Yamada, Akitake Mukasa

    Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology   Vol. 129 ( 10 ) page: 2205 - 2214   2018.10

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    OBJECTIVE: Hemifacial spasm (HFS) is caused by arterial conflict at the root exit zone of the facial nerve. As the offending artery is pulsatile in nature, this study investigated the association of heart rate fluctuation with HFS. METHODS: Twenty-four preoperative patients underwent simultaneous recordings of facial electromyogram and electrocardiogram overnight. Series of R-wave to R-wave intervals (RRIs) in the electrocardiogram were analyzed across subjects in relation to HFS. The degree of heart rate fluctuation was quantified by analyzing the heart rate variability (HRV). The sleep stage was evaluated during the period of HFS. RESULTS: A 0.1 Hz fluctuation in RRIs by 5% compared to the baseline preceded a few seconds the onset of the HFS, indicating that a significant increase in the heart rate coincided with HFS. HRV analysis demonstrated that fluctuations in the heart rate were significantly enhanced during HFS. Wake or light sleep stages were more often accompanied by HFS, suggesting an association with autonomic activities. CONCLUSION: Our findings suggest that the etiology of HFS is more than just a mechanical compression of the facial nerve and may involve changes in pulsatile frequency in offending arteries. SIGNIFICANCE: We propose the etiology of HFS from a unique standpoint.

    DOI: 10.1016/j.clinph.2018.07.003

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  73. Nearest Correlation-Based Input Variable Weighting for Soft-sensor Design, Frontiers in Chemistry Reviewed International journal

    K. Fujiwara, M. Kano

    Frontiers in Chemistry     page: .   2018.5

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  74. Design of false heart rate feedback system for improving game experience Reviewed

    Sayaka Ogawa, Koichi Fujiwara, Toshitaka Yamakawa, Erika Abe, Manabu Kano

    2018 IEEE International Conference on Consumer Electronics, ICCE 2018   Vol. 2018-January   page: 1 - 4   2018.3

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    © 2018 IEEE. When players are excited by playing a video game, corresponding physiological responses such as sweating or changes in heart rate may appear. It is assumed that presenting physiological responses during game play to players in real-time alters their game experience even when they play the same game. Based on this assumption, this work investigated the effect of false heart rate (HR) feedback on game experience through experiments using a simple action game. Our experimental results indicated that false HR feedback not only prevented the players from becoming tired of the game but also enhanced players' experiences. In addition, a new game controller that can present HR information audibly and tactually was developed for realizing a game system based on false HR feedback.

    DOI: 10.1109/ICCE.2018.8326254

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  75. CFD-Based Design of Focal Brain Cooling System for Suppressing Epileptic Seizures Reviewed

    Kei Hata, Takuto Abe, Takao Inoue, Koichi Fujiwara, Takatomi Kubo, Toshitaka Yamakawa, Sadahiro Nomura, Hirochika Imoto, Michiyasu Suzuki, Manabu Kano

    Computer Aided Chemical Engineering   Vol. 44   page: 2089 - 2094   2018.1

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    © 2018 Elsevier B.V. Epilepsy is a group of neurological disorders which is caused by excessive neuronal activities in cerebrum and characterized by recurrent seizures. A quarter of patients have intractable epilepsy and do not become seizure-free with medication. We are developing an implantable and wearable focal brain cooling system, which enables the patients to lead ordinary daily life. The system cools the epileptic focus, where the excessive neuronal activities begin, by delivering cold saline to a cranially implanted cooling device. In this research, we developed a whole system model through the first principles and animal experiments. The results of system design have shown that a cooling device with more complex channel structure achieves higher temperature uniformity in the brain with lower flow rate of saline. The optimal structure was derived by taking account of the trade-off between pressure drop and temperature uniformity. In addition, the results have demonstrated that the cooling duration is less than 10 minutes for the average temperature 2 mm below the cooling device (inside the brain) to reach 25 °C; it is short enough to cool the brain after seizure is predicted by existing electroencephalogram (EEG)-based algorithms. Moreover, the frequency of battery charging would be once in several days for most patients.

    DOI: 10.1016/B978-0-444-64241-7.50343-8

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  76. Validation of HRV-based drowsy-driving detection method with EEG sleep stage classification Reviewed

    T. Yamakawa, K. Fujiwara, T. Hiraoka, M. Kano, Y. Sumi, F. Masuda, M. Matsuo, H. Kadotani

    Sleep Medicine (Proc. of World Sleep Congress)   Vol. 40   page: e352 - e352   2017.12

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    DOI: 10.1016/j.sleep.2017.11.1038

  77. Seizure prediction in localization-related epilepsy by heart rate variability monitoring Reviewed International journal

    Miyajima M, Fujiwara K, Toshitaka Y, Yoko S, Sasai-Sakuma T, Kano M, Maehara T, Watanabe Y, Watanabe S, Murata Y, Sasano T, Eisuke M

    JOURNAL OF THE NEUROLOGICAL SCIENCES   Vol. 381   page: 554 - 555   2017.10

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    DOI: 10.1016/j.jns.2017.08.3769

  78. A new infarction detection method based on heart rate variability in rat middle cerebral artery occlusion model Reviewed

    Tomonobu Kodata, Keisuke Kamata, Koichi Fujiwara, Manabu Kano, Toshiki Yamakawa, Ichiro Yuki, Yuichi Murayama

    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS     page: 3061 - 3064   2017.9

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    © 2017 IEEE. Objective: The present study proposes a cerebral infarction detection algorithm based on heart rate variability (HRV). Methods: It has been reported that infarction affects HRV. Therefore, infarction could be detected at an acute stage by monitoring HRV. This study uses multivariate statistical process control (MSPC), which is a well-known anomaly monitoring method. HRV data shortly after infarction onsets are collected by using the middle cerebral artery occlusion (MCAO) model in rats. This study prepares 11 MCAO-operated rats and 11 sham-operated rats. Three sham-operated rats' data are used for model construction of MSPC, and the other 19 rats' data are used for its validation. Results: The sensitivity and specificity of the proposed algorithm were 82 % and 75 %, respectively. Conclusion: An infarction onset could be detected at an acute stage by monitoring HRV.

    DOI: 10.1109/EMBC.2017.8037503

    Web of Science

    Scopus

    PubMed

    Other Link: http://orcid.org/0000-0002-2325-1043

  79. Design of focal brain cooling system for suppressing epileptic seizures Reviewed

    Kei Hata, Koichi Fujiwara, Manabu Kano, Takao Inoue, Sadahiro Nomura, Hirochika Imoto, Michiyasu Suzuki

    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS     page: 283 - 286   2017.9

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    © 2017 IEEE. Epilepsy is a group of diseases caused by excessive neuronal activities, and one-quarter of the patients do not become seizure-free by the existing treatments. The potential treatments include focal brain cooling, which aims to cool the region where the excessive neuronal activities begin. We are developing a focal brain cooling system. The system delivers cold saline to a cranially implanted cooling device. The outflow is cooled by a Peltier device and pumped for circulation. The Peltier device and the pump are activated only when a seizure is predicted. In this research, the length of time for cooling the brain was calculated with a computational fluid dynamics (CFD)-based model of the focal brain cooling system. As a result, it takes less than 10 minutes for the average temperature 2 mm below the cooling device to reach 25.0 °C. It is much shorter than the time from seizure prediction to seizure onset when an existing algorithm for prediction is used.

    DOI: 10.1109/EMBC.2017.8036817

    Web of Science

    Scopus

    PubMed

  80. Development of correlation-based process characteristics visualization method and its application to fault detection Reviewed

    Koichi Fujiwara, Manabu Kano

    IEEE International Conference on Control and Automation, ICCA     page: 940 - 945   2017.8

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    © 2017 IEEE. Although process monitoring is important for maintaining safety and product quality, it is difficult to understand process characteristics particularly when they are changing. Since the correlation among variables changes due to changes in process characteristics, process data visualization based on the correlation among variables helps process characteristic understanding. In the present work, a new correlation-based data visualization method is proposed by integrating joint decorrelation (JD) and stochastic proximity embedding (SPE). JD is a blind source separation (BSS) method that can separates sample based on the correlation, and SPE is a self-organizing algorithm that can map high-dimensional data to a two-dimensional plane. The proposed method, referred to as JD-SPE, separates samples based on the correlation using JD and the separated samples are visualized in the two-dimensional plane by SPE. Correlation matrices have to be constructed before sample separation for JD; however how to construct them is not clear. The present work also proposes a correlation matrix construction method for JD by using nearest correlation spectral clustering (NCSC), which is a correlation-based clustering method. In addition, a new process monitoring method based on multivariate statistical process control (MSPC) which is a well-known process monitoring algorithm and JD-SPE. This monitoring method is referred to as JD-SPE-r2. The proposed JD-SPE-Γ2 can detect a fault that can not detected by the conventional MSPC. The usefulness of the proposed methods is demonstrated through numerical examples.

    DOI: 10.1109/ICCA.2017.8003187

    Scopus

  81. 運転中の能動的行為によるドライバの覚醒維持効果と運転安全性 Reviewed International journal

    伊部達郎, 平岡敏洋, 阿部恵里花, 藤原幸一, 山川俊貴

    自動車技術会論文集   Vol. 48 ( 2 ) page: 463 - 469   2017.2

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    Language:Japanese   Publishing type:Research paper (scientific journal)  

    DOI: 10.11351/jsaeronbun.48.463

  82. Comparisons of Portable Sleep Monitors of Different Modalities: Potential as Naturalistic Sleep Recorders Reviewed

    M. Matsuo, F. Masuda, Y. Sumi, M. Takahashi, N. Yamada, M. H. Ohira, K. Fujiwara, T. Kanemura and H. Kadotani

    Frontiers in Neurology     2016.6

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.3389/fneur.2016.00110

    PubMed

  83. Evaluation of a Portable Two-channel Electroencephalogram Monitoring System to Analyze Sleep Stages Reviewed

    T. Kanemura, H. Kadotani, M. Matsuo, F. Masuda, K. Fujiwara , M. Ohira and N. Yamada

    Journal of Oral and Sleep Medicine   Vol. 2   page: 101-108   2016.5

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  84. Development of Drowsiness Detection Method by Integrating Heart Rate Variability Analysis and Multivariate Statistical Process Control Reviewed

    E. Abe, K. Fujiwara, T. Hiraoka, T. Yamakawa and M. Kano

    SICE Journal of Control, Measurement, and System Integration   Vol. 9   page: 10-17   2016.1

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.9746/jcmsi.9.10

  85. Efficient wavenumber selection based on spectral fluctuation dividing and correlation-based clustering for calibration modeling Reviewed

    T. Miyano, K. Fujiwara, M. Kano, H. Tanabe, H. Nakagawa, T. Watanabe, H. Minami

    Chemometrics and Intelligent Laboratory Systems   Vol. 148   page: 85-94   2015.9

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    DOI: 10.1016/j.chemolab.2015.09.009

  86. Efficient input variable selection for soft-senor design based on nearest correlation spectral clustering and group Lasso Reviewed

    Koichi Fujiwara, Manabu Kano

    ISA Transactions   Vol. 58 ( 9 ) page: 367-379   2015.9

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    Appropriate input variables have to be selected for building highly accurate soft sensor. A novel input variable selection method based on nearest correlation spectral clustering (NCSC) has been proposed, and it is referred to as NCSC-based variable selection (NCSC-VS). Although NCSC-VS can select appropriate input variables, a lot of parameters have to be tuned carefully for selecting proper variables. The present work proposes a new methodology for efficient input variable selection by integrating NCSC and group Lasso. The proposed NCSC-based group Lasso (NCSC-GL) can not only reduce the number of tuning parameters but also achieve almost the same performance as NCSC-VS. The usefulness of the proposed NCSC-GL is demonstrated through applications to soft sensor design for a pharmaceutical process and a chemical process.

    DOI: 10.1016/j.isatra.2015.04.007

    Scopus

    PubMed

  87. A Study on Heart Rate Monitoring in Daily Life by Using a Surface-Type Sensor Reviewed

    H. Chigira, A. Maeda, M. Kobayashi, K. Fujiwara, T. Hiraoka, A. Tanaka, T. Tanaka

    SICE Journal of Control, Measurement, and System Integration   Vol. 8 ( 1 ) page: 74-78   2015.1

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.9746/jcmsi.8.74

  88. Virtual sensing technology in process industries: Trends and challenges revealed by recent industrial applications Reviewed

    M. Kano; K. Fujiwara

    Journal of Chemical Engineering of Japan   Vol. 46 ( 1 ) page: 1-17   2013

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1252/jcej.12we167

  89. Development of correlation-based pattern recognition algorithm and adaptive soft-sensor design Reviewed

    K. Fujiwara; M. Kano; S. Hasebe

    Control Engineering Practice   Vol. 20 ( 4 ) page: 371-378   2012.4

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.conengprac.2010.11.013

    J-GLOBAL

  90. Correlation-based spectral clustering for flexible process monitoring Reviewed

    K. Fujiwara; M. Kano; S. Hasebe

    Journal of Process Control   Vol. 21 ( 10 ) page: 1438-1448   2011

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.jprocont.2011.06.023

  91. Development of correlation-based clustering method and its application to software sensing Reviewed

    K. Fujiwara; M. Kano; S. Hasebe

    Chemometrics and Intelligent Laboratory Systems   Vol. 101 ( 2 ) page: 130-138   2010.4

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1016/j.chemolab.2010.02.006

    J-GLOBAL

  92. 相関型 Just-In-Time モデリングによるソフトセンサの設計 Reviewed

    藤原幸一, 加納学, 長谷部伸治

    計測自動制御学会論文集   Vol. 44 ( 4 ) page: 317-324   2008.4

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    Language:Japanese   Publishing type:Research paper (scientific journal)  

    DOI: 10.9746/ve.sicetr1965.44.317

  93. ウェーブレット解析を用いたバッチプロセス操作プロファイルの最適化 Reviewed

    藤原幸一;加納学;長谷部伸治;大野弘

    計測自動制御学会論文集   Vol. 42 ( 10 ) page: 1143-1149   2006.10

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    Language:Japanese   Publishing type:Research paper (scientific journal)  

    DOI: 10.9746/sicetr1965.42.1143

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  94. 運転データに基づく階層型品質改善システムの開発 : 品質制御のための操作変数選択 Reviewed

    藤原幸一, 加納学, 長谷部伸治, 大野弘

    計測自動制御学会論文集   Vol. 42 ( 8 ) page: 909-915   2006.8

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    Language:Japanese   Publishing type:Research paper (scientific journal)  

    DOI: 10.9746/sicetr1965.42.909

  95. 運転データに基づく品質改善のための定性的品質情報の定量化 Reviewed

    加納学;藤原幸一;長谷部伸治;大野弘

    計測自動制御学会論文集   Vol. 42 ( 8 ) page: 902-908   2006.8

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    Language:Japanese   Publishing type:Research paper (scientific journal)  

    DOI: 10.9746/sicetr1965.42.902

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▼display all

Books 2

  1. スモールデータ解析と機械学習

    藤原幸一( Role: Sole author)

    オーム社  2022.2  ( ISBN:4274227782

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    Total pages:304   Responsible for pages:304   Language:Japanese Book type:Scholarly book

  2. 次世代医療AI - 生体信号を介した人とAIの融合 - (計測・制御セレクションシリーズ 1)

    藤原 幸一 (著, 編集), 久保 孝富 (著, 編集), 山川 俊貴 (著), 伊藤 健史 (著), 中野 高志 (著), 吉本 潤一郎 (著), 松尾 剛行 (著), 藤田 卓仙 (著), 桐山 瑶子 (著), 計測自動制御学会 (編集)( Role: Joint author)

    コロナ社  2021.6  ( ISBN:4339033812

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    Total pages:272   Responsible for pages:60   Language:English Book type:Scholarly book

MISC 50

  1. レム睡眠行動障害におけるデルタ・ガンマ帯域パワー値の増大は夢内容行動化と関連する

    伊達 俊坪, 藤原 幸一, 角 幸頼, 角谷 寛, 今井 眞, 小川 景子

    日本睡眠学会定期学術集会プログラム・抄録集   Vol. 47回   page: 241 - 241   2022.6

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    Language:Japanese   Publisher:(一社)日本睡眠学会  

  2. Research on Technological and Social Issues Related to Social Implementation of Embedded Cyborg Technology Invited

    藤原幸一, 藤田卓仙, 山川俊貴, 久保孝富, 日永田智絵, 桐山瑶子, 川島浩誉, 川治徹真, 野田隼人, 田畑淳

    人工知能   Vol. 36 ( 6 ) page: 674 - 683   2021.11

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    Authorship:Lead author, Corresponding author   Language:Japanese  

    J-GLOBAL

  3. 畳み込みニューラルネットワークを用いた睡眠時無呼吸症候群スクリーニング

    王 歩雲, 岩崎 絢子, 藤原 幸一, 永元 哲治, 角 幸頼, 加納 学, 井関 邦敏, 名嘉村 博, 角谷 寛

    日本睡眠学会定期学術集会プログラム・抄録集   Vol. 46回   page: 221 - 221   2021.9

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  4. SST-RUSを用いた睡眠脳波解析による異なる音環境下でのスピンドル出現の評価

    小枝 正汰, 藤原 幸一, 木下 貴文, 角 幸頼, 角谷 寛, 山木 清志, 森島 守人, 川嶋 隆宏

    日本睡眠学会定期学術集会プログラム・抄録集   Vol. 46回   page: 214 - 214   2021.9

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  5. 睡眠脳波に基づく日中の疲労と眠気の鑑別に関する調査

    藤原 幸一, 後藤 有貴, 角 幸頼, 加納 学, 角谷 寛

    日本睡眠学会定期学術集会プログラム・抄録集   Vol. 46回   page: 211 - 211   2021.9

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  6. A Focal-Origin Bilateral Tonic-clonic Seizure Detection Algorithm Based on AI Analysis of Heart Rate Variability

    芹野真郷, 宮島美穂, 藤原幸一, 鈴木陽子, 加納学, 稲次基希, 橋本聡華, 中里信和, 神一敬, 星田徹, 澤井康子, 渡辺裕貴, 山本信二, 岩崎真樹, 前原健寿

    てんかん研究   Vol. 39 ( 2 ) page: 399 - 399   2021.7

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    J-GLOBAL

  7. レム睡眠行動障害研究の進歩 レム睡眠行動障害の自律神経障害

    角 幸頼, 松尾 雅博, 尾関 祐二, 仲山 千佳夫, 藤原 幸一, 角谷 寛

    臨床神経生理学   Vol. 48 ( 5 ) page: 401 - 401   2020.10

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    Language:Japanese   Publisher:(一社)日本臨床神経生理学会  

  8. Sleep Spindle Detection Using RUSBoost and Synchrosqueezed Wavelet Transform Reviewed

    Kinoshita T, Fujiwara K, Kano M, Ogawa K, Sumi Y, Matsuo M, Kadotani H

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society   Vol. 28 ( 2 ) page: 390-398   2020.2

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    DOI: 10.1109/TNSRE.2020.2964597

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  9. Over- and Under-sampling Approach for Extremely Imbalanced and Small Minority Data Problem in Health Record Analysis. Reviewed

    Fujiwara K, Huang Y, Hori K, Nishioji K, Kobayashi M, Kamaguchi M, Kano M

    Frontiers in public health   Vol. 8   page: 178   2020

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    DOI: 10.3389/fpubh.2020.00178

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  10. Development of Sleep Spindle Detection Algorithm by Combining Synchrosqueezed Wavelet Transform and RUSBoost

    藤原幸一, 木下貴文, 角幸頼, 松尾雅博, 小川景子, 加納学, 角谷寛

    人工知能学会全国大会(Web)   Vol. 34th   2020

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  11. レム睡眠行動障害の自律神経障害

    角幸頼, 松尾雅博, 尾関祐二, 仲山千佳夫, 藤原幸一, 角谷寛

    臨床神経生理学(Web)   Vol. 48 ( 5 )   2020

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  12. Obstructive sleep apnea screening by heart rate variability-based apnea/normal respiration discriminant model Reviewed

    Nakayama C, Fujiwara K, Sumi Y, Matsuo M, Kano M, Kadotani H

    Physiological measurement   Vol. 40 ( 12 ) page: 125001   2019.12

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    Language:English  

    DOI: 10.1088/1361-6579/ab57be

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  13. 医師患者関係のトラスト構築に向けたAI活用の可能性

    藤田 卓仙, 江間 有沙, 近藤 諭, 藤原 幸一, 中谷内 一也, 尾藤 誠司

    医療情報学連合大会論文集   Vol. 39回   page: 126 - 128   2019.11

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  14. Development of a Sleep Apnea Detection Algorithm Using Long Short-Term Memory and Heart Rate Variability. Reviewed International journal

    Ayako Iwasaki, Chikao Nakayama, Koichi Fujiwara, Yukiyoshi Sumi, Masahiro Matsuo, Manabu Kano, Hiroshi Kadotani

    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference   Vol. 2019   page: 3964 - 3967   2019.7

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    Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with the increased risk of lifestyle diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with high sensitivity (100%) and specificity (100%).

    DOI: 10.1109/EMBC.2019.8856463

    Web of Science

    PubMed

  15. ウェーブレット・シンクロスクイージング変換とランダムアンダーサンプリングによる高精度睡眠紡錘波検出アルゴリズムの開発 International journal

    藤原 幸一, 木下 貴文, 角 幸頼, 松尾 雅博, 角谷 寛, 加納 学

    日本睡眠学会定期学術集会プログラム・抄録集   Vol. 44回   page: 279 - 279   2019.6

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  16. サポートベクターマシンに基づいた変数重要度による手首アクチグラフによる週末の寝だめ有無の推定および要因検討 International journal

    後藤 有貴, 藤原 幸一, 角 幸頼, 松尾 雅博, 加納 学, 角谷 寛

    日本睡眠学会定期学術集会プログラム・抄録集   Vol. 44回   page: 284 - 284   2019.6

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  17. 心拍変動解析を用いたCPAPの自律神経活動への短期的効果の検証

    仲山 千佳夫, 藤原 幸一, 松尾 雅博, 加納 学, 角谷 寛

    日本睡眠学会定期学術集会プログラム・抄録集   Vol. 44回   page: 220 - 220   2019.6

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  18. てんかん発作検知・予知に関する最新の研究動向 Reviewed

    宮島 美穂, 藤原 幸一, 山川 俊貴

    クリニシアン   Vol. 66 ( 637 ) page: 40 - 45   2019.5

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  19. Epileptic Seizure Suppression by Focal Brain Cooling With Recirculating Coolant Cooling System: Modeling and Simulation Reviewed

    Hata K, Fujiwara K, Inoue T, Abe T, Kubo T, Yamakawa T, Nomura S, Imoto H, Suzuki M, Kano M

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society   Vol. 27 ( 2 ) page: 162-171   2019.2

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    DOI: 10.1109/TNSRE.2019.2891090

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  20. Emotional Contagion From Humans to Dogs Is Facilitated by Duration of Ownership. Reviewed

    Katayama M, Kubo T, Yamakawa T, Fujiwara K, Nomoto K, Ikeda K, Mogi K, Nagasawa M, Kikusui T

    Frontiers in psychology   Vol. 10   page: 1678   2019

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    DOI: 10.3389/fpsyg.2019.01678

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  21. Sleep Apnea Detection by Combining Long Short-Term Memory and Heart Rate Variability International journal

    IWASAKI Ayako, NAKAYAMA Chikao, FUJIWARA Koichi, SUMI Yukiyoshi, MATSUO Masahiro, KANO Manabu, KADOTANI Hiroshi

    Proceedings of the Annual Conference of JSAI   Vol. 2019 ( 0 ) page: 1H4J1303 - 1H4J1303   2019

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    Language:Japanese   Publisher:The Japanese Society for Artificial Intelligence  

    <p>Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with increased risk of cardiovascular diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of the neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with sensitivity (100%) and specificity (100%).</p>

    DOI: 10.11517/pjsai.JSAI2019.0_1H4J1303

    CiNii Research

    J-GLOBAL

  22. シンクロスクイージングウェーブレット変換とRUSBoostによる睡眠紡錘波検出アルゴリズム

    藤原幸一, 木下貴文, 角幸頼, 松尾雅博, 角谷寛, 加納学

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)   Vol. 2019   2019

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  23. 手首アクチグラフによる週末の寝だめ有無の推定および変数重要度に基づいた要因検討

    後藤有貴, 藤原幸一, 角幸頼, 松尾雅博, 加納学, 角谷寛

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)   Vol. 2019   2019

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  24. Analysis of VNS Effect on EEG Connectivity with Granger Causality and Graph Theory Reviewed International journal

    T. Uchida, K. Fujiwara, T. Inoue, Y. Maruta, M. Kano, M. Suzuki

    APSIPA ASC 2018     2018.11

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  25. Denoising Autoencoder-based Modification of RRI data with Premature Ventricular Contraction for Precise Heart Rate Variability Analysis Reviewed International journal

    S. Miyatani, K. Fujiwara, M. Kano

    IEEE EMBC 2018     2018.7

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  26. Deniosing Autoencoder-based Modification of RRI data with Premature Ventricular Contraction for Precise Heart Rate Variability Analysis. Reviewed

    Miyatani S, Fujiwara K, Kano M

    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference   Vol. 2018   page: 5018-5021   2018.7

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    Language:English  

    DOI: 10.1109/EMBC.2018.8513218

    PubMed

  27. 睡眠時無呼吸症候群患者における多変量統計的プロセス管理と心拍変動解析を用いた持続陽圧呼吸療法の自律神経活動への短期的効果の検証

    仲山 千佳夫, 藤原 幸一, 松尾 雅博, 角谷 寛, 加納 学

    日本睡眠学会定期学術集会プログラム・抄録集   Vol. 43回   page: 200 - 200   2018.7

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  28. RRI Modification by Integrating of the Combination of Singular Spectrum Analysis and Denoising Autoencoder and its Application to Open Data International journal

    宮谷将太, 藤原幸一, 加納学

    システム制御情報学会研究発表講演会講演論文集(CD-ROM)   Vol. 62nd   page: ROMBUNNO.137‐7   2018.5

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  29. Optimization of channel structure and operating condition of neuroprotective focal brain cooling device International journal

    阿部拓斗, 井上貴雄, 藤原幸一, 野村貞宏, 井本浩哉, 鈴木倫保, 加納学

    システム制御情報学会研究発表講演会講演論文集(CD-ROM)   Vol. 62nd   page: ROMBUNNO.212‐6   2018.5

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  30. Design of false heart rate feedback system for improving game experience Reviewed International journal

    Sayaka Ogawa, Koichi Fujiwara, Toshitaka Yamakawa, Erika Abe, Manabu Kano

    2018 IEEE International Conference on Consumer Electronics, ICCE 2018   Vol. 2018-January   page: 1 - 4   2018.3

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    © 2018 IEEE. When players are excited by playing a video game, corresponding physiological responses such as sweating or changes in heart rate may appear. It is assumed that presenting physiological responses during game play to players in real-time alters their game experience even when they play the same game. Based on this assumption, this work investigated the effect of false heart rate (HR) feedback on game experience through experiments using a simple action game. Our experimental results indicated that false HR feedback not only prevented the players from becoming tired of the game but also enhanced players' experiences. In addition, a new game controller that can present HR information audibly and tactually was developed for realizing a game system based on false HR feedback.

    DOI: 10.1109/ICCE.2018.8326254

    Scopus

  31. Accurate softsensor development by nearest corrleation based-variable weighting International journal

    藤原幸一, 加納学

    計測自動制御学会制御部門マルチシンポジウム(CD-ROM)   Vol. 5th   page: ROMBUNNO.Sa41‐4   2018.3

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  32. Comparison of causal inference methods for time series data of nonlinear systems International journal

    和田拓也, 藤原幸一, 加納学

    計測自動制御学会制御部門マルチシンポジウム(CD-ROM)   Vol. 5th   page: ROMBUNNO.Fr43‐3   2018.3

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  33. Causal analysis based on non-time-series kernel Granger causality in a steelmaking process Reviewed International journal

    Ryosuke Sato, Koichi Fujiwara, Masahiro Tani, Junichi Mori, Junji Ise, Kohhei Harada, Manabu Kano

    2017 Asian Control Conference, ASCC 2017   Vol. 2018-January   page: 1778 - 1782   2018.2

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    © 2017 IEEE. In the manufacturing industry, it is extremely important to identify variables that affect product quality. Identifying variables which affect quality variables is called causal analysis. In batch processes, time-series data of process variables and the corresponding data of quality variables are generally acquired. Since causal analysis using the raw data needs a large computation load, it is often performed after compressing time-series process variables data into non-time-series feature variables data. Various causal analysis methods using such data have been developed, however, none have shown effective results in actual plants. In the present work, non-time-series kernel Granger causality (NTS-KGC) is proposed for causal analysis with non-time-series data of batch processes. This is a method that kernel Granger causality [1], which is used for causal analysis with time-series data in nonlinear systems, is expanded for causal analysis with non-time-series data. The validity of the proposed method is demonstrated through a numerical example of a nonlinear batch process. In addition, we conducted a case study of applying NTS-KGC to data obtained from a real steelmaking process. The results demonstrate that NTS-KGC is superior to other existing methods using the following indexes, i.e. variable influence on projection (VIP) of partial least squares (PLS), regression coefficients of PLS, and variable importance of Random Forest.

    DOI: 10.1109/ASCC.2017.8287443

    Scopus

    Other Link: http://orcid.org/0000-0002-2325-1043

  34. CFD-Based Design of Focal Brain Cooling System for Suppressing Epileptic Seizures International journal

    Kei Hata, Takuto Abe, Takao Inoue, Koichi Fujiwara, Takatomi Kubo, Toshitaka Yamakawa, Sadahiro Nomura, Hirochika Imoto, Michiyasu Suzuki, Manabu Kano

    Computer Aided Chemical Engineering   Vol. 44   page: 2089 - 2094   2018.1

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    © 2018 Elsevier B.V. Epilepsy is a group of neurological disorders which is caused by excessive neuronal activities in cerebrum and characterized by recurrent seizures. A quarter of patients have intractable epilepsy and do not become seizure-free with medication. We are developing an implantable and wearable focal brain cooling system, which enables the patients to lead ordinary daily life. The system cools the epileptic focus, where the excessive neuronal activities begin, by delivering cold saline to a cranially implanted cooling device. In this research, we developed a whole system model through the first principles and animal experiments. The results of system design have shown that a cooling device with more complex channel structure achieves higher temperature uniformity in the brain with lower flow rate of saline. The optimal structure was derived by taking account of the trade-off between pressure drop and temperature uniformity. In addition, the results have demonstrated that the cooling duration is less than 10 minutes for the average temperature 2 mm below the cooling device (inside the brain) to reach 25 °C; it is short enough to cool the brain after seizure is predicted by existing electroencephalogram (EEG)-based algorithms. Moreover, the frequency of battery charging would be once in several days for most patients.

    DOI: 10.1016/B978-0-444-64241-7.50343-8

    Scopus

  35. 心拍数変動解析と多変量統計的プロセス管理を用いたウェアラブルてんかん発作予知システムの開発 Reviewed

    山川 俊貴, 宮島 美穂, 藤原 幸一, 加納 学, 鈴木 陽子, 渡辺 裕貴, 渡邊 さつき, 村田 佳子, 星田 徹, 前原 健寿

    てんかん研究   Vol. 35 ( 3 ) page: 730 - 730   2018.1

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  36. Nearest Correlation-Based Input Variable Weighting for Soft-Sensor Design. Reviewed

    Fujiwara K, Kano M

    Frontiers in chemistry   Vol. 6   page: 171   2018

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    DOI: 10.3389/fchem.2018.00171

    PubMed

  37. Generalized epileptic seizure prediction based on HRV analysis and its mechanism consideration International journal

    SAKANE FUMIYA, Fujiwara Koichi, Miyajima Miho, Suzuki Yoko, Yamakawa Toshitaka, Kano Manabu, Maehara Taketoshi

    Transactions of Japanese Society for Medical and Biological Engineering   Vol. 56 ( 0 ) page: S59 - S59   2018

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    &lt;p&gt;The present study aims to predict generalized seizures based on heart rate variability (HRV) analysis and multivariate statistical process control (MSPC). We applied the existing anomaly monitoring algorithm, i.e. HRV-based MSPC developed by Fujiwara et al. to 17 pre-ictal episodes and 74 inter-ictal episodes whose total length were about 63 hours. Consequently, the sensitivity and the false positive rate were 76.5% and 1.39 times per hour respectively, and the proportion of duration under false alarms was 5.96%. These results suggest that generalized seizures may be predicted by analyzing HRV. Based on the previous research, we hypothesize that the change in the autonomic nervous activity induce generalized epileptic seizures.&lt;/p&gt;

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  38. 中大脳動脈閉塞ラットモデルを用いた心拍変動解析による脳卒中早期検知システム実現性の検証 International journal

    藤原幸一, 鎌田啓輔, 児玉智信, 加納学, 村山雄一, 結城一郎

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)   Vol. 2017   page: ROMBUNNO.SS05‐9   2017.11

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  39. 心拍変動解析と多変量統計的プロセス管理による全般性てんかん発作予測 International journal

    坂根史弥, 藤原幸一, 宮島美穂, 鈴木陽子, 山川俊貴, 加納学, 前原健寿

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)   Vol. 2017   page: ROMBUNNO.SS05‐7   2017.11

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  40. 次第に速くなる心拍音提示によるゲーム体験の向上 International journal

    小川紗也加, 藤原幸一, 山川俊貴, 阿部恵里花, 加納学

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)   Vol. 2017   page: ROMBUNNO.SS05‐12   2017.11

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  41. 睡眠時無呼吸症候群患者における持続陽圧呼吸療法の心拍への短期的効果 International journal

    藤原幸一, 仲山千佳夫, 松尾雅博, 加納学, 角谷寛

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)   Vol. 2017   page: ROMBUNNO.SS05‐8   2017.11

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  42. ウェアラブルデバイスとスマートフォンを用いたてんかん発作予知技術

    藤原 幸一, 宮島 美穂, 山川 俊貴

    Epilepsy: てんかんの総合学術誌   Vol. 11 ( 2 ) page: 75 - 81   2017.11

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    CiNii Books

    Other Link: http://search.jamas.or.jp/link/ui/2017406986

  43. Validation of HRV-Based Drowsy-Driving Detection Method with EEG Sleep Stage Classification Reviewed International journal

    T. Yamakawa, K. Fujiwara, T. Hiraoka, M. Kano, Y. Sumi, F. Masuda, M. Matsuo, H. Kadotani

    Proc. of World Sleep Congress     2017.10

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  44. てんかん発作抑制を目指した局所脳冷却システムの設計 International journal

    畑啓, 藤原幸一, 加納学, 井上貴雄, 野村貞宏, 井本浩哉, 鈴木倫保

    化学工学会秋季大会研究発表講演要旨集(CD-ROM)   Vol. 49th   page: ROMBUNNO.S307   2017.9

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  45. A new infarction detection method based on heart rate variability in rat middle cerebral artery occlusion model Reviewed International journal

    Tomonobu Kodata, Keisuke Kamata, Koichi Fujiwara, Manabu Kano, Toshiki Yamakawa, Ichiro Yuki, Yuichi Murayama

    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS   Vol. 2017   page: 3061 - 3064   2017.9

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    © 2017 IEEE. Objective: The present study proposes a cerebral infarction detection algorithm based on heart rate variability (HRV). Methods: It has been reported that infarction affects HRV. Therefore, infarction could be detected at an acute stage by monitoring HRV. This study uses multivariate statistical process control (MSPC), which is a well-known anomaly monitoring method. HRV data shortly after infarction onsets are collected by using the middle cerebral artery occlusion (MCAO) model in rats. This study prepares 11 MCAO-operated rats and 11 sham-operated rats. Three sham-operated rats' data are used for model construction of MSPC, and the other 19 rats' data are used for its validation. Results: The sensitivity and specificity of the proposed algorithm were 82 % and 75 %, respectively. Conclusion: An infarction onset could be detected at an acute stage by monitoring HRV.

    DOI: 10.1109/EMBC.2017.8037503

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    PubMed

  46. Design of focal brain cooling system for suppressing epileptic seizures Reviewed International journal

    Kei Hata, Koichi Fujiwara, Manabu Kano, Takao Inoue, Sadahiro Nomura, Hirochika Imoto, Michiyasu Suzuki

    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS   Vol. 2017   page: 283 - 286   2017.9

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    © 2017 IEEE. Epilepsy is a group of diseases caused by excessive neuronal activities, and one-quarter of the patients do not become seizure-free by the existing treatments. The potential treatments include focal brain cooling, which aims to cool the region where the excessive neuronal activities begin. We are developing a focal brain cooling system. The system delivers cold saline to a cranially implanted cooling device. The outflow is cooled by a Peltier device and pumped for circulation. The Peltier device and the pump are activated only when a seizure is predicted. In this research, the length of time for cooling the brain was calculated with a computational fluid dynamics (CFD)-based model of the focal brain cooling system. As a result, it takes less than 10 minutes for the average temperature 2 mm below the cooling device to reach 25.0 °C. It is much shorter than the time from seizure prediction to seizure onset when an existing algorithm for prediction is used.

    DOI: 10.1109/EMBC.2017.8036817

    Scopus

    PubMed

  47. ヘルスモニタリングのための心拍変動解析 Invited Reviewed

    藤原 幸一

    システム/制御/情報   Vol. 61 ( 9 ) page: 381 - 386   2017.9

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    DOI: 10.11509/isciesci.61.9_381

  48. Development of correlation-based process characteristics visualization method and its application to fault detection Reviewed International journal

    Koichi Fujiwara, Manabu Kano

    IEEE International Conference on Control and Automation, ICCA     page: 940 - 945   2017.8

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    © 2017 IEEE. Although process monitoring is important for maintaining safety and product quality, it is difficult to understand process characteristics particularly when they are changing. Since the correlation among variables changes due to changes in process characteristics, process data visualization based on the correlation among variables helps process characteristic understanding. In the present work, a new correlation-based data visualization method is proposed by integrating joint decorrelation (JD) and stochastic proximity embedding (SPE). JD is a blind source separation (BSS) method that can separates sample based on the correlation, and SPE is a self-organizing algorithm that can map high-dimensional data to a two-dimensional plane. The proposed method, referred to as JD-SPE, separates samples based on the correlation using JD and the separated samples are visualized in the two-dimensional plane by SPE. Correlation matrices have to be constructed before sample separation for JD; however how to construct them is not clear. The present work also proposes a correlation matrix construction method for JD by using nearest correlation spectral clustering (NCSC), which is a correlation-based clustering method. In addition, a new process monitoring method based on multivariate statistical process control (MSPC) which is a well-known process monitoring algorithm and JD-SPE. This monitoring method is referred to as JD-SPE-r2. The proposed JD-SPE-Γ2 can detect a fault that can not detected by the conventional MSPC. The usefulness of the proposed methods is demonstrated through numerical examples.

    DOI: 10.1109/ICCA.2017.8003187

    Scopus

  49. 多変量統計的プロセス管理と心拍変動解析を用いたてんかん発作予知技術の開発 Invited Reviewed

    藤原幸一, 宮島美穂, 鈴木陽子, 山川俊貴, 加納学

    計測と制御   Vol. 56 ( 7 ) page: 526 - 529   2017.7

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    DOI: 10.11499/sicejl.56.526

  50. 心拍変動解析と多変量統計的プロセス管理に基づく全般性てんかん発作予測 International journal

    坂根史弥, 藤原幸一, 宮島美穂, 鈴木陽子, 山川俊貴, 加納学, 前原健寿

    システム制御情報学会研究発表講演会講演論文集(CD-ROM)   Vol. 61st   page: ROMBUNNO.134‐2   2017.5

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Presentations 75

  1. AI model for predicting postoperative pain exacerbation using a wearable electrocardiogram sensor and an intravenous patient-controlled analgesia device

    NAKANISHI Toshiyuki, FUJIWARA Koichi, SENTO Yoshiki, SOBUE Kazuya

    Proceedings of the Annual Conference of JSAI  2023  The Japanese Society for Artificial Intelligence

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    Language:Japanese   Presentation type:Oral presentation (general)  

    There is a need to develop objective and real-time postoperative pain assessment methods in perioperative medicine. Few studies have evaluated the relationship between pain severity and temporal changes of physiological signals in actual postoperative patients. The aim of the study was to evaluate postoperative pain continuously and to predict pain exacerbation in real-time. We focused on intravenous patient-controlled analgesia (IV-PCA), a common analgesic modality utilized in post-surgical patients. We chose an electrocardiogram (ECG) as a feature to detect pain exacerbation. We developed a machine learning model which was trained from IV-PCA records and ECG of postoperative patients to predict pain exacerbation. A self-attentive autoencoder (SA-AE) model achieved 54% of sensitivity and a 1.76 times/h of false positive rate. In summary, we propose a novel pain evaluation method using an IV-PCA device. According to the current findings, ECG features may be used to predict postoperative pain exacerbation in real-time.

  2. Nearest Neighbor Search-Based Modification of RRI Data with Premature Atrial Contraction and Premature Ventricular Contraction

    Sifeng Chen, Shota Kato, Koichi Fujiwara, Manabu Kano

    2023 SICE INTERNATIONAL SYMPOSIUM ON CONTROL SYSTEMS, SICE ISCS  2023  IEEE

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    Language:English   Presentation type:Oral presentation (general)  

    Heart rate variability (HRV) analysis plays an essential role in healthcare. HRV features cannot be extracted accurately from the R-R interval (RRI) when RRI data contains artifacts. Previous research for modifying RRI data with artifacts considered premature atrial contraction (PAC) and premature ventricular contraction (PVC), which are the most common types of extrasystoles occurring every day in healthy persons. This research proposed three new RRI modification algorithms for PAC and PVC using nearest neighbor search (NNS) algorithms: k-nearest neighbors (KNN), clustering-KNN (CKNN), and approximate nearest neighbors (ANN). The present work demonstrated that the ANN-based RRI modification (ANN-RM) algorithm achieved lower root mean squared errors (RMSEs) than the CKNN-based RRI modification algorithm and the highest computational speed. The RMSEs of ANN-RM for PAC and PVC were 23.0 ms and 26.2 ms, respectively.

  3. レム睡眠行動障害におけるデルタ・ガンマ帯域パワー値の増大は夢内容行動化と関連する

    伊達 俊坪, 藤原 幸一, 角 幸頼, 角谷 寛, 今井 眞, 小川 景子

    日本睡眠学会定期学術集会プログラム・抄録集  2022.6  (一社)日本睡眠学会

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  4. レム睡眠行動障害におけるデルタ・ガンマ帯域パワー値の増大は夢内容行動化と関連する

    伊達 俊坪, 藤原 幸一, 角 幸頼, 角谷 寛, 今井 眞, 小川 景子

    日本睡眠学会定期学術集会プログラム・抄録集  2022.6  (一社)日本睡眠学会

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  5. 畳み込みニューラルネットワークを用いた睡眠時無呼吸症候群スクリーニング

    王 歩雲, 岩崎 絢子, 藤原 幸一, 永元 哲治, 角 幸頼, 加納 学, 井関 邦敏, 名嘉村 博, 角谷 寛

    日本睡眠学会定期学術集会プログラム・抄録集  2021.9  (一社)日本睡眠学会

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    Event date: 2021.9

    Language:Japanese   Presentation type:Oral presentation (general)  

  6. 畳み込みニューラルネットワークを用いた睡眠時無呼吸症候群スクリーニング

    王 歩雲, 岩崎 絢子, 藤原 幸一, 永元 哲治, 角 幸頼, 加納 学, 井関 邦敏, 名嘉村 博, 角谷 寛

    日本睡眠学会定期学術集会プログラム・抄録集  2021.9  (一社)日本睡眠学会

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  7. 睡眠脳波に基づく日中の疲労と眠気の鑑別に関する調査

    藤原 幸一, 後藤 有貴, 角 幸頼, 加納 学, 角谷 寛

    日本睡眠学会定期学術集会プログラム・抄録集  2021.9  (一社)日本睡眠学会

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    Language:Japanese   Presentation type:Oral presentation (general)  

  8. 睡眠脳波に基づく日中の疲労と眠気の鑑別に関する調査

    藤原 幸一, 後藤 有貴, 角 幸頼, 加納 学, 角谷 寛

    日本睡眠学会定期学術集会プログラム・抄録集  2021.9  (一社)日本睡眠学会

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  9. SST-RUSを用いた睡眠脳波解析による異なる音環境下でのスピンドル出現の評価

    小枝 正汰, 藤原 幸一, 木下 貴文, 角 幸頼, 角谷 寛, 山木 清志, 森島 守人, 川嶋 隆宏

    日本睡眠学会定期学術集会プログラム・抄録集  2021.9  (一社)日本睡眠学会

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  10. SST-RUSを用いた睡眠脳波解析による異なる音環境下でのスピンドル出現の評価

    小枝 正汰, 藤原 幸一, 木下 貴文, 角 幸頼, 角谷 寛, 山木 清志, 森島 守人, 川嶋 隆宏

    日本睡眠学会定期学術集会プログラム・抄録集  2021.9  (一社)日本睡眠学会

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  11. A Focal-Origin Bilateral Tonic-clonic Seizure Detection Algorithm Based on AI Analysis of Heart Rate Variability

    芹野真郷, 宮島美穂, 藤原幸一, 鈴木陽子, 加納学, 稲次基希, 橋本聡華, 中里信和, 神一敬, 星田徹, 澤井康子, 渡辺裕貴, 山本信二, 岩崎真樹, 前原健寿

    てんかん研究  2021 

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    Language:English   Presentation type:Oral presentation (general)  

  12. Preliminary Study Using Autoencoder for Early Detection of Heat Illness from Heart Rate Variability Obtained with Wearable Device.

    Nao Inatsu, Aoi Noguchi, Koshi Ota, Koichi Fujiwara, Takatomi Kubo, Toshitaka Yamakawa

    APSIPA ASC  2021 

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    Language:English   Presentation type:Oral presentation (general)  

    Other Link: https://dblp.uni-trier.de/rec/conf/apsipa/2021

  13. レム睡眠行動障害研究の進歩 レム睡眠行動障害の自律神経障害

    角 幸頼, 松尾 雅博, 尾関 祐二, 仲山 千佳夫, 藤原 幸一, 角谷 寛

    臨床神経生理学  2020.10  (一社)日本臨床神経生理学会

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  14. レム睡眠行動障害研究の進歩 レム睡眠行動障害の自律神経障害

    角 幸頼, 松尾 雅博, 尾関 祐二, 仲山 千佳夫, 藤原 幸一, 角谷 寛

    臨床神経生理学  2020.10  (一社)日本臨床神経生理学会

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    Language:Japanese   Presentation type:Oral presentation (general)  

  15. Development of Sleep Spindle Detection Algorithm by Combining Synchrosqueezed Wavelet Transform and RUSBoost

    藤原幸一, 木下貴文, 角幸頼, 松尾雅博, 小川景子, 加納学, 角谷寛

    人工知能学会全国大会(Web)  2020 

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    Event date: 2020

    Language:English   Presentation type:Oral presentation (general)  

  16. レム睡眠行動障害の自律神経障害

    角幸頼, 松尾雅博, 尾関祐二, 仲山千佳夫, 藤原幸一, 角谷寛

    臨床神経生理学(Web)  2020 

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    Language:English   Presentation type:Oral presentation (general)  

  17. Views of patients with epilepsy on wearable seizure prediction system; impact of two different type of devices on sleep quality

    M. Miyajima, T. Yamakawa, K. Fujiwara, T. Seki, T. ohno, M. Iimori, M. Inaji, H. Osoegawa, M. Kano, T. Maehara

    Sleep Medicine  2019.12  Elsevier {BV}

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    Language:English   Presentation type:Oral presentation (general)  

  18. 医師患者関係のトラスト構築に向けたAI活用の可能性

    藤田 卓仙, 江間 有沙, 近藤 諭, 藤原 幸一, 中谷内 一也, 尾藤 誠司

    医療情報学連合大会論文集  2019.11  (一社)日本医療情報学会

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    Language:Japanese   Presentation type:Oral presentation (general)  

  19. てんかん患者におけるウェアラブル自律神経機能モニタリングの試み てんかん突然死のリスク評価を目指し

    宮島 美穂, 山川 俊貴, 藤原 幸一, 関 拓哉, 稲次 基希, 橋本 聡華, 岩崎 真樹, 長綱 敏和, 藤井 正美, 山本 信二, 加納 学, 前原 健寿

    てんかん研究  2019.9  (一社)日本てんかん学会

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    Language:Japanese   Presentation type:Oral presentation (general)  

  20. Development of a Sleep Apnea Detection Algorithm Using Long Short-Term Memory and Heart Rate Variability.

    Ayako Iwasaki, Chikao Nakayama, Koichi Fujiwara, Yukiyoshi Sumi, Masahiro Matsuo, Manabu Kano, Hiroshi Kadotani

    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference  2019.7 

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    Event date: 2019.7

    Language:English   Presentation type:Oral presentation (general)  

    Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with the increased risk of lifestyle diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with high sensitivity (100%) and specificity (100%).

  21. 心拍変動解析を用いたCPAPの自律神経活動への短期的効果の検証

    仲山 千佳夫, 藤原 幸一, 松尾 雅博, 加納 学, 角谷 寛

    日本睡眠学会定期学術集会プログラム・抄録集  2019.6  (一社)日本睡眠学会

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    Language:Japanese   Presentation type:Oral presentation (general)  

  22. ウェーブレット・シンクロスクイージング変換とランダムアンダーサンプリングによる高精度睡眠紡錘波検出アルゴリズムの開発

    藤原 幸一, 木下 貴文, 角 幸頼, 松尾 雅博, 角谷 寛, 加納 学

    日本睡眠学会定期学術集会プログラム・抄録集  2019.6  (一社)日本睡眠学会

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    Language:Japanese   Presentation type:Oral presentation (general)  

  23. サポートベクターマシンに基づいた変数重要度による手首アクチグラフによる週末の寝だめ有無の推定および要因検討

    後藤 有貴, 藤原 幸一, 角 幸頼, 松尾 雅博, 加納 学, 角谷 寛

    日本睡眠学会定期学術集会プログラム・抄録集  2019.6  (一社)日本睡眠学会

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    Language:Japanese   Presentation type:Oral presentation (general)  

  24. Sleep Apnea Detection by Combining Long Short-Term Memory and Heart Rate Variability

    IWASAKI Ayako, NAKAYAMA Chikao, FUJIWARA Koichi, SUMI Yukiyoshi, MATSUO Masahiro, KANO Manabu, KADOTANI Hiroshi

    Proceedings of the Annual Conference of JSAI  2019  The Japanese Society for Artificial Intelligence

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    Event date: 2019

    Language:Japanese   Presentation type:Oral presentation (general)  

    <p>Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with increased risk of cardiovascular diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of the neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with sensitivity (100%) and specificity (100%).</p>

  25. 手首アクチグラフによる週末の寝だめ有無の推定および変数重要度に基づいた要因検討

    後藤有貴, 藤原幸一, 角幸頼, 松尾雅博, 加納学, 角谷寛

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)  2019 

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    Language:English   Presentation type:Oral presentation (general)  

  26. シンクロスクイージングウェーブレット変換とRUSBoostによる睡眠紡錘波検出アルゴリズム

    藤原幸一, 木下貴文, 角幸頼, 松尾雅博, 角谷寛, 加納学

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)  2019 

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    Language:English   Presentation type:Oral presentation (general)  

  27. Analysis of VNS Effect on EEG Connectivity with Granger Causality and Graph Theory

    T. Uchida, K. Fujiwara, T. Inoue, Y. Maruta, M. Kano, M. Suzuki

    APSIPA ASC 2018  2018.11 

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    Language:English   Presentation type:Oral presentation (general)  

  28. 睡眠時無呼吸症候群患者における多変量統計的プロセス管理と心拍変動解析を用いた持続陽圧呼吸療法の自律神経活動への短期的効果の検証

    仲山 千佳夫, 藤原 幸一, 松尾 雅博, 角谷 寛, 加納 学

    日本睡眠学会定期学術集会プログラム・抄録集  2018.7  (一社)日本睡眠学会

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    Language:Japanese   Presentation type:Oral presentation (general)  

  29. Denoising Autoencoder-based Modification of RRI data with Premature Ventricular Contraction for Precise Heart Rate Variability Analysis

    S. Miyatani, K. Fujiwara, M. Kano

    IEEE EMBC 2018  2018.7 

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    Language:English   Presentation type:Oral presentation (general)  

  30. Optimization of channel structure and operating condition of neuroprotective focal brain cooling device

    阿部拓斗, 井上貴雄, 藤原幸一, 野村貞宏, 井本浩哉, 鈴木倫保, 加納学

    システム制御情報学会研究発表講演会講演論文集(CD-ROM)  2018.5.16 

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    Language:Japanese   Presentation type:Oral presentation (general)  

  31. RRI Modification by Integrating of the Combination of Singular Spectrum Analysis and Denoising Autoencoder and its Application to Open Data

    宮谷将太, 藤原幸一, 加納学

    システム制御情報学会研究発表講演会講演論文集(CD-ROM)  2018.5.16 

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    Language:Japanese   Presentation type:Oral presentation (general)  

  32. Design of false heart rate feedback system for improving game experience

    Sayaka Ogawa, Koichi Fujiwara, Toshitaka Yamakawa, Erika Abe, Manabu Kano

    2018 IEEE International Conference on Consumer Electronics, ICCE 2018  2018.3.26 

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    Language:English   Presentation type:Oral presentation (general)  

    © 2018 IEEE. When players are excited by playing a video game, corresponding physiological responses such as sweating or changes in heart rate may appear. It is assumed that presenting physiological responses during game play to players in real-time alters their game experience even when they play the same game. Based on this assumption, this work investigated the effect of false heart rate (HR) feedback on game experience through experiments using a simple action game. Our experimental results indicated that false HR feedback not only prevented the players from becoming tired of the game but also enhanced players' experiences. In addition, a new game controller that can present HR information audibly and tactually was developed for realizing a game system based on false HR feedback.

  33. Comparison of causal inference methods for time series data of nonlinear systems

    和田拓也, 藤原幸一, 加納学

    計測自動制御学会制御部門マルチシンポジウム(CD-ROM)  2018.3.8 

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    Language:Japanese   Presentation type:Oral presentation (general)  

  34. Accurate softsensor development by nearest corrleation based-variable weighting

    藤原幸一, 加納学

    計測自動制御学会制御部門マルチシンポジウム(CD-ROM)  2018.3.8 

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    Language:Japanese   Presentation type:Oral presentation (general)  

  35. Causal analysis based on non-time-series kernel Granger causality in a steelmaking process

    Ryosuke Sato, Koichi Fujiwara, Masahiro Tani, Junichi Mori, Junji Ise, Kohhei Harada, Manabu Kano

    2017 Asian Control Conference, ASCC 2017  2018.2.7 

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    Language:English   Presentation type:Oral presentation (general)  

    © 2017 IEEE. In the manufacturing industry, it is extremely important to identify variables that affect product quality. Identifying variables which affect quality variables is called causal analysis. In batch processes, time-series data of process variables and the corresponding data of quality variables are generally acquired. Since causal analysis using the raw data needs a large computation load, it is often performed after compressing time-series process variables data into non-time-series feature variables data. Various causal analysis methods using such data have been developed, however, none have shown effective results in actual plants. In the present work, non-time-series kernel Granger causality (NTS-KGC) is proposed for causal analysis with non-time-series data of batch processes. This is a method that kernel Granger causality [1], which is used for causal analysis with time-series data in nonlinear systems, is expanded for causal analysis with non-time-series data. The validity of the proposed method is demonstrated through a numerical example of a nonlinear batch process. In addition, we conducted a case study of applying NTS-KGC to data obtained from a real steelmaking process. The results demonstrate that NTS-KGC is superior to other existing methods using the following indexes, i.e. variable influence on projection (VIP) of partial least squares (PLS), regression coefficients of PLS, and variable importance of Random Forest.

  36. CFD-Based Design of Focal Brain Cooling System for Suppressing Epileptic Seizures

    Kei Hata, Takuto Abe, Takao Inoue, Koichi Fujiwara, Takatomi Kubo, Toshitaka Yamakawa, Sadahiro Nomura, Hirochika Imoto, Michiyasu Suzuki, Manabu Kano

    Computer Aided Chemical Engineering  2018.1.1 

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    © 2018 Elsevier B.V. Epilepsy is a group of neurological disorders which is caused by excessive neuronal activities in cerebrum and characterized by recurrent seizures. A quarter of patients have intractable epilepsy and do not become seizure-free with medication. We are developing an implantable and wearable focal brain cooling system, which enables the patients to lead ordinary daily life. The system cools the epileptic focus, where the excessive neuronal activities begin, by delivering cold saline to a cranially implanted cooling device. In this research, we developed a whole system model through the first principles and animal experiments. The results of system design have shown that a cooling device with more complex channel structure achieves higher temperature uniformity in the brain with lower flow rate of saline. The optimal structure was derived by taking account of the trade-off between pressure drop and temperature uniformity. In addition, the results have demonstrated that the cooling duration is less than 10 minutes for the average temperature 2 mm below the cooling device (inside the brain) to reach 25 °C; it is short enough to cool the brain after seizure is predicted by existing electroencephalogram (EEG)-based algorithms. Moreover, the frequency of battery charging would be once in several days for most patients.

  37. 心拍数変動解析と多変量統計的プロセス管理を用いたウェアラブルてんかん発作予知システムの開発

    山川 俊貴, 宮島 美穂, 藤原 幸一, 加納 学, 鈴木 陽子, 渡辺 裕貴, 渡邊 さつき, 村田 佳子, 星田 徹, 前原 健寿

    てんかん研究  2018.1  (一社)日本てんかん学会

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  38. Generalized epileptic seizure prediction based on HRV analysis and its mechanism consideration

    SAKANE FUMIYA, Fujiwara Koichi, Miyajima Miho, Suzuki Yoko, Yamakawa Toshitaka, Kano Manabu, Maehara Taketoshi

    Transactions of Japanese Society for Medical and Biological Engineering  2018  Japanese Society for Medical and Biological Engineering

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    Language:Japanese   Presentation type:Oral presentation (general)  

    &lt;p&gt;The present study aims to predict generalized seizures based on heart rate variability (HRV) analysis and multivariate statistical process control (MSPC). We applied the existing anomaly monitoring algorithm, i.e. HRV-based MSPC developed by Fujiwara et al. to 17 pre-ictal episodes and 74 inter-ictal episodes whose total length were about 63 hours. Consequently, the sensitivity and the false positive rate were 76.5% and 1.39 times per hour respectively, and the proportion of duration under false alarms was 5.96%. These results suggest that generalized seizures may be predicted by analyzing HRV. Based on the previous research, we hypothesize that the change in the autonomic nervous activity induce generalized epileptic seizures.&lt;/p&gt;

  39. 睡眠時無呼吸症候群患者における持続陽圧呼吸療法の心拍への短期的効果

    藤原幸一, 仲山千佳夫, 松尾雅博, 加納学, 角谷寛

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)  2017.11.25 

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    Event date: 2017.11

    Language:Japanese   Presentation type:Oral presentation (general)  

  40. 次第に速くなる心拍音提示によるゲーム体験の向上

    小川紗也加, 藤原幸一, 山川俊貴, 阿部恵里花, 加納学

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)  2017.11.25 

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    Event date: 2017.11

    Language:Japanese   Presentation type:Oral presentation (general)  

  41. 心拍変動解析と多変量統計的プロセス管理による全般性てんかん発作予測

    坂根史弥, 藤原幸一, 宮島美穂, 鈴木陽子, 山川俊貴, 加納学, 前原健寿

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)  2017.11.25 

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    Event date: 2017.11

    Language:Japanese   Presentation type:Oral presentation (general)  

  42. 中大脳動脈閉塞ラットモデルを用いた心拍変動解析による脳卒中早期検知システム実現性の検証

    藤原幸一, 鎌田啓輔, 児玉智信, 加納学, 村山雄一, 結城一郎

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)  2017.11.25 

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    Event date: 2017.11

    Language:Japanese   Presentation type:Oral presentation (general)  

  43. Validation of HRV-Based Drowsy-Driving Detection Method with EEG Sleep Stage Classification

    T. Yamakawa, K. Fujiwara, T. Hiraoka, M. Kano, Y. Sumi, F. Masuda, M. Matsuo, H. Kadotani

    Proc. of World Sleep Congress  2017.10 

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    Language:English   Presentation type:Oral presentation (general)  

  44. てんかん発作抑制を目指した局所脳冷却システムの設計

    畑啓, 藤原幸一, 加納学, 井上貴雄, 野村貞宏, 井本浩哉, 鈴木倫保

    化学工学会秋季大会研究発表講演要旨集(CD-ROM)  2017.9.20 

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    Event date: 2017.9

    Language:Japanese   Presentation type:Oral presentation (general)  

  45. A new infarction detection method based on heart rate variability in rat middle cerebral artery occlusion model

    Tomonobu Kodata, Keisuke Kamata, Koichi Fujiwara, Manabu Kano, Toshiki Yamakawa, Ichiro Yuki, Yuichi Murayama

    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS  2017.9.13 

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    Event date: 2017.9

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    © 2017 IEEE. Objective: The present study proposes a cerebral infarction detection algorithm based on heart rate variability (HRV). Methods: It has been reported that infarction affects HRV. Therefore, infarction could be detected at an acute stage by monitoring HRV. This study uses multivariate statistical process control (MSPC), which is a well-known anomaly monitoring method. HRV data shortly after infarction onsets are collected by using the middle cerebral artery occlusion (MCAO) model in rats. This study prepares 11 MCAO-operated rats and 11 sham-operated rats. Three sham-operated rats' data are used for model construction of MSPC, and the other 19 rats' data are used for its validation. Results: The sensitivity and specificity of the proposed algorithm were 82 % and 75 %, respectively. Conclusion: An infarction onset could be detected at an acute stage by monitoring HRV.

  46. Design of focal brain cooling system for suppressing epileptic seizures

    Kei Hata, Koichi Fujiwara, Manabu Kano, Takao Inoue, Sadahiro Nomura, Hirochika Imoto, Michiyasu Suzuki

    Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS  2017.9.13 

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    Event date: 2017.9

    Language:English   Presentation type:Oral presentation (general)  

    © 2017 IEEE. Epilepsy is a group of diseases caused by excessive neuronal activities, and one-quarter of the patients do not become seizure-free by the existing treatments. The potential treatments include focal brain cooling, which aims to cool the region where the excessive neuronal activities begin. We are developing a focal brain cooling system. The system delivers cold saline to a cranially implanted cooling device. The outflow is cooled by a Peltier device and pumped for circulation. The Peltier device and the pump are activated only when a seizure is predicted. In this research, the length of time for cooling the brain was calculated with a computational fluid dynamics (CFD)-based model of the focal brain cooling system. As a result, it takes less than 10 minutes for the average temperature 2 mm below the cooling device to reach 25.0 °C. It is much shorter than the time from seizure prediction to seizure onset when an existing algorithm for prediction is used.

  47. Development of correlation-based process characteristics visualization method and its application to fault detection

    Koichi Fujiwara, Manabu Kano

    IEEE International Conference on Control and Automation, ICCA  2017.8.4 

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    Event date: 2017.8

    Language:English   Presentation type:Oral presentation (general)  

    © 2017 IEEE. Although process monitoring is important for maintaining safety and product quality, it is difficult to understand process characteristics particularly when they are changing. Since the correlation among variables changes due to changes in process characteristics, process data visualization based on the correlation among variables helps process characteristic understanding. In the present work, a new correlation-based data visualization method is proposed by integrating joint decorrelation (JD) and stochastic proximity embedding (SPE). JD is a blind source separation (BSS) method that can separates sample based on the correlation, and SPE is a self-organizing algorithm that can map high-dimensional data to a two-dimensional plane. The proposed method, referred to as JD-SPE, separates samples based on the correlation using JD and the separated samples are visualized in the two-dimensional plane by SPE. Correlation matrices have to be constructed before sample separation for JD; however how to construct them is not clear. The present work also proposes a correlation matrix construction method for JD by using nearest correlation spectral clustering (NCSC), which is a correlation-based clustering method. In addition, a new process monitoring method based on multivariate statistical process control (MSPC) which is a well-known process monitoring algorithm and JD-SPE. This monitoring method is referred to as JD-SPE-r2. The proposed JD-SPE-Γ2 can detect a fault that can not detected by the conventional MSPC. The usefulness of the proposed methods is demonstrated through numerical examples.

  48. 心拍変動解析と多変量統計的プロセス管理に基づく全般性てんかん発作予測

    坂根史弥, 藤原幸一, 宮島美穂, 鈴木陽子, 山川俊貴, 加納学, 前原健寿

    システム制御情報学会研究発表講演会講演論文集(CD-ROM)  2017.5.23 

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    Event date: 2017.5

    Language:Japanese   Presentation type:Oral presentation (general)  

  49. Canine emotional states assessment with heart rate variability

    Eri Nakahara, Yuki Maruno, Takatomi Kubo, Rina Ouchi, Maki Katayama, Koichi Fujiwara, Miho Nagasawa, Takefumi Kikusui, Kazushi Ikeda

    2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016  2017.1.17  Institute of Electrical and Electronics Engineers Inc.

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    Event date: 2017.1

    Language:English   Presentation type:Oral presentation (general)  

    Emotions of a person affect the person's performance in a task and so do emotions of a rescue dog that works after disasters. Hence, estimating emotions of a rescue dog by the handler can improve its performance and welfare. Emotions also appear in physiological signals such as heart rate variability (HRV). In fact, HRV has information of emotions in both cases of human and dogs. To make emotion estimation more practical, we proposed a method for emotion estimation from HRV of dogs and evaluated its performance using real data. The method classified positive, negative, and neutral emotions with 88% accuracy within each subject and 72% over all subjects. These accuracies are high enough for practical use in rescue dogs.

  50. Development of Photoplethysmogram Sensor-embedded Video Game Controller

    Erika Abe, Koichi Fujiwara, Manabu Kano, Hiroshi Chigira, Toshitaka Yamakawa

    2016 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)  2016  IEEE

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    Event date: 2016

    Language:English   Presentation type:Oral presentation (general)  

    If player condition during video game playing could be measured in real time, it would become possible to develop a new game interaction system. Since heart rate (HR) has been used for various psychological condition estimation, it can be used for player condition estimation. In the present work, a new game controller that can measure player HR without letting the player be conscious of the controller based on a photoplethysmogram (PPG) was developed. The experiment result demonstrated that the newly developed game controller could measure the player HR with sufficiently high accuracy.

  51. Correlation-based spectral clustering for flexible soft-sensor design

    Koichi Fujiwara, Manabu Kano, Shinji Hasebe

    IFAC Proceedings Volumes (IFAC-PapersOnline)  2010 

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    Event date: 2010

    Language:English   Presentation type:Oral presentation (general)  

    The current issues concerning soft-sensors are how to cope with changes in process characteristics and how to cope with parallelized, slightly different, multiple processes. To make soft-sensors adaptive and flexible, the development of practical design techniques, instead of impracticable ideas, is crucial; this is the motivation of the present research. In practice, it is difficult to successfully apply a single soft-sensor to parallelized production devices due to their individual difference. Since the individual difference is expressed as difference of the correlation among variables, it is useful to classify samples into multiple clusters according to the correlation in order to adopt a multi-model approach. In the present work, a new correlation-based clustering method, referred to as NC-spectral clustering, is proposed by integrating the nearest correlation (NC) method and spectral clustering. The NC method can detect samples that are similar to the query from the viewpoint of the correlation. In the proposed method, the NC method is used for constructing the weighted graph that expresses the correlation-based similarities between samples and the constructed graph is partitioned by using spectral clustering. In addition, a new soft-sensor design method is proposed on the basis of the proposed NC-spectral clustering. The superiority of the proposed method over conventional methods is demonstrated through a numerical example and a case study of parallelized batch processes. © 2009 IFAC.

  52. Correlation-based pattern recognition and its application to adaptive soft-sensor design

    Fujiwara K, Kano M, Hasebe S

    IFAC Proceedings Volumes (IFAC-PapersOnline)  2009 

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    Event date: 2009

    Language:English   Presentation type:Oral presentation (general)  

    Other Link: http://orcid.org/0000-0002-2325-1043

  53. AI/IoT を活用した新たなてんかん治療法の開発 Invited

    藤原 幸一

    名古屋大学医学部脳とこころの研究センタ・サマースクール  2019.7.17 

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    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  54. Closed-Loop てんかんケアの実現に向けたてんかん発作予知アルゴリズムの開発 Invited

    藤原 幸一

    電子情報通信学会ソサイエティ大会  2019.9.12 

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    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  55. AI/IoTによるソーシャルディスンス社会におけるヒトのセンシング Invited

    藤原幸一

    名古屋大学高等研究院ウェビナー  2020.6.2 

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    Language:English   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  56. IoTシステム設計において考慮すべきこと Invited

    藤原幸一

    化学工学会第51回Continuing Educationシリーズ講習  2020.1.21 

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    Language:English   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  57. MATLABを用いた医療機器ソフトウェア開発心拍変動解析とてんかん発作予知 Invited

    藤原 幸一

    MATALB Expo 2019  2019.5.28 

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    Language:English   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  58. ウェアラブル心拍変動センサを用いたてんかん発作予測システムの開発 Invited

    藤原 幸一

    第52回日本てんかん学会学術集会  2018.10.27 

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    Language:English   Presentation type:Symposium, workshop panel (nominated)  

  59. 医療AI人材とか何か〜てんかん・睡眠障害のモニタリングAIの開発を例に Invited

    藤原 幸一

    日本睡眠学会医師向けセミナー  2019.6.27 

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    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  60. 医学における AI の活用てんかん・睡眠障害を例に Invited

    藤原 幸一

    東京医科歯科大学脳機能外科セミナー  2019.7.2 

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    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  61. 人工知能で測れないものを測る Invited

    藤原 幸一

    天白高校・出前授業  2019.11.7 

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    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  62. 人と人をつなぐテクノロジ Invited

    藤原 幸一

    七尾市青年会議所公開授業  2017.9.5 

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    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  63. リアルタイム心拍変動解析を用いたヘルスモニタリング Invited

    藤原 幸一

    京都大学テックフォーラム  2017.11.6 

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    Language:English   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  64. スモールデータ解析でAIに勝つ〜データ解析を活用した医療機器開発 Invited

    藤原 幸一

    ものづくり企業に役立つ応用数理手法の研究会  2018.12.12 

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    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  65. スモールデータでAIに勝つ~てんかん発作予知を例に Invited

    藤原 幸一

    鉄鋼協会産学若手交流セミナー  2018.9.8 

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    Language:English   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  66. 医療AI開発とその活用 〜てんかん発作予知を例に Invited

    藤原 幸一

    第39回医療情報学連合大会企画カンファレンス  2019.11.22 

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    Language:English   Presentation type:Symposium, workshop panel (nominated)  

  67. 心拍変動解析と機械学習を用いたてんかんアラーム〜スモールデータ解析でAIに勝つ, Invited

    藤原 幸一

    市村学術賞受賞記念講演  2017.9.6 

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    Language:English   Presentation type:Oral presentation (invited, special)  

  68. 心拍変動を用いた入眠検出 Invited

    藤原 幸一

    第24回日本時間生物学会学術大会シンポジウム  2017.10.29 

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    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

  69. 報道と研究 - 現場から

    藤原 幸一

    新聞労連研修会  2017.1.21 

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    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

  70. 医療×AIシンポジウム -医療×AI推進人材を考える Invited

    藤原 幸一

    日本マイクロソフトDeep Learning Lab  2019.2.10 

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    Language:English   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  71. 若手研究者による講演 Invited

    藤原 幸一

    JSPS卓越研究員事業説明会  2019.3.2 

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    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  72. 熱中症アラーム開発の取り組み - 2020年に向けて Invited

    藤原 幸一

    鹿児島県西之表市「スマートエコアイランド種子島」シンポジウム  2017.3.8 

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    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  73. 新たなてんかんケアの可能性~てんかん発作予知システムの開発 Invited

    藤原 幸一

    名古屋大学医学部市民公開講座  2019.12.1 

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    Language:Japanese   Presentation type:Oral presentation (invited, special)  

  74. 心拍変動解析を用いたてんかん発作予知・検知技術の開発 Invited

    藤原 幸一

    名古屋大・聖隷浜松合同カンファレンス  2019.10.5 

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    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  75. 医学と工学の垣根を越えた医療AI開発 Invited

    藤原幸一

    マイクロソフトDeep Learning Lab Healthcare Day 2021 ~医療 x AI への参入障壁を乗り越える~  2021.2.20 

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    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

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Research Project for Joint Research, Competitive Funding, etc. 38

  1. 心拍変動解析によるてんかん発作予知AIシステムの研究開発

    2021.4 - 2026.3

    医工連携・人工知能実装研究事業 

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    Authorship:Principal investigator  Grant type:Competitive

  2. ウェアラブルデバイスによる熱中症発症予防のための熱中症アラームシステム

    2019.4 - 2023.3

    科研費基盤B 

    藤原 幸一

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    Authorship:Principal investigator  Grant type:Competitive

  3. 非専門医によるてんかん診療質向上のための診療支援AI基盤の創出

    2018.10 - 2022.9

    さきがけ 

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    Grant type:Competitive

  4. Research for development of connectome-based functional neurosurgery and non-invasive simulation system

    Grant number:22H03184  2022.4 - 2027.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship:Coinvestigator(s) 

    Grant amount:\15860000 ( Direct Cost: \12200000 、 Indirect Cost:\3660000 )

  5. Fundamental development of physiological measurement and analysis platform toward 2nd-generation healthcare IoT technology

    Grant number:21H03855  2021.4 - 2025.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship:Coinvestigator(s) 

    Grant amount:\17160000 ( Direct Cost: \13200000 、 Indirect Cost:\3960000 )

  6. Identifying modifiable factors for cognitive decline: using multimodal biological data.

    Grant number:21H03851  2021.4 - 2025.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship:Coinvestigator(s) 

    Grant amount:\17290000 ( Direct Cost: \13300000 、 Indirect Cost:\3990000 )

  7. 心拍変動解析と機械学習を用いたてんかん発作予知AIの実証研究

    2021.4 - 2022.3

    研究助成 

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    Authorship:Principal investigator 

  8. サイボーグ技術によって身体を再定義し,自己の能力を従来の人の限界を超えて高め誰もが自己実現できる社会

    2021.2 - 2021.8

    ムーンショット型研究開発事業 新たな目標検討のためのビジョン公募 

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    Authorship:Principal investigator 

  9. 心拍変動解析に基づくCOVID-19重症化予測機械学習アルゴリズムの開発研究

    2020.7 - 2021.6

    新型コロナウィルス感染症対策 助成プログラム 

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    Authorship:Coinvestigator(s) 

  10. COVID-19重症化予測AIの開発

    2020.7 - 2021.3

    牧誠記念研究助成 

    藤原幸一

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    Authorship:Principal investigator 

  11. 牧誠記念研究助成

    2020.6

  12. てんかん発作オンデマンド介入のための発作予測システムの開発

    2019.11 - 2020.3

    先端計測プログラム・加速費 

    藤原 幸一

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    Authorship:Principal investigator  Grant type:Competitive

  13. 心電図解析によるてんかん発作の検知・予知システム確立のための広帯域頭蓋内脳波解析

    2019.4 - 2021.3

    科研費基盤C 

    前原健寿

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    Grant type:Competitive

  14. てんかん発作オンデマンド介入のための発作予測システムの開発

    2018.12 - 2019.3

    平成30年度第2回医療分野の研究開発関連の調整費 

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    Grant type:Competitive

  15. 卓越研究員研究費

    2018.11 - 2020.3

    卓越研究員制度 

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    Grant type:Competitive

  16. マルチモダリティ生体信号計測によるてんかん発作自動検出および重症度評価技術の確立

    2018.4 - 2021.3

    科研費基盤C 

    宮島美穂

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    Grant type:Competitive

  17. AIによる教育と医療で共有可能なADHDスクリーニング及び治療適正化方法の開発

    2018.4 - 2021.3

    科研費基盤C 

    阪上由子

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    Grant type:Competitive

  18. リアルタイム心拍変動解析技術を用いたヘルスケアサービス開発

    2018.4 - 2020.3

    インキュベーションプログラム 

    藤原 幸一

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    Authorship:Principal investigator  Grant type:Competitive

  19. 非専門医のてんかん診療の質改善のためのてんかん診療支援クラウドAIの開発

    2018.1 - 2018.12

    国内共同研究 

  20. 保健医療用人工知能の技術革新と国際競争力向上に資する人材育成に関する研究

    2017.10 - 2019.3

    厚生労働省科学研究費補助金 

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    Grant type:Competitive

  21. ロバスト主成分分析を用いたてんかん発作予知システムの実用化研究

    2017.10 - 2018.9

    国内共同研究 

  22. てんかん発作オンデマンド介入のための発作予測システムの開発

    2017.8 - 2020.3

    先端計測分析技術・機器開発プログラム 

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    Grant type:Competitive

  23. クラウド型てんかん発作診療支援AIの開発

    2017.8 - 2018.7

    国内共同研究 

  24. ウェアラブルセンシングと人工知能の融合によるクラウドてんかん発作診療支援システムの開発

    2017.4 - 2020.3

    国内共同研究 

  25. 生理機能に基づくレビー小体型認知症早期診断ウェアラブルデバイスの開発

    2017.4 - 2020.3

    科研費基盤A 

    角谷寛

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    Grant type:Competitive

  26. 夜間・休日を含む小児救急医療体制の最適化及び情報発信方法に関する研究

    2017.4 - 2019.3

    厚生労働省科学研究費補助金 

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    Grant type:Competitive

  27. 心拍変動解析と機械学習の融合による脳卒中検知システムの基盤技術開発

    2017.4 - 2019.3

    国内共同研究 

  28. センシング技術を基軸とした健康管理システムの地域特性に基づく分析

    2017.4 - 2019.3

    科研費基盤C 

    下野僚子

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    Grant type:Competitive

  29. 治療抵抗性高血圧症に対する頭側延髄腹外側野の微小血管減圧術-確実な診断技術の開発

    2016.4 - 2019.3

    科研費基盤C 

    浜崎禎

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    Grant type:Competitive

  30. 心拍変動解析と機械学習に基づいた熱中症発症予測アルゴリズム構築

    2016.4 - 2017.3

    国内共同研究 

  31. PLSと構造正則化に基づいた高精度溶銑温度予測モデルの開発

    2015.12 - 2017.11

    国内共同研究 

  32. 自動車運転中に特化したてんかん発作兆候監視システム開発およびインタフェース設計

    2014.10 - 2016.9

    国内共同研究 

  33. 迷走神経刺激療法有効性事前判定のためのてんかん発作軽減効果予測手法の開発

    2014.4 - 2018.3

    科研費若手B 

    藤原 幸一

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    Authorship:Principal investigator  Grant type:Competitive

  34. てんかん発作発現前の生理的脳内ネットワークの変調に基づいた発作予知理論の実証

    2014.4 - 2017.3

    科研費基盤C 

    丸田雄一

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    Grant type:Competitive

  35. 心拍変動解析によるてんかん発作早期予知デバイスの開発

    2014.4 - 2015.3

    国内共同研究 

  36. 心拍変動に基づくてんかん発作兆候検知システムの構築

    2014.1 - 2014.12

    国内共同研究 

  37. ウェアラブルHRVセンサを用いたてんかん発作兆候検知システムの開発

    2013.4 - 2017.3

    科研費基盤B 

    宮島美穂

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    Grant type:Competitive

  38. ネックレス型心拍数ワイヤレス計測デバイスを用いた小型・低コストな車載用居眠り検知システムの基盤技術開発

    2012.10 - 2014.3

    A-Step シーズ顕在化タイプ 

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    Grant type:Competitive

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KAKENHI (Grants-in-Aid for Scientific Research) 11

  1. Research for development of connectome-based functional neurosurgery and non-invasive simulation system

    Grant number:22H03184  2022.4 - 2027.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship:Coinvestigator(s) 

  2. Fundamental development of physiological measurement and analysis platform toward 2nd-generation healthcare IoT technology

    Grant number:21H03855  2021.4 - 2025.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship:Coinvestigator(s) 

  3. Identifying modifiable factors for cognitive decline: using multimodal biological data.

    Grant number:21H03851  2021.4 - 2025.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship:Coinvestigator(s) 

  4. Development of Heat Stroke Alert System by Wearable Device

    Grant number:19H04501  2019.4 - 2023.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

    Fujiwara Koichi

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    Authorship:Principal investigator 

    Grant amount:\15990000 ( Direct Cost: \12300000 、 Indirect Cost:\3690000 )

    Preventing severe heat illness, called heatstroke, is crucial because it can lead to long-term multiple organ damage, including the brain, and results in more than 600 deaths per year in the United States. It has been reported that heat stress affects heart rate variability (HRV), which is the fluctuations of the R-R interval (RRI) on an electrocardiogram (ECG). We propose a method for detecting symptoms of heat illness based on HRV analysis in order to prevent exacerbation of heat illness. In the proposed method, monitoring abnormal changes in HRV caused by heat stress is monitored. To validate the proposed method, we recruited 103 healthy volunteers with risks of heat illness development. The result of applying the proposed method showed that a sensitivity of 75% (21 out of 28 cases) and a false-positive rate of 1.02 times per hour were achieved.

  5. Wide-band ECoG analysis for establishing epilepsy detection and prediction system based on ECG algorithm

    Grant number:19K09475  2019.4 - 2022.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (C)

    MAEHARA TAKETOSHI

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    Authorship:Coinvestigator(s) 

    We have proposed an epileptic seizure prediction method using a machine learning anomaly detection technique based on heart rate variability (HRV) during long-term scalp electroencephalogram (EEG) recordings. However, mechanism of epilepsy prediction system using HRV is unclear. In this study, we confirmed usefulness of this system and tried to disclose the mechanism in patients underwent intracranial electrocorticogram (ECoG) recordings. Between May 2019 and January 2022, 11 patients underwent 12 times intracranial ECoG recordings for detection of epileptic focus. Epilepsy prediction rate is 78% and the number of false-positive prediction is 1.42 times/hr, that were almost same as those of EEG recording. These results suggested that HRV change is preceded before epileptic seizures. We also found that electrical seizure without clinical manifestation is predicted by HRV analysis. Therefore, we need to pay attention the possibility that false-negative prediction might be a true seizure.

  6. AIによる、教育と医療で共有可能なADHDスクリーニング及び治療適正化方法の開発

    Grant number:18K10960  2018.4 - 2022.3

    科学研究費助成事業  基盤研究(C)

    阪上 由子, 藤原 幸一, 澤井 ちひろ, 大平 雅子, 松尾 雅博

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    Authorship:Coinvestigator(s) 

    コロナウイルスの感染拡大が当初の想定よりも長期化し、コントロールスタデイとして当初予定していた小学生を対象にした睡眠行動データの回収が困難な状態が続いたため、ADHD群のみを対象とした研究に切り替え実施することとした。新たな研究計画は、本学倫理委員会の審査を経て受理された。が、研究実施の予定時期に主任研究者が入院加療を要する事態となり、次年度からの研究再開のめどが立たないため、やむなく廃棄申請するに至った。

  7. XX

    Grant number:18K12141  2018.4 - 2021.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (C)

    MIYAJIMA Miho

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    Authorship:Coinvestigator(s) 

    We used autoencoder, which is a type of neural network, for detecting changes in heart rate variability associated with an epileptic seizure. We collected electrocardiogram data from 66 patients with focal epilepsy. The collected ictal data included focal aware seizures and focal impaired awareness seizures as well as focal to bilateral tonic-clonic seizures. We trained an autoencoder model from randomly selected 78 hours of interictal data and validated the model using the rest of episodes. The overall seizure detection sensitivity by 60 sec from clinical seizure onset was 77.6%. The area under the curve (AUC) of 0.92 was achieved. This means the level of detection performance is generally considered meaningful. The false positive rate for an unknown cause was 1.5 per hour. This results suggest that the proposed epileptic seizure detection algorithm demonstrated preferable performance focal seizures including nonconvulsive seizures.

  8. Development of Early Lewy Detection and Estimation Report System (ELDER System)

    Grant number:17H00872  2017.4 - 2021.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

    Kadotani Hiroshi

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    Authorship:Coinvestigator(s) 

    Orthostatic hypotension (OH) caused by autonomic dysfunction is a common symptomin older people and patients with idiopathic rapid eyemovement sleep behavior disorder (iRBD). We measured the electrocardiograms of patients with iRBD and healthy older people during an orthostatic challenge test. We found that short-term heart rate variability (HRV) indices in the supine position would predict subsequent OH in iRBD patients. Our results are of clinical importance in terms of showing the possibility that OH can be predicted using only HRV in the supine position without an orthostatic challenge test, which would improve the efficiency and safety of testing.

  9. Analysis of regional healthcare-management-system based on monitoring technology

    Grant number:17K01245  2017.4 - 2020.3

    Shimono Ryoko

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    Authorship:Coinvestigator(s) 

    We aim a development of healthcare-management-system for preventing lifestyle related diseases and analyze the plural aspects for effectiveness of implementing monitoring technology and health promotion projects. We proceeded research activities through collaboration with regional player such as medical institutes and local governments for increasing feasibility. In actual, we delivered the analyzed results contributing for project implementation for Specific health checkups and Specific health guidance, for health promotion event, incentive point earning for health behavior and so on.

  10. Microvascular decompression of the rostral ventrolateral medulla for drug-resistant neurogenic hypertension

    Grant number:16K10789  2016.4 - 2020.3

    Hamasaki Tadashi

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    Authorship:Coinvestigator(s) 

    Advances in our understanding of human brain physiology have expanded application of neuromodulation therapy to treat disease symptoms. We constructed an in-house system to investigate whether there is a specific area in the human brain where neurosurgical manipulation may exert an effect on autonomic activities and thereby cause changes in systemic circulation. Forty-one surgeries were recorded and analyzed. Statistical analysis demonstrated sympathetic hyperactivity, hypertension, and tachycardia when the area of the lateral aspect of the ponto-medullary junction in the human brain stem was continuously electrically stimulated for the purpose of monitoring a cranial nerve function during tumor removal. These findings provided physiological evidence showing that there is an autonomic center in the lower brain stem of the human brain. We suggest that the area is a possible target of neuromodulation for disorders with autonomic imbalance including drug-resistant hypertension.

  11. Development of method for predicting effect of VNS on epileptic seizure alleviation

    Grant number:26870314  2014.4 - 2018.3

    Fujiwara Koichi

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    Authorship:Principal investigator 

    Grant amount:\3900000 ( Direct Cost: \3000000 、 Indirect Cost:\900000 )

    Vagus Nerve Stimulation (VNS) is treatment of refractory epilepsy; however, its physiological mechanism has not been fully understood. The mechanism of VNS needs to be investigated in order to avoid ineffective operations. Because an epileptic seizure is caused by the spread of excessive discharge from neurons in the cerebrum, analyzing effects of VNS on EEG would be useful for VNS mechanism investigation. The EEG data of epileptic patients with VNS were analyzed by using Granger Causality (GC) and the graph theory. In addition, a directed graph constructed from those GC values would express neural connection. The result supported the existing hypothesis indicating the bilateral asymmetry of the VNS effect on the brain, and furthermore, it suggested that VNS would increase neural connection between the frontal lobe and other brain regions, and that should control epileptic seizures by keeping patients awake.

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Industrial property rights 17

  1. 不整脈重症度分類装置

    藤原幸一, 永田祥也

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    Applicant:国立大学法人東海国立大学機構

    Application no:特願2022-077509  Date applied:2022.5

  2. 居眠り検知装置、検知方法、及びコンピュータプログラム

    藤原幸一, 加納学, 堀憲太郎, 岩本洋紀

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    Applicant:国立大学法人京都大学

    Application no:特願2021-029195  Date applied:2021.7

  3. 熱中症発症検知装置

    藤原幸一, 太田鴻志, 山川俊貴, 久保孝富

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    Application no:特願2020-097152  Date applied:2020.6

  4. てんかん性発作兆候検知装置、てんかん性発作兆候検知モデル生成装置、てんかん性発作兆候検知方法、てんかん性発作兆候検知モデル生成方法、てんかん性発作兆候検知プログラムおよびてんかん性発作兆候検知モデル生成プログラム

    加納 学, 藤原 幸一

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    Applicant:京都大学

    Application no:特願2013-258494  Date applied:2013.12

    Announcement no:特開2015-112423  Date announced:2015.6

    Patent/Registration no:特許6344912  Date registered:2018.6  Date issued:2018.6

  5. 分類システム

    藤原幸一, 尾崎紀夫, 岩本邦弘, 宮田聖子, 角田 柊二

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    Applicant:国立大学法人東海国立大学機構

    Application no:特願2022-169896  Date applied:2022.10

  6. 熱中症発症検知装置

    藤原幸一, 太田鴻志, 山川俊貴, 久保孝富

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    Applicant:名古屋大学

    Application no:2020-097152  Date applied:2020.6

    Country of applicant:Domestic  

  7. 睡眠時無呼吸症候群判定装置、睡眠時無呼吸症候群判定方法、及び、睡眠時無呼吸症候群判定プログラム

    藤原幸一,仲山千佳夫,岩崎絢子

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    Applicant:国立大学法人京都大学

    Application no:2019-23217  Date applied:2019.6

    Country of applicant:Domestic  

  8. 睡眠時無呼吸症候群判定装置、睡眠時無呼吸症候群判定方法、及び、睡眠 時無呼吸症候群判定プログラム

    藤原 幸一, 仲山 千佳夫, 岩崎 絢子

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    Application no:特願2019-023217  Date applied:2019.2

  9. てんかん発作予測装置、心電指標データの分析方法、発作予測コンピュー タプログラム、モデル構築装置、モデル構築方法、モデル構築コンピュータプログラム

    藤原幸一, 坂根史弥

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    Applicant:京都大学

    Application no:特願2018-181414  Date applied:2018.9

  10. 演算装置、検知装置、演算方法、及び、コンピュータプログラム

    藤原幸一, 宮谷将太

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    Applicant:京都大学

    Application no:特願2018-90592  Date applied:2018.5

  11. 無呼吸識別システム及びコンピュータプログラム

    藤原幸一, 仲山千佳夫, 加納学

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    Applicant:京都大学

    Application no:特願2015-101782  Date applied:2015.5

    Announcement no:特開2016-214491  Date announced:2016.12

    Patent/Registration no:特許6691334  Date registered:2020.4  Date issued:2020.4

  12. 眠気検出方法及び眠気検出装置

    山川俊貴, 藤原幸一, 平岡敏洋, 阿部恵里花

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    Applicant:京都大学

    Application no:特願2014-114093  Date applied:2014.6

    Announcement no:特開2015-226696  Date announced:2015.12

    Patent/Registration no:特許6375496  Date registered:2018.8  Date issued:2018.8

  13. センサ情報解析装置、携帯情報端末間通信制御装置、方法、及びプログラム

    藤原 幸一,竹内考

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    Applicant:日本電信電話株式会社

    Application no:特願2012-152618  Date applied:2012.7

    Announcement no:特開2014-17605  Date announced:2014.1

    Country of applicant:Domestic  

  14. 予測モデル構築装置、方法、及びプログラム、並びに発電量予測装置、及び方法

    藤原 幸一,須山敬之

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    Applicant:日本電信電話株式会社

    Application no:特願2011-240543  Date applied:2011.11

    Announcement no:特開2013-99143  Date announced:2013.5

    Patent/Registration no:特許5661594  Date registered:2014.12 

    Country of applicant:Domestic  

  15. プラント制御情報生成装置及び方法、並びにそのためのコンピュータプログラム

    加納学, 藤原幸一

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    Applicant:京都大学

    Application no:特願2009-151745  Date applied:2009.6

    Announcement no:特開2011-008562  Date announced:2011.1

    Patent/Registration no:特許5457737  Date registered:2014.1 

    Country of applicant:Domestic  

  16. 変数決定方法、変数決定装置、プログラム及び記録媒体

    加納学, 藤原幸一

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    Applicant:京都大学

    Announcement no:特開2008-517936  Date announced:2007.5

    Country of applicant:Domestic  

  17. 操作変数選択装置,操作変数選択方法,操作変数選択プログラムおよびそれを記録したコンピュータ読み取り可能な記録媒体

    加納学, 藤原幸一

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    Announcement no:特開2006-323523  Date announced:2006.11

    Country of applicant:Domestic  

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Teaching Experience (On-campus) 4

  1. メディカルAI人材育成産学協同育成拠点・特別講義

    2022

  2. 線形代数学1

    2020

  3. 先進プロセス情報学

    2020

  4. システム制御

    2020

Teaching Experience (Off-campus) 5

  1. AI-MAILs - メディカルAI人材養成産学協働拠点・講義

    2021.9 名古屋大学医学部)

  2. Programming Practice

    2020.10 Nagoya University)

  3. 先進プロセス情報論

    2019.4 Nagoya University)

  4. Linear Algebra

    2019.4 Nagoya University)

  5. システム制御

    2015.10 名古屋大学,同志社大学)

 

Social Contribution 3

  1. 七尾市鵬学園高校 出前授業

    Role(s):Lecturer

    七尾市青年会議所  2017.9

  2. 京都市立衣笠小学校 出前授業

    Role(s):Lecturer

    京都新聞  ソフィアがやってきた  2016.10

  3. 鹿児島県立種子島高校 出前授業

    Role(s):Lecturer

    西之表市  2016.10

Media Coverage 22

  1. てんかん発作予知を含めた患者状態予知・検知アプリの実用化に向けて Newspaper, magazine

    日本経済新聞  2023.6

  2. 医療AI開発についての提言 Internet

    PC Watch  2022.6

  3. てんかん発作予知AIシステムについて Newspaper, magazine

    日刊ゲンダイ・ヘルスケア  2021.6

  4. てんかん発作予知について TV or radio program

    NHK  NHK World  2020.5

  5. てんかん発作予知について TV or radio program

    NHK  おはよう日本  2020.4

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    Author:Myself 

  6. てんかん発作予知について TV or radio program

    NHK京都局  ニュース 630 京いちにち  2020.3

  7. てんかん発作予知システム開発について Newspaper, magazine

    日本経済新聞  日経新聞夕刊  ライフサポート面  2019.7

  8. イヌがヒトと共感する能力を有していることを実証した研究について Newspaper, magazine

    朝日新聞  朝日新聞夕刊  朝日新聞夕刊  2019.7

  9. 第53回人工知能学会における睡眠時無呼吸症候群スクリーニングアルゴリズムの開発についての発表について Internet

    m3.com  m3.com  2019.6

  10. 日本マイクロソフトDeep Learning Lab 医療×AIシンポジウム 講演紹介 Internet

    m3.com  m3.com  2019.3

  11. スモールデータ解析とてんかん発作予知について Newspaper, magazine

    日本経済新聞  日本経済新聞  かがくアゴラ  2019.2

  12. てんかん発作予知システム開発の紹介 Promotional material

    AMED  AMED事業紹介パンフレット  2019.2

  13. hamonを用いたてんかん発作予知について TV or radio program

    NHK大阪局  NHK関西「ルソンの壺」  2019.1

  14. てんかん学会でのスペシャルセッションでの招待講演について Newspaper, magazine

    CLINIC magazine  CLINIC magazine  2019.1

  15. てんかん発作予知システム開発について Newspaper, magazine

    読売新聞社  読売新聞  社会面  2018.12

  16. 第32回人工知能学会全国大会医療AIセッションのシンポジウムについて Internet

    m3.com  m3.com  2018.6

  17. てんかん発作予知システムについて Newspaper, magazine

    Hello! Docto  Hello! Docto  2018.5

  18. てんかん発作予知システムについて Newspaper, magazine

    日経BP  日経メディカル  2017.12

  19. てんかん発作予知システムに係るAMED班会議について

    京都新聞社  京都新聞  2017.11

  20. てんかん発作予知システムについて Newspaper, magazine

    日経新聞社  日経産業新聞  2017.10

  21. 公益法人新技術開発財団市村賞について Newspaper, magazine

    日経新聞社  日経新聞  2017.4

  22. ニッポンのジレンマ出演 TV or radio program

    NHK  ニッポンのジレンマ  2016.10

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