| Project/Area Number |
17KT0127
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 特設分野 |
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| Research Field |
Complex Systems Disease Theory
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| Research Institution | Osaka University |
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Principal Investigator |
Kiyono Ken 大阪大学, 基礎工学研究科, 教授 (40434071)
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| Project Period (FY) |
2017-07-18 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 心拍変動 / 複雑系ゆらぎ / 予後予測 / 生体生命情報学 / 生体データ科学 / 生体物理学 |
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| Outline of Final Research Achievements |
In this study, we studied on the dynamical characteristics of heart rate variability (HRV) and developed a new methodology for prognosis and diagnosis of heart disease patients. As further applications of HRV, health management methods in the working environments were also developed. As a new analysis method for HRV, we introduced coarse-grained entropy analysis, cumulant decomposition analysis, multimodal index combining physical activity, and long-term cross-correlation analysis. We established the mathematical theory for these analysis methods. In addition, we also investigated the mortality risk assessment of heart failure patients using the Quasi-Recurrent Neural Network (QRNN). By using QRNN in addition to the conventional HRV features, the predictive power of the mortality was significantly improved.
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| Academic Significance and Societal Importance of the Research Achievements |
近年，ウェアブルセンサやIoT技術の発展により，日常生活中の心拍数や心拍変動を継続的に計測することが可能になった．本研究では，そのようなデータを活用し，心疾患患者の状態の評価や予後予測，さらには，労働者の日常の体調評価を実現する方法を開発した．今後，国内では心疾患患者数の増加により病院ベッド数の不足が予測されている．そのため，在宅での療養を効果的なものに改善する必要がある．本研究の成果は，そのような在宅での患者モニタリングに応用可能である．さらに，職場環境における健康管理など，日常の健康管理への幅広い応用が期待できる．
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