『徳島大学 教育・研究者情報データベース (EDB)』---[学外] /
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EID=220009EID:220009, Map:0, LastModified:2013年6月17日(月) 16:09:56, Operator:[大家 隆弘], Avail:TRUE, Censor:0, Owner:[柏原 考爾], Read:継承, Write:継承, Delete:継承.
種別 (必須): 学術論文 (審査論文) [継承]
言語 (必須): 英語 [継承]
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著者 (必須): 1.柏原 考爾
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2. (英) Kawada Toru (日) (読)
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3. (英) Uemura Kazunori (日) (読)
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4. (英) Sugimachi Masaru (日) (読)
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5. (英) Sunagawa Kenji (日) (読)
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題名 (必須): (英) Adaptive predictive control of arterial blood pressure based on a neural network during acute hypotension.  (日)    [継承]
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要約 (任意): (英) In acute hypotension, an automated drug infusion system to control mean arterial blood pressure (MAP) has not been previously studied, though many investigations have examined the use of vasodilating drugs to control MAP in postoperative hypertension. Therefore, we examined an automated control of MAP during acute hypotension using a neural network (NN) approach. A proportional-integral-derivative (PID) control, an adaptive predictive control using a NN (APC(NN)), a combined control of APC(NN) and PID (APC(NN-PID)), a fuzzy control, and a model predictive control were tested in computer simulation based on the MAP response to norepinephrine (NE) of 25 microg ml(-1). In six anesthetized rabbits, using the NE of 25 microg ml(-1), the PID control, APC(NN), and APC(NN-PID) prevented severe hypotension compared to an uncontrolled condition. Under PID control, four of the six animals showed MAP oscillation. Using NE of 50 microg ml(-1), the rabbits recovered from acute hypotension for all systems tested but showed sustained MAP oscillation during PID control. In conclusion, utilization of a NN for adaptive predictive control systems could facilitate the development of an automated drug infusion apparatus because it provides robust control even when acute or large perturbations and inter-individual differences in the sensitivity to therapeutic agents occur.  (日)    [継承]
キーワード (推奨): 1. (英) Acute Disease (日) (読) [継承]
2. (英) Adaptation, Physiological (日) (読) [継承]
3. (英) Algorithms (日) (読) [継承]
4. (英) Animals (日) (読) [継承]
5. (英) Blood Pressure (日) (読) [継承]
6. (英) Computer Simulation (日) (読) [継承]
7. (英) Dose-Response Relationship, Drug (日) (読) [継承]
8. (英) Drug Therapy, Computer-Assisted (日) (読) [継承]
9. (英) Feedback (日) (読) [継承]
10. (英) Hypotension (日) (読) [継承]
11. (英) Infusion Pumps (日) (読) [継承]
12. (英) Infusions, Intra-Arterial (日) (読) [継承]
13. (英) Models, Cardiovascular (日) (読) [継承]
14. (英) Neural Networks (Computer) (日) (読) [継承]
15. (英) Norepinephrine (日) (読) [継承]
16. (英) Rabbits (日) (読) [継承]
17. (英) Treatment Outcome (日) (読) [継承]
18. (英) Vasodilator Agents (日) (読) [継承]
発行所 (推奨):
誌名 (必須): Annals of Biomedical Engineering (Biomedical Engineering Society (BMES).)
(pISSN: 0090-6964, eISSN: 1573-9686)

ISSN (任意): 0090-6964
ISSN: 0090-6964 (pISSN: 0090-6964, eISSN: 1573-9686)
Title: Annals of biomedical engineering
Title(ISO): Ann Biomed Eng
Supplier: Kluwer Online
Publisher: Springer
 (NLM Catalog  (Scopus  (CrossRef (Scopus information is found. [need login])
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(必須): 32 [継承]
(必須): 10 [継承]
(必須): 1365 1383 [継承]
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年月日 (必須): 西暦 2004年 10月 初日 (平成 16年 10月 初日) [継承]
URL (任意): http://ci.nii.ac.jp/naid/30020243567/ [継承]
DOI (任意): 10.1114/B:ABME.0000042225.19806.34    (→Scopusで検索) [継承]
PMID (任意): 15535055    (→Scopusで検索) [継承]
NAID (任意): 30020243567 [継承]
WOS (任意): 000223983400005 [継承]
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備考 (任意): 1.(英) Article.Affiliation: Department of Cardiovascular Dynamics, National Cardiovascular Center Research Institute, 5-7-1 Fujishirodai, Suita, Osaka 565-8565, Japan. kasihara@ri.ncvc.go.jp  (日)    [継承]
2.(英) Article.PublicationTypeList.PublicationType: Evaluation Studies  (日)    [継承]
3.(英) Article.PublicationTypeList.PublicationType: Journal Article  (日)    [継承]
4.(英) Article.PublicationTypeList.PublicationType: Research Support, Non-U.S. Gov't  (日)    [継承]
5.(英) Article.PublicationTypeList.PublicationType: Validation Studies  (日)    [継承]

標準的な表示

和文冊子 ● Koji Kashihara, Toru Kawada, Kazunori Uemura, Masaru Sugimachi and Kenji Sunagawa : Adaptive predictive control of arterial blood pressure based on a neural network during acute hypotension., Annals of Biomedical Engineering, Vol.32, No.10, 1365-1383, 2004.
欧文冊子 ● Koji Kashihara, Toru Kawada, Kazunori Uemura, Masaru Sugimachi and Kenji Sunagawa : Adaptive predictive control of arterial blood pressure based on a neural network during acute hypotension., Annals of Biomedical Engineering, Vol.32, No.10, 1365-1383, 2004.

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