An optimal control approach to reference level tracking in general anesthesia

In this paper the neuromuscular blockade level and the bispectral index level tracking problems by means of automatic control are considered in the context of general anesthesia. These tracking problems are formulated as optimal control problems that are numerically solved using direct methods. The results shown in this paper are preliminary but illustrate a good performance of this strategy when applied to biomedical problems. © 2015 IEEE

Model predictive control using MISO approach for drug co-administration in anesthesia

In this paper, a model predictive control system for the depth of hypnosis is proposed and analyzed. This approach considers simultaneous co-administration of the hypnotic and analgesic drugs and their effect on the Bispectral Index Scale (BIS). The control scheme uses the nonlinear multiple-input–single-output (MISO) model to predict the remifentanil influence over the propofol hypnotic effect. Then, it exploits a generalized model predictive control algorithm and a ratio between the two drugs in order to provide the optimal dosage for the desired BIS level, taking into account the typical constraints of the process. The proposed approach has been extensively tested in simulation, using a set of patients described by realistic nonlinear pharmacokinetic/pharmacodynamic models, which are representative of a wide population. Additionally, an exhaustive robustness evaluation considering inter- and intra-patient variability has been included, which demonstrates the effectiveness of the analyzed control structure

Closed-loop control of anesthesia : survey on actual trends, challenges and perspectives

Automation empowers self-sustainable adaptive processes and personalized services in many industries. The implementation of the integrated healthcare paradigm built on Health 4.0 is expected to transform any area in medicine due to the lightning-speed advances in control, robotics, artificial intelligence, sensors etc. The two objectives of this article, as addressed to different entities, are: i) to raise awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations, ii) to provide the actualized insights of drug-delivery research in order to create an opening horizon towards precision medicine with significantly improved human outcomes. This article presents a concise overview on the recent evolution of closed-loop anesthesia delivery control systems by means of control strategies, depth of anesthesia monitors, patient modelling, safety systems, and validation in clinical trials. For decades, anesthesia control has been in the midst of transformative changes, going from simple controllers to integrative strategies of two or more components, but not achieving yet the breakthrough of an integrated system. However, the scientific advances that happen at high speed need a modern review to identify the current technological gaps, societal implications, and implementation barriers. This article provides a good basis for control research in clinical anesthesia to endorse new challenges for intelligent systems towards individualized patient care. At this connection point of clinical and engineering frameworks through (semi-) automation, the following can be granted: patient safety, economical efficiency, and clinicians' efficacy

Performance of an Adaptive Controller for the Neuromuscular Blockade Based on Inversion of a Wiener Model

An adaptive controller based on a minimally parameterized parsimonious Wiener model for the effect of the muscle relaxant rocuronium in the neuromuscular blockade is presented. The controller structure combines inversion of the recursively identified static nonlinearity of the Wiener model with a positive compartmental control law for the linearized system. The overall strategy exploits the fact that the model has only two parameters, which are estimated by an extended Kalman filter. Due to the fact that the positive control law for total mass conservation of compartmental systems is only proven to be convergent for time-invariant systems, the identification of the parameter in the linear block of the minimally parameterized parsimonious Wiener model is stopped when the controller is turned on. The controller was implemented in the platform Galeno and tested in simulation and in thirteen real cases of patients under general anesthesia. The good reference tracking results and robustness to noise show the reliability of the proposed strategy

A simplified control approach for the neuromuscular blockade level

In this paper a new simplified control scheme for the neuromuscular blockade level that only requires the knowledge of one model parameter is proposed. The control law is designed to track a desired target neuromuscular blockade level. Furthermore, an identification procedure to obtain the necessary model parameter is implemented. The results were validated by simulations based on real data collected during surgeries. © Springer International Publishing Switzerland 2017

Robust Control of Maintenance-Phase Anesthesia

In biomedical systems, feedback control can be applied whenever adequate sensors, actuators, and sufficiently accurate mathematical models are available. The key issue is the capacity of the control algorithm to tackle the large levels of uncertainty, both structured and unstructured, associated with patient dynamics. In the particular case of intravenous anesthesia considered here, manipulated variables are drug infusion rates, administered by syringe pumps, and the measured signal outputs are the levels of hypnosis or depth of anesthesia (DoA) and of neuromuscular blockade (NMB). Figure 1 provides an example of a loop closed for the control of NMB

NMB target level tracking via an optimization based control law

In this paper a state-feedback law for the control of the neuromuscular blockade level is presented. The control law is designed based on an optimal problem that is relaxed into a semi-definite program using a change of variable. For that purpose a parsimoniously parameterized model is used to describe the patient?s response to a muscle relaxant. Due to clinical restrictions the controller action begins when the patient recovers after an initial drug bolus. The results obtained encourage the implementation of this controller in the clinical environment even in the presence of noise