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AuthorPadmanabhan, Regina
AuthorMeskin, Nader
AuthorHaddad, Wassim M.
Available date2016-04-21T14:44:37Z
Publication Date2014
Publication NameIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - ADPRL 2014: 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, Proceedings
CitationR. Padmanabhan, N. Meskin and W. M. Haddad, "Closed-loop control of anesthesia and mean arterial pressure using reinforcement learning," 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), Orlando, FL, 2014, pp. 1-8.
ISSN2325-1824
URIhttp://dx.doi.org/10.1109/ADPRL.2014.7010644
URIhttp://hdl.handle.net/10576/4429
AbstractGeneral anesthesia is required for patients undergoing surgery as well as for some patients in the intensive care units with acute respiratory distress syndrome. How-ever, most anesthetics affect cardiac and respiratory functions. Hence, it is important to monitor and control the infusion of anesthetics to meet sedation requirements while keeping patient vital parameters within safe limits. The critical task of anesthesia administration also necessitates that drug dosing be optimal, patient specific, and robust. In this paper, the concept of reinforcement learning (RL) is used to develop a closed-loop anesthesia controller using the bispectral index (BIS) as a control variable while concurrently accounting for mean arterial pressure (MAP). In particular, the proposed framework uses these two parameters to control propofol infusion rates to regulate the BIS and MAP within a desired range. Specifically, a weighted combination of the error of the BIS and MAP signals is considered in the proposed RL algorithm. This reduces the computational complexity of the RL algorithm and consequently the controller processing time.
SponsorNPRP grant No. 4-187-2-060 from Qatar National Research Fund (a member of Qatar Foundation).
Languageen
PublisherIEEE
SubjectAnesthesiology
SubjectAnesthetics
SubjectBlood pressure
SubjectClosed loop control systems
SubjectControllers
SubjectDynamic programming
SubjectIntensive care units
SubjectAcute respiratory distress syndrome
SubjectBispectral index
SubjectClosed-loop control
SubjectGeneral anesthesias
SubjectMean arterial pressure
SubjectMonitor and control
SubjectRespiratory function
SubjectVital parameters
SubjectReinforcement learning
TitleClosed-loop control of anesthesia and mean arterial pressure using reinforcement learning
TypeConference Paper


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