2022 Fiscal Year Final Research Report
Fundamental study on automatic detection of EEG characteristic parameters for the diagnosis of dementia
| Project/Area Number |
20K12672
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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 | 一般 |
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| Review Section |
Basic Section 90130:Medical systems-related
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| Research Institution | Saga University |
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Principal Investigator |
Sugi Takenao 佐賀大学, 理工学部, 教授 (00274580)
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| Co-Investigator(Kenkyū-buntansha) |
松田 吉隆 佐賀大学, 海洋エネルギー研究所, 准教授 (00578429)
後藤 聡 佐賀大学, 理工学部, 教授 (20225650)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | 脳波 / 認知症 / 脳波自動判読システム / 早期診断 / バイオマーカー |
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| Outline of Final Research Achievements |
We validated EEG characteristic parameters associated with the progression and early detection of dementia from EEGs of 82 patients obtained from a hospital. From 57 patients with dementia and mild cognitive impairment, we extracted several characteristic parameters that were correlated with the progression of dementia, including those established in this study. In addition, the analysis including 25 non-dementia patients suggested that EEG characteristic parameters have a possibility to discriminate between dementia and non-dementia patients. Furthermore, we developed the basis of an automatic EEG characteristic parameter detection system for aiming at clinical applications.
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| Free Research Field |
生体医工学
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| Academic Significance and Societal Importance of the Research Achievements |
脳波検査は簡便に脳機能診断が可能なため，脳波特徴パラメータが認知症診断のバイオマーカーとなれば，広く一般的に利用可能で客観的な認知症の診断指標となりうる．また，本研究は脳波特徴パラメータの自動検出を念頭においているため，コンピュータによる診断支援技術が確立されれば，脳波検査による認知症診断の効率が飛躍的に向上し，専門医不足の解消にも寄与する．加えて，脳波検査は脳機能情報の経年的記録が容易であるため，脳波特徴パラメータの推移から，薬物治療効果の定量評価や認知症の進行発症予測への応用も可能である．
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