Trial of estimating blood pressure using green light photoplethysmogram
| Project/Area Number |
16K16385
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Biomedical engineering/Biomaterial science and engineering
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| Research Institution | Shinshu University |
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Principal Investigator |
Abe Makoto 信州大学, 学術研究院工学系, 准教授 (90604637)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2018: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2017: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 光電容積脈波 / ニューラルネットワーク / 血圧 / 重回帰モデル / 生体工学 |
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| Outline of Final Research Achievements |
This study was aimed at developing a system of estimation of blood pressure in daily life using a green light photoplethysmogram which is a non-invasive and inexpensive sensor, and at making a system to prevent lifestyle-related diseases.
One of main findings was that we could estimate blood pressure with high accuracy by using the neural network which had features obtained from green light photoplethysmographic signals as input and blood pressure as output. Furthermore, we ascertained that using experimental data including large blood pressure variability improved the estimation accuracy in constructing the model of blood pressure estimation.
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| Academic Significance and Societal Importance of the Research Achievements |
光電容積脈波を用いた血圧推定については，これまで多くの研究がなされてきたが，本研究のように，計測条件や日時が異なっても高精度の血圧推定が可能となるものではなく，汎用性の点において優位性があり，学術的意義がある．加えて，提案手法では，一度個人の血圧推定モデルを作成することで，日常生活においてもウェアラブルデバイス等を用いて簡便な血圧計測が可能となり，生活習慣病の予防のための個人ごとの健康管理に役立つと考えられる．
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Report
(4 results)
Research Products
(11 results)
