Particle Swarm Optimization Fuzzy Modeling and Closed-loop Anaesthesia Control Based on Cerebral State Index_Journal of Biomedical Engineering

Authors：

TANGJingtian ¹ ,  CAOYang ¹ , XIAOJiaying ¹ , GUOQulian ²

1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
2. Department of Anesthesiology, Xiangya Hospital, Central South University, Changsha 410008, China;

Corresponding author：

CAOYang, Email: caoyangbme@163.com

Keywords：

particle swarm optimization algorithm; fuzzy control; cerebral state index; closed-loop anaesthesia control

DOI：

10.7507/1001-5515.20140099

Video：

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Abstract Full text Figures/Tables Video References Cited by

Due to individual differences of the depth of anaesthesia (DOA) controlled objects, the drawbacks of monitoring index, the traditional PID controller of anesthesia depth could not meet the demands of nonlinear control. However, the adjustments of the rules of DOA fuzzy control often rely on personal experience and, therefore, it could not achieve the satisfactory control effects. The present research established a fuzzy closed-loop control system which takes the cerebral state index (CSI) value as a feedback controlled variable, and it also adopts the particle swarm optimization (PSO) to optimize the fuzzy control rule and membership functions between the change of CSI and propofol infusion rate. The system sets the CSI targets at 40 and 30 through the system simulation, and it also adds some Gaussian noise to imitate clinical disturbance. Experimental results indicated that this system could reach the set CSI point accurately, rapidly and stably, with no obvious perturbation in the presence of noise. The fuzzy controller based on CSI which has been optimized by PSO has better stability and robustness in the DOA closed loop control system.

Citation： TANGJingtian, CAOYang, XIAOJiaying, GUOQulian. Particle Swarm Optimization Fuzzy Modeling and Closed-loop Anaesthesia Control Based on Cerebral State Index. Journal of Biomedical Engineering, 2014, 31(3): 532-537. doi: 10.7507/1001-5515.20140099 Copy

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2.	KURITA T, DOI M, KATOH T, et al. Auditory evoked potential index predicts the depth of sedation and movement in response to skin incision during sevoflurane anesthesia[J]. Anesthesiology, 2001, 95(2):364-370.
3.	JANG J R. ANFIS:adaptive network based fuzzy inference system[J]. IEEE Trans Syst Man Cybern, 1993, 23(3):665-685.
4.	BAKER G W, SLEIGH J W, SMITH P. Electroencephalographic indices related to hypnosis and amnesia during propofol anaesthesia for cardioversion[J]. Anaesth Intensive Care, 2000, 28(4):386-391.
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6.	ANDERSON R E, JAKOBSSON J G. Cerebral state index response to incision:a clinical study in day-surgical patients[J]. Acta Anaesthesiol Scand, 2006, 50(6):749-753.
7.	SCHWILDEN H,SCHÜTTLER J,STOEKEL H. Pharmacokinetics as applied to total intravenous anesthesia. Theoretical considerations[J].Anesthesia, 1983, 38(Suppl):51-52.
8.	KENNY G C, WHITE M. A portable computerized infusion system for propofol[J]. Anaesthesia, 1990, 45:692-693.
9.	MARSH B, WHITE M, MORTON N, et al. Pharmacokinetic model driven infusion of propofol in children[J]. Br J Anaesth, 1991, 67(1):41-48.
10.	MAHFOUF M C. Generalised predictive control (GPC):a powerful control tool in medicine[J]. Control Theory and Applications, IEE Proceedings, 1997, 144(1):8-14.
11.	TING C H, ARNOTT R H, LINKENS D A, et al. Generalised predictive control of evoked potentials for general anaesthesia[J]. Control Theory and Applications, IEE Proceedings, 2002, 149(6):481-493.
12.	SHEPPARD C W. The theory of the study of transfers within a multi-compartment system using isotopic tracers[J]. J Appl Phys, 1948, 19(1):70-76.
13.	SHEINER L B, STANSKI D R, VOZEH S, et al. Simultaneous modeling of pharmacokinetics and pharmacodynamics:application to d-tubocurarine[J]. Clin Pharmacol Ther, 1979, 25(3):358-371.
14.	SCHNIDER T W, MINTO C F, SHAFER S L, et al. The influence of age on propofol pharmacodynamics[J]. Anesthesiology, 1999, 90(6):1502-1516.
15.	MAMDANI E H. Applications of fuzzy algorithms for control of simple dynamic plants[J]. Proc IEE, 1974, 121(12):1585-1588.
16.	ELKFAFI M, SHIEH J S, LINKENS D A, et al. Fuzzy logic for auditory evoked response monitoring and control of depth of anaesthesia[J]. Fuzzy Sets Syst, 1998, 100(1-3):29-43.
17.	ALLEN R, SMITH D. Neuro-fuzzy closed-loop control of depth of anaesthesia[J]. Artif Intell Med, 2001, 21(1-3):185-191.
18.	SHIEH J S, LINKENS D A, PEACOCK J E. Hierarchical rule-based and self-organizing fuzzy logic control for depth of anaesthesia[J]. IEEE Trans Syst Man Cybern, 1999, 29(1):98-109.
19.	SHIEH J S, KAO M H, LIU C C. Genetic fuzzy modelling and control of bispectral index (BIS) for general intravenous anaesthesia[J]. Med Eng Phys, 2006, 28(2):134-148.
20.	KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of the 4th IEEE International Conference on Neural Networks, Piscataway:1995:1942-1948.
21.	RASMUSSEN T K, KRINK T. Improved hidden markov model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid[J]. Biosystems, 2003, 72(1-2):5-17.

1. KISSIN I. Depth of anesthesia and bispectral index monitoring[J]. Anesth Analg, 2000, 90(5):1114-1117.
2. KURITA T, DOI M, KATOH T, et al. Auditory evoked potential index predicts the depth of sedation and movement in response to skin incision during sevoflurane anesthesia[J]. Anesthesiology, 2001, 95(2):364-370.
3. JANG J R. ANFIS:adaptive network based fuzzy inference system[J]. IEEE Trans Syst Man Cybern, 1993, 23(3):665-685.
4. BAKER G W, SLEIGH J W, SMITH P. Electroencephalographic indices related to hypnosis and amnesia during propofol anaesthesia for cardioversion[J]. Anaesth Intensive Care, 2000, 28(4):386-391.
5. ZHONG T, GUO Q L, PANG Y D, et al. Comparative evaluation of the cerebral state index and the bispectral index during target-controlled infusion of propofol[J]. Br J Anaesth, 2005, 95(6):798-802.
6. ANDERSON R E, JAKOBSSON J G. Cerebral state index response to incision:a clinical study in day-surgical patients[J]. Acta Anaesthesiol Scand, 2006, 50(6):749-753.
7. SCHWILDEN H,SCHÜTTLER J,STOEKEL H. Pharmacokinetics as applied to total intravenous anesthesia. Theoretical considerations[J].Anesthesia, 1983, 38(Suppl):51-52.
8. KENNY G C, WHITE M. A portable computerized infusion system for propofol[J]. Anaesthesia, 1990, 45:692-693.
9. MARSH B, WHITE M, MORTON N, et al. Pharmacokinetic model driven infusion of propofol in children[J]. Br J Anaesth, 1991, 67(1):41-48.
10. MAHFOUF M C. Generalised predictive control (GPC):a powerful control tool in medicine[J]. Control Theory and Applications, IEE Proceedings, 1997, 144(1):8-14.
11. TING C H, ARNOTT R H, LINKENS D A, et al. Generalised predictive control of evoked potentials for general anaesthesia[J]. Control Theory and Applications, IEE Proceedings, 2002, 149(6):481-493.
12. SHEPPARD C W. The theory of the study of transfers within a multi-compartment system using isotopic tracers[J]. J Appl Phys, 1948, 19(1):70-76.
13. SHEINER L B, STANSKI D R, VOZEH S, et al. Simultaneous modeling of pharmacokinetics and pharmacodynamics:application to d-tubocurarine[J]. Clin Pharmacol Ther, 1979, 25(3):358-371.
14. SCHNIDER T W, MINTO C F, SHAFER S L, et al. The influence of age on propofol pharmacodynamics[J]. Anesthesiology, 1999, 90(6):1502-1516.
15. MAMDANI E H. Applications of fuzzy algorithms for control of simple dynamic plants[J]. Proc IEE, 1974, 121(12):1585-1588.
16. ELKFAFI M, SHIEH J S, LINKENS D A, et al. Fuzzy logic for auditory evoked response monitoring and control of depth of anaesthesia[J]. Fuzzy Sets Syst, 1998, 100(1-3):29-43.
17. ALLEN R, SMITH D. Neuro-fuzzy closed-loop control of depth of anaesthesia[J]. Artif Intell Med, 2001, 21(1-3):185-191.
18. SHIEH J S, LINKENS D A, PEACOCK J E. Hierarchical rule-based and self-organizing fuzzy logic control for depth of anaesthesia[J]. IEEE Trans Syst Man Cybern, 1999, 29(1):98-109.
19. SHIEH J S, KAO M H, LIU C C. Genetic fuzzy modelling and control of bispectral index (BIS) for general intravenous anaesthesia[J]. Med Eng Phys, 2006, 28(2):134-148.
20. KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of the 4th IEEE International Conference on Neural Networks, Piscataway:1995:1942-1948.
21. RASMUSSEN T K, KRINK T. Improved hidden markov model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid[J]. Biosystems, 2003, 72(1-2):5-17.

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