A model-based control scheme for depth of hypnosis in anesthesia

Abstract In this paper we propose a model-based scheme to control the depth of hypnosis in anesthesia that uses the BIS signal as controlled variable. In particular, the control scheme exploits the propofol pharmacokinetics/pharmacodynamics model of the patient so that the estimated effect-site concentration is used as a feedback signal for a standard PID controller, which compensates for the model uncertainties. The tuning of the parameters is performed off-line using genetic algorithms to minimize a performance index over a given data set of patients. The effectiveness of the proposed method is verified by means of a Monte Carlo method that takes into account both the intra-patient and inter-patient variability. In general, we obtain a fast induction phase with limited overshoot and a good disturbance rejection during maintenance of anesthesia.

[1]  K. Leslie,et al.  Low Bispectral Index Values and Death: The Unresolved Causality Dilemma , 2011, Anesthesia and analgesia.

[2]  Michel M R F Struys,et al.  Performance Evaluation of Two Published Closed-loop Control Systems Using Bispectral Index Monitoring: A Simulation Study , 2004, Anesthesiology.

[3]  S L Shafer,et al.  The influence of age on propofol pharmacodynamics. , 1999, Anesthesiology.

[4]  Clara M. Ionescu,et al.  Advanced Model-Based Control Studies for the Induction and Maintenance of Intravenous Anaesthesia , 2015, IEEE Transactions on Biomedical Engineering.

[5]  Gade Pandu Rangaiah,et al.  Advanced Control Strategies for the Regulation of Hypnosis with Propofol , 2009 .

[6]  C. Rosow,et al.  Bispectral index monitoring , 2001 .

[7]  Antonio Visioli,et al.  Event-Based control of depth of hypnosis in anesthesia , 2017, Comput. Methods Programs Biomed..

[8]  Fredrik Granath,et al.  Mortality Within 2 Years After Surgery in Relation to Low Intraoperative Bispectral Index Values and Preexisting Malignant Disease , 2009, Anesthesia and analgesia.

[9]  Arturo Martínez Robust control : PID vs. fractional control design, a case study , 2006 .

[10]  Teresa Mendonça,et al.  Controlling the depth of anesthesia by a novel positive control strategy , 2014, Comput. Methods Programs Biomed..

[11]  J. Boelaert,et al.  Should clearance be normalised to body surface or to lean body mass? , 1981, British journal of clinical pharmacology.

[12]  Kristian Soltesz,et al.  On Automation in Anesthesia , 2013 .

[13]  C. Minto,et al.  Contributions of PK/PD Modeling to Intravenous Anesthesia , 2008, Clinical pharmacology and therapeutics.

[14]  Hugo Vereecke,et al.  Spectral Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect: A Comparison with Bispectral Index and Processed Midlatency Auditory Evoked Response , 2004, Anesthesiology.

[15]  D.A. Linkens,et al.  Adaptive and intelligent control in anesthesia , 1992, IEEE Control Systems.

[16]  Guy Albert Dumont,et al.  Quantifying cortical activity during general anesthesia using wavelet analysis , 2006, IEEE Transactions on Biomedical Engineering.

[17]  Chuang Liu,et al.  Design of Type-1 and Interval Type-2 Fuzzy PID Control for Anesthesia Using Genetic Algorithms , 2014 .

[18]  Julio E. Normey-Rico,et al.  Robust Predictive Control Strategy Applied for Propofol Dosing Using BIS as a Controlled Variable During Anesthesia , 2008, IEEE Transactions on Biomedical Engineering.

[19]  Hugo Vereecke,et al.  Ability of the Bispectral Index, Autoregressive Modelling with Exogenous Input-derived Auditory Evoked Potentials, and Predicted Propofol Concentrations to Measure Patient Responsiveness during Anesthesia with Propofol and Remifentanil , 2003, Anesthesiology.

[20]  Efstratios N. Pistikopoulos,et al.  Explicit hybrid model predictive control strategies for intravenous anaesthesia , 2017, Comput. Chem. Eng..

[21]  Naira Hovakimyan,et al.  Neural Network Adaptive Output Feedback Control for Intensive Care Unit Sedation and Intraoperative Anesthesia , 2007, IEEE Transactions on Neural Networks.

[22]  A. Absalom,et al.  Closed-loop Control of Anesthesia Using Bispectral Index: Performance Assessment in Patients Undergoing Major Orthopedic Surgery under Combined General and Regional Anesthesia , 2002, Anesthesiology.

[23]  G D Puri,et al.  Closed-Loop Anaesthesia Delivery System (CLADSTM) using Bispectral Index: A Performance Assessment Study , 2007, Anaesthesia and intensive care.

[24]  M. Janda,et al.  Clinical evaluation of a simultaneous closed‐loop anaesthesia control system for depth of anaesthesia and neuromuscular blockade * , 2011, Anaesthesia.

[25]  S. Shafer,et al.  The Influence of Method of Administration and Covariates on the Pharmacokinetics of Propofol in Adult Volunteers , 1998, Anesthesiology.

[26]  Efstratios N. Pistikopoulos,et al.  Model predictive control of anesthesia under uncertainty , 2014, Comput. Chem. Eng..

[27]  Manfred Morari,et al.  Challenges and opportunities in process control: Biomedical processes , 2001 .

[28]  G N Kenny,et al.  Closed-loop control of propofol anaesthesia. , 1999, British journal of anaesthesia.

[29]  A. Absalom,et al.  Closed-loop control of propofol anaesthesia using bispectral index: performance assessment in patients receiving computer-controlled propofol and manually controlled remifentanil infusions for minor surgery. , 2003, British journal of anaesthesia.

[30]  Tore Hägglund,et al.  Individualized closed-loop control of propofol anesthesia: A preliminary study , 2013, Biomed. Signal Process. Control..

[31]  Rafael Castro-Linares,et al.  Control Adaptativo Fraccionario Optimizado por Algoritmos Genéticos, Aplicado a Reguladores Automáticos de Voltaje , 2016 .

[32]  Guy Albert Dumont,et al.  Robust closed-loop control of hypnosis with propofol using WAVCNS index as the controlled variable , 2012, Biomed. Signal Process. Control..

[33]  Antonio Visioli,et al.  Inversion-based propofol dosing for intravenous induction of hypnosis , 2016, Commun. Nonlinear Sci. Numer. Simul..

[34]  Guy A. Dumont,et al.  Robust control of depth of anesthesia , 2008 .

[35]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[36]  Clara M. Ionescu,et al.  Optimized PID control of depth of hypnosis in anesthesia , 2017, Comput. Methods Programs Biomed..