An Adaptive Multi-critic Neuro-fuzzy Control Framework for Intravenous Anesthesia Administration

Khodaei, Mohammad Javad; Mehrvarz, Amin; Jalili, Nader

doi:10.1016/j.ifacol.2019.01.066

Cited by 9 publications

(13 citation statements)

References 31 publications

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“…Therefore, the complete set-point tracking problem is presented in a form where we can apply Theorem 1 with the addition of LMIs (18) and (20).…”

Section: Constant Set-point Trackingmentioning

confidence: 99%

“…Model-based control (MBC) schemes can overcome this limitation, by including dynamic mathematical models that describe the underlying processes within the human body. Various MBC approaches have been proposed, using fuzzy control 16 adaptive control, [17][18][19] model based PID controllers 20 and model predictive control (MPC). 4,[21][22][23] More recently, an extension 24 on the usual MPC studies on anesthesia, considers simultaneous administration of Propofol and remifentanil in order to gain control on the analgesic-hypnotic balance.…”

Section: Introductionmentioning

confidence: 99%

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A robust model predictive control framework for the regulation of anesthesia process with Propofol

Ntouskas

Sarimveis

2021

Optim Control Appl Methods

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In this work, we present a robust Model Predictive Control (MPC) strategy based on linear matrix inequalities (LMIs) for the intravenous administration of Propofol, a drug which is used for anesthesia during surgeries. The controller is designed for a population of patients and takes into account constraints on the amount of administered drug and on the drug concentration profile. A detailed compartmental mathematical model available in the literature is adjusted to the available data and provides the future predictions of the process. In the context of the application, only the depth of anesthesia (BIS index) is assumed to be measured-as it is common in practice. The state of the system (drug amount in organs) is estimated in real-time by incorporating a state observer.The derived control scheme, along with the designed state observer are able to deal with major challenges in controlling the depth of anesthesia which are inter-and intra-patient variability, model nonlinearity, and model uncertainty.The controller is able to satisfy the divergent characteristics of the patients of the dataset, while satisfying in parallel all the imposed constraints. Moreover, we show that by considering smaller groups of patients with similar characteristics the corresponding responses are significantly improved.

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“…Therefore, the complete set-point tracking problem is presented in a form where we can apply Theorem 1 with the addition of LMIs (18) and (20).…”

Section: Constant Set-point Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A robust model predictive control framework for the regulation of anesthesia process with Propofol

Ntouskas

Sarimveis

2021

Optim Control Appl Methods

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“…A medical application is displayed in [31], developing control strategies in a closed-loop for infusion and medication administration being especially useful in anesthetic during numerous surgeries to provide stability for the necessary state of consciousness. The authors propose a neuro-fuzzy adaptive controller to overcome the current challenges in control closed loops of the anesthetic, like inter and intrapatient variability, complex and non-linear dynamics, measurement noises and surgical alterations, and sub-impulse and overflow in the induction stage.…”

Section: ) Specific Applicationsmentioning

confidence: 99%

Control of a MIMO Coupled Plant Using a Neuro-Fuzzy Adaptive System Based on Boolean Relations

2021

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This document describes the implementation of a neuro-fuzzy adaptive system MIMO (Multiple Input Multiple Output), using two neuro-fuzzy MIMO systems: one for control and the other for identifying the plant. Under this approach, the controller is optimized, employing the model obtained during the identification of the plant that utilizes data generated from the controller's operation. In this way, the plant identification and the controller optimization is performed iteratively. The application case consists of controlling a MIMO non-linear hydraulic system fed by a pump and a three-way valve. In order to observe the controller performance various experimental configurations are considered.

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“…In fact, none of the four controllers discussed are by themselves able to overcome the complex problem of anesthesia due to the presence of intraand inter-patient variability, surgical disturbances, and nonlinear dynamics. As recent papers indicate [326], [327], [339], combinations of these controllers -optimized to leverage their individual strengths -show promise for improving the performance of closed-loop anesthesia and achieving acceptable simulation results.…”

Section: Outlook For Automated Anesthesiamentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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Over the past decade, there has been an unprecedented international focus on improved quality and availability of medical care, which has reignited interest in clinical automation and drawn researchers toward novel solutions in the field of physiological closed-loop control systems (PCLCs). Today, multidisciplinary groups of expert scientists, engineers, clinicians, mathematicians, and policy-makers are combining their knowledge and experience to develop both the next generation of PCLC-based medical equipment and a collaborative commercial/academic infrastructure to support this rapidly expanding frontier. In the following article, we provide a robust introduction to the various aspects of this growing field motivated by the recent and ongoing work supporting two leading technologies: the artificial pancreas (AP) and automated anesthesia. Following a brief high-level overview of the main concepts in automated therapy and some relevant tools from systems and control theory, we explore -separately -the developments, challenges, state-ofthe-art, and probable directions for AP and automated anesthesia systems. We then close the review with a consideration of the common lessons gleaned from these ventures and the implications they present for future investigations and adjacent research.

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An Adaptive Multi-critic Neuro-fuzzy Control Framework for Intravenous Anesthesia Administration

Cited by 9 publications

References 31 publications

A robust model predictive control framework for the regulation of anesthesia process with Propofol

A robust model predictive control framework for the regulation of anesthesia process with Propofol

Control of a MIMO Coupled Plant Using a Neuro-Fuzzy Adaptive System Based on Boolean Relations

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

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