文章基本信息

标题：Constructing a control-ready model of EEG signal during general anesthesia in humans ⁎
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作者：John H. Abel ; Marcus A. Badgeley ; Taylor E. Baum 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：15870-15876
DOI：10.1016/j.ifacol.2020.12.243
语种：English
出版社：Elsevier
摘要：AbstractSignificant effort toward the automation of general anesthesia has been made in the past decade. One open challenge is in the development of control-ready patient models for closed-loop anesthesia delivery. Standard depth-of-anesthesia tracking does not readily capture inter-individual differences in response to anesthetics, especially those due to age, and does not aim to predict a relationship between a control input (infused anesthetic dose) and system state (commonly, a function of electroencephalography (EEG) signal). In this work, we developed a control-ready patient model for closed-loop propofol-induced anesthesia using data recorded during a clinical study of EEG during general anesthesia in ten healthy volunteers. We used principal component analysis to identify the low-dimensional state-space in which EEG signal evolves during anesthesia delivery. We parameterized the response of the EEG signal to changes in propofol target-site concentration using logistic models. We note that inter-individual differences in anesthetic sensitivity may be captured by varying a constant cofactor of the predicted effect-site concentration. We linked the EEG dose-response with the control input using a pharmacokinetic model. Finally, we present a simple nonlinear model predictive controlin silicodemonstration of how such a closed-loop system would work.
关键词：KeywordsBiomedical controlmedical applicationsnonlinear controlmodel predictive controlpower spectral density