Towards automation in anaesthesia: a review

Simpósio Internacional MeMeA, realizado em 2014.Surgeries represent a risk for patients and a big cost for the hospital. Anaesthesia represents a complex part of surgery also carries risks for patients. The most known are awareness (with deep psychological consequences); increased risk of morbidity and mortality; adverse reactions and long post-op recovery. The complexity of anaesthesia management can be reduced by studying the patients' responses and developing indicators of the patient state. To assess the level of depth of anaesthesia, the anaesthetist needs to be aware of the patient physiological responses to the drugs and to surgical stimuli. A system that could advise on the patient state considering all clinical signs being measured, the patient individual response and the amount of drugs, will have a big impact on patient overall safety and future health, post-op recovery and hospital resources. This paper does a review of different systems and methods applied to several aspects of the anaesthesia field. All with the goal of working towards automation in this very complex area, that involves high risks for patients. This paper covers advisor systems; signal processing; new monitors and devices; mathematical modelling; and control algorithms; all focused on practical clinical implementation. The objective is to have an overview of the work done so far and the steps taken towards automation in anaesthesia.ISPA - System Integration and Process Automation Unit - Part of the LAETA (Associated Laboratory of Energy, Transports and Aeronautics) a I&D Unit of the Foundation for Science and Technology (FCT), Portugal. FCT support under project PEst-OE/EME/LA0022/2013.info:eu-repo/semantics/publishedVersio

Depth of anesthesia control using internal model control techniques

The major difficulty in the design of closed-loop control during anaesthesia is the inherent patient variability due to differences in demographic and drug tolerance. These discrepancies are translated into the pharmacokinetics (PK), and pharmacodynamics (PD). These uncertainties may affect the stability of the closed loop control system. This paper aims at developing predictive controllers using Internal Model Control technique. This study develops patient dose-response models and to provide an adequate drug administration regimen for the anaesthesia to avoid under or over dosing of the patients. The controllers are designed to compensate for patients inherent drug response variability, to achieve the best output disturbance rejection, and to maintain optimal set point response. The results are evaluated compared with traditional PID controller and the performance is confirmed in our simulation

Advanced Signal Processing and Control in Anaesthesia

This thesis comprises three major stages: classification of depth of anaesthesia (DOA); modelling a typical patient’s behaviour during a surgical procedure; and control of DOAwith simultaneous administration of propofol and remifentanil. Clinical data gathered in theoperating theatre was used in this project. Multiresolution wavelet analysis was used to extract meaningful features from the auditory evoked potentials (AEP). These features were classified into different DOA levels using a fuzzy relational classifier (FRC). The FRC uses fuzzy clustering and fuzzy relational composition. The FRC had a good performance and was able to distinguish between the DOA levels. A hybrid patient model was developed for the induction and maintenance phase of anaesthesia. An adaptive network-based fuzzy inference system was used to adapt Takagi-Sugeno-Kang (TSK) fuzzy models relating systolic arterial pressure (SAP), heart rate (HR), and the wavelet extracted AEP features with the effect concentrations of propofol and remifentanil. The effect of surgical stimuli on SAP and HR, and the analgesic properties of remifentanil were described by Mamdani fuzzy models, constructed with anaesthetist cooperation. The model proved to be adequate, reflecting the effect of drugs and surgical stimuli. A multivariable fuzzy controller was developed for the simultaneous administration of propofol and remifentanil. The controller is based on linguistic rules that interact with three decision tables, one of which represents a fuzzy PI controller. The infusion rates of the two drugs are determined according to the DOA level and surgical stimulus. Remifentanil is titrated according to the required analgesia level and its synergistic interaction with propofol. The controller was able to adequately achieve and maintain the target DOA level, under different conditions. Overall, it was possible to model the interaction between propofol and remifentanil, and to successfully use this model to develop a closed-loop system in anaesthesia

Integrated anaesthesia software : data acquisition, controlled infusion schemes and intelligent alarms

Tese de doutoramento. Engenharia Biomédica. Universidade do Porto. Faculdade de Engenharia. 201

CLOSED-LOOP CONTROLLED TOTAL INTRA VENOUS ANAESTHESIA

Anaesthesia is important for both surgery and intensive care and intravenous anaesthetics are widely used to provide rapid onset, stable maintenance, and rapid recovery compared with inhaled anaesthetics. The aim of the project on which this thesis is based was to investigate a reliable and safe methodology for delivering total intravenous anaesthesia using closed-loop control technology and bispectral analysis of human electroencephalogram (EEG) waveform. In comparison with Target Controlled Infusion (TCI), drug effect is measured during drug infusion in closed loop anaesthesia (CLAN). This may provide superior safety, better patient care, and better quality of anaesthesia whilst relieving the clinician of the need to make recurrent and minor alterations to drug administration. However, the development of a CLAN system has been hindered by the Jack of a 'gold standard' for anaesthetic states and difficulties with patient variability in pharmacokinetic and pharmacodynamic modelling, and a new and generic mathematical model of a closed-loop anaesthesia system was developed for this investigation. By using this CLAN model, investigations on pharmacokinetic and pharmacodynamic variability existing in patients were carried out. A new control strategy that combines a Proportional, Integral, Derivative (PID) controller, bispectral analysis of EEG waveform and pharmacokinetic/ pharmacodynamic models was investigated. Based on the mathematical model, a prototype CLAN system, the first CLAN system capable of delivering both hypnotics and analgesics simultaneously for total intravenous anaesthesia, was developed. A Bispectral Index (BIS), derived from power spectral and bispectral analysis on EEG waveform, is used to measure depth of anaesthesia. A supervision system with built-in digital signal processing techniques was developed to compensate the non-linear characteristics inherent in the system while providing a comprehensive protection mechanism for patient safety. The CLAN system was tested in 78125 virtual patients modelled using published data. Investigations on intravenous anaesthesia induction and maintenance with the CLAN system were carried out in various clinical settings on 21 healthy volunteers and 15 patients undergoing surgery. Anaesthesia targets were achieved quickly and well maintained in all volunteers/patients except for 2 patients with clinically satisfactory anaesthesia quality.Derriford Hospita

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

An open source patient simulator for design and evaluation of computer based multiple drug dosing control for anesthetic and hemodynamic variables

We are witnessing a notable rise in the translational use of information technology and control systems engineering tools in clinical practice. This paper empowers the computer based drug dosing optimization of general anesthesia management by means of multiple variables for patient state stabilization. The patient simulator platform is designed through an interdisciplinary combination of medical, clinical practice and systems engineering expertise gathered in the last decades by our team. The result is an open source patient simulator in Matlab/Simulink from Mathworks(R). Simulator features include complex synergic and antagonistic interaction aspects between general anesthesia and hemodynamic stabilization variables. The anesthetic system includes the hypnosis, analgesia and neuromuscular blockade states, while the hemodynamic system includes the cardiac output and mean arterial pressure. Nociceptor stimulation is also described and acts as a disturbance together with predefined surgery profiles from a translation into signal form of most commonly encountered events in clinical practice. A broad population set of pharmacokinetic and pharmacodynamic (PKPD) variables are available for the user to describe both intra- and inter-patient variability. This simulator has some unique features, such as: i) additional bolus administration from anesthesiologist, ii) variable time-delays introduced by data window averaging when poor signal quality is detected, iii) drug trapping from heterogeneous tissue diffusion in high body mass index patients. We successfully reproduced the clinical expected effects of various drugs interacting among the anesthetic and hemodynamic states. Our work is uniquely defined in current state of the art and first of its kind for this application of dose management problem in anesthesia. This simulator provides the research community with accessible tools to allow a systematic design, evaluation and comparison of various control algorithms for multi-drug dosing optimization objectives in anesthesia

Robust fractional order PI control for cardiac output stabilisation

Drug regulatory paradigms are dependent on the hemodynamic system as it serves to distribute and clear the drug in/from the body. While focusing on the objective of the drug paradigm at hand, it is important to maintain stable hemodynamic variables. In this work, a biomedical application requiring robust control properties has been used to illustrate the potential of an autotuning method, referred to as the fractional order robust autotuner. The method is an extension of a previously presented autotuning principle and produces controllers which are robust to system gain variations. The feature of automatic tuning of controller parameters can be of great use for data-driven adaptation during intra-patient variability conditions. Fractional order PI/PD controllers are generalizations of the well-known PI/PD controllers that exhibit an extra parameter usually used to enhance the robustness of the closed loop system. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

Nociception level during anaesthesia : analysis and control

Tese de Programa Doutoral. Engenharia Biomédica. Universidade do Porto. Faculdade de Engenharia. 201

Electromyogram prediction during anesthesia by using a hybrid intelligent model

[Abstract] In the search for new and more efficient ways to administer drugs, clinicians are turning to engineering tools. The availability of these models to predict physiological variables are a significant factor. A model is set out in this research to predict the EMG (electromyogram) signal during surgery, in patients under general anaesthesia. This prediction hinges on the Bispectral Index™ (BIS) and the infusion rate of the drug propofol. The results of the research are very satisfactory, with error values of less than 0.67 (for a Normalized Mean Squared Error). A hybrid intelligent model is used which combines both clustering and regression algorithms. The resulting model is validated and trained using real data.Ministerio de Innovación y Ciencia; DPI2010-1827