Non-contact Sleep Apnea syndrome detection based on wake in sleep stage
| Project/Area Number |
20K21829
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 62:Applied informatics and related fields
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| Research Institution | The University of Electro-Communications |
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Principal Investigator |
Takadma Keiki 電気通信大学, 大学院情報理工学研究科, 教授 (20345367)
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| Project Period (FY) |
2020-07-30 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥6,110,000 (Direct Cost: ¥4,700,000、Indirect Cost: ¥1,410,000)
Fiscal Year 2021: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2020: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
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| Keywords | 睡眠時無呼吸症候群 / 覚醒 / 機械学習 / ランダムフォレスト / 無拘束型センサ |
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| Outline of Research at the Start |
本研究では，睡眠障害の約6割を占める睡眠時無呼吸症候群(sleep apnea syndrome：SAS)に着目し，寝具の下に敷いた無拘束型センサを用いてSASを判定する手法を考案する．具体的には，睡眠中の「覚醒」に着目したSAS判定法を確立し，従来手法の問題(低呼吸判定の限界と努力呼吸の誤判定)と，医師の診断の問題(SAS状態を過小評価する問題)の解決を目指す．
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| Outline of Final Research Achievements |
This research focuses on the sleep apnea syndrome (SAS), proposes the non-contact SAS detection method based on “wake” in sleep stage instead of “respiration” during sleep in order to solve the problems of the conventional SAS detection, and shows that the proposed method succeeds to derive the high accuracy of SAS detection. This research has also revealed that the sleep stage of the healthy human subjects tends to become a “wake” when the body movement is large and tends to become a “non-REM” when the body movement is small, while the sleep stage of the SAS patients shows the opposite tendency in addition to the same tendency.
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| Academic Significance and Societal Importance of the Research Achievements |
学術的意義としては，SASの主たる原因である「呼吸」に着目するのではなく，睡眠中の「覚醒」に着目した新しいSAS判定法を確立し，従来手法の問題(低呼吸判定の限界と努力呼吸の誤判定)を克服したことである．社会的意義としては，無拘束型のマットセンサを用いた提案手法によってSASを早期に発見することで，糖尿病・高血圧・心筋梗塞・脳梗塞のリスクを低減させ，SASによる不眠からくるヒューマンエラーや産業・交通事故を削減し，労働生産性の低下を抑制することが可能となる．
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Report
(3 results)
Research Products
(23 results)
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[Presentation] Alzheimer Dementia Detection based on Circadian Rhythm Disorder of Heartrate2021
Author(s)
Matsuda, N., Nakari, I., Arai, R., Sato, H., Takadama, K., Hirose, M., Hasegawa, H., Shiraishi, M., Matsuda, T.
Organizer
2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021)
Related Report
Int'l Joint Research
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[Presentation] Increasing Accuracy and Interpretability of High-Dimensional Rules for Learning Classifier System2021
Author(s)
Shiraishi, H., Tadokoro, M., Hayamizu, Y., Fukumoto, Y.，Sato, H., and Takadama, K.
Organizer
2021 IEEE Congress on Evolutionary Computation (CEC2021)
Related Report
Int'l Joint Research
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[Presentation] Misclassification Detection based on Conditional VAE for Rule Evolution in Learning Classifier System2021
Author(s)
Shiraishi, H., Tadokoro, M., Hayamizu, Y., Fukumoto, Y.，Sato, H., and Takadama, K.
Organizer
Genetic and Evolutionary Computation Conference (GECCO 2021)
Related Report
Int'l Joint Research
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