Development of novel biomarker from electroencephalography data for by machine learning approach
| Project/Area Number |
17K16365
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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 |
Psychiatric science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Tokuda Keita 東京大学, 医学部附属病院, 助教 (50762176)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | 脳波 / バイオマーカー / 精神疾患 / 非線形時系列解析 / 精神病理学 |
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| Outline of Final Research Achievements |
The aim of the project was to characterize the disease state of psychosis using electroencephalogram recordings (EEG). During this project, we developed a novel mathematical method combining the machine learning techniques such as the deeplearning and the nonlinear time series analysis. We succeeded in developing a system that is able to classify subjects from the EEG recording data, by training the system with the psychiatrists’ diagnosis labels as the training data. The developed method should be applicable to various time series data recorded from the central nervous system other than EEG data.
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| Academic Significance and Societal Importance of the Research Achievements |
高精度の診断・治療効果の評価、病態進行の個別予想、脳波を用いたバイオフィードバックなどによる治療法の開発、疾患動物モデルの開発・評価による創薬の効率化、疾患の基礎的な神経生理学的・病理学的な理解などの実現に貢献し得ると考えられる。
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Report
(4 results)
Research Products
(3 results)
