Automatic Control of Anesthesia During Surgery Using Fuzzy Controller

Journal of Applied Dynamic Systems and Control

دوره 5، شماره 2، اسفند 2022، صفحه 80-86   

نوع مقاله: Original Article

نویسندگان

Maryam Goodarzian؛ Mohammad Reza Yousefi^* ؛ Neda Behzadfar

Department of Electrical Engineering, Najafabad Branch, Islamic ‎Azad University, Najafabad, Iran

چکیده

Creating the desired depth of anesthesia is done by controlling the amount of anesthetic drug applied to the patient. Applying an excessive amount of anesthetic causes the patient to regain consciousness, and on the other hand, using an amount less than necessary causes the patient to perceive the painful stimuli caused by the surgery. In this article, using the lowest amount of drug as a control input, the desired depth of anesthesia (the desired value of 50%) is created as the output of the model in the patient. The aim of designing an improved control method to adjust the drug dose is to use the second type of fuzzy logic, which is more advanced and has higher accuracy and flexibility than the first type of fuzzy logic. In order to analyze the results of this research, the system has been simulated using MATLAB software, and the effects of disturbance and noise have been considered in the output of the model. The results show that the proposed control structure controls the model well. Based on the simulation done in MATLAB software, the use of type two fuzzy control structure can reduce the amount of fluctuations in disturbance and measurement noise by 25% compared to type one fuzzy method, and in the conditions Without disturbance and noise, the proposed method does not have any subjugation and at the same time, the amount of time to achieve the desired value is improved by 87% compared to the type one fuzzy method.

کلیدواژه‌ها

Disturbance Effect؛ Drug Dose Adjustment؛ Depth of Anesthesia؛ Surgery؛ Fuzzy Controller

مراجع

[1]   M. Dorvashi, N. Behzadfar, G. Shahgholian, “Classification of alcoholic and non-alcoholic individuals based on frequency and non-frequency features of electroencephalogram signal”, Journal Iranian Journal of Biomedical Engineering, vol. 14, no. 2, pp. 121-130, Sum. 2020.

[2]   N. Behzadfar, “A brief overview on analysis and feature extraction of electroencephalogram signals”, Signal Processing and Renewable Energy, vol. 6, no. 1, pp. 39-64, 2022.

[3]   S. Liu, W. Wei, G. Ding, J. Ke, F. Hong, M. Tian, “Relationshipbetween depth of anesthesiaand effect-siteconcentration of propofol during induction with thetarget-controlled infusion technique in elderlypatients”, Chinese Medical Journal, vol. 122, no.8, pp. 935-940, 2009.

[4]   M. Lotfi-Forushani, B. Karimi, G. Shahgholian, “Optimal PID controller tuning for multivariable aircraft longitudinal autopilot based on particle swarm optimization algorithm”, Journal of Intelligent Proced­ures in Electrical Technology, vol. 3, no. 9, pp. 41-50, June 2012.

[5]   B. Ahmadzade, G. Shahgholian, F. M. Tehrani, M. Mahdavian, "Model predictive control to improve power system oscillations of SMIB with fuzzy logic controller", Proceeding of the IEEE/ICEMS, pp. 1-5,  Beijing, China, Aug. 2011

[6]   F. Doctor, H. Hagras, V. Callaghan, “A type-2 fuzzy embedded agent to realizeambient intelligence in ubiquitous computing environ­ments”, Information Sciences, vol. 171, no. 4, pp. 309–334, 2005.

[7]   M. Babaei, S.R.N. Kalhori, S. Sheybani, H. Karim, “A fuzzy rule-based expert system to determine propofol drug dosage in anesthesia”, Frontiers in Health Informatics, vol. 10, Article Number: 89, 2021.

[8]   S.P. Arunachalam, S. Kapa, S.K. Mulpuru, P.A. Friedman, E.G. Tolkacheva, “Intelligent fractional-order PID (FOPID) heart rate controller for cardiac pacemaker”, Proceeding of the IEEE/HI-POCT, pp. 105-108, Cancun, Mexico, 2016.

[9]   J.J. Hsu, J.I. Wang, A. Lee, D.Y. Li, C.H. Chen, S. Huang, A. Liu, B.K. Yoon, S.K. Kim, T.J. Tsai, “Automated control of blood glucose in the OR and surgical ICU”, Proceeding of the IEEE/EMBS, pp. 1286–1289, Minneapolis, MN, USA, 2009.

[10]           M. Denai, M. Mahfouf, J. Ross, “A fuzzy decision support system for therapy administration in cardiovascular intensive care patients”, Proceeding of the IEEE/IFSC, pp. 1-6, London, UK, 2007.

[11]           D.S. Diwase, R.W. Jasutkar, “Expert controller for estimating dose of isoflurane”, International Journal of Advanced Engineering Sciences and Technologies, vol. 9, no. 2, pp. 218-221, 2011.

[12]           D.P. Zaharieva, L.H. Messer, B. Paldus, D.N. O’Neal, D.M. Maahs, M.C. Riddell, “Erratum to glucose control during physical activity and exercise using closed loop technology in adults and adolescents with type 1 diabetes”, Canadian Journal of Diabetes, vol. 45, no. 1. Article Number: 96, 2021.

[13]           T. Chidentree, U. Sermsak, “Biological systems drug in fusion controller using FREN with sliding bounds”, IEEE Trans. Biomed. Eng, vol. 53, pp. 2405–2408, 2015

[14]           M.E. Karar, M.A. El-Brawany, “Automated cardiac drug infusion system using adaptive fuzzy neural networks controller”, B­i­om­edi­cal Engin­eering and Computational Biology, vol. 3, pp. 1–11, Jan. 2011.

[15]           L. Merigo, N. Latronico, F. Padula, M. Paltenghi, M. Schiavo, A. Visioli, "Optimization-based design of closed-loop control of anesthesia", Automated Drug Delivery in Anesthesia, pp. 233-267, 2020.

[16]           N. Jamali, A. Sadegheih, M. M. Lotfi, L. C. Wood, M. J. Ebadi, "Estimating the depth of anesthesia during the induction by a novel adaptive neuro-fuzzy inference system: A case study", Neural Processing Letters, vol. 53, no. 1, pp. 131-175, 2021.

[17]           Y. Tian, Z. Chu, and G. Ma, "Fuzzy logic control theory in clinical anesthesia", Expert Systems, vol. 39, no. 3, Article Number: e12761, March 2022.

[18]           M. Schiavo, F. Padula, N. Latronico, M. Paltenghi, and A. Visioli, "Individualized PID tuning for maintenance of general anesthesia with propofol", IFAC-PapersOnLine, vol. 54, no. 3, pp. 679-684, Jan. 2022.

[19]           S. Chakravarty, A. S. Waite, J. H. Abel, and E. N. Brown, "A simulation-based comparative analysis of PID and LQG control for closed-loop anesthesia delivery", IFAC-PapersOnLine, vol. 53, no. 2, pp. 15898-15903, 2020.

[20]           E. Hosseini, G. Shahgholian, H. Mahdavi-Nasab, F. Mesrinejad, “Variable speed wind turbine pitch angle control using three-term fuzzy controller”, International Journal of Smart Electrical Engineering, vol. 11, no. 2, pp. 63-70, June 2022.

[21]           S. Nasr, H. Mahmoodian, “Insulin drug regulation by general type 2 fuzzy controller with alpha plane”, Journal of Intelligent Procedures in Electrical Technology, vol. 10, no. 37, pp. 39-48, 2019.

[22]           S. Dequan, G. Guili, G. Zhiwei, X. Peng, “Application of expert fuzzy PID method for temperature control of heating furnace”, Procedia Engineering, vol. 29, pp. 257-261, 2012.

[23]           H. Huang, S. Zhang, Z. Yang, Y. Tian, X. Zhao, Z. Yuan, S. Hao, J. Leng, Y. Wei, “Modified Smith fuzzy PID temperature control in an oil-repleni­shing device for deep-sea hydraulic system”, Ocean Engineering, vol. 149, pp. 14-22, 2018.

[24]           G. Shahgholian, M. Maghsoodi, M. Mahdavian, M. Janghorbani, M. Azadeh, S. Farazpey, "Analysis of speed control in DC motor drive by using fuzzy control based on model reference adaptive control", Proceeding of the IEEE/ECTI-CON, pp. 1-6, Chiang Mai, Thailand, June/July 2016, doi: 10.1109/ECTICon.2016.7561239.

[25]           F. Hedarpour, G. Shahgholian, "Design and simulation of sliding and fuzzy sliding mode controller in hydro-turbine governing system", Journal of Iranian Dam and Hedroelectric Powerplant, vol. 4, no. 12, pp. 10-20, Aug. 2017.

[26]           M. R. Yousefi, S. Hasanzadeh, H. Mirinejad, M. Ghasemian, "A hybrid neuro-fuzzy approach for greenhouse climate modeling", Proceeding of the IEEE/ICIS, pp. 212-217, London, UK, July 2010, doi: 10.1109/IS.2010.5548375.

[27]           M Montazeri, M.R. Yousefi, K. Shojaei, G. Shahgholian, “Fast adaptive fuzzy terminal sliding mode control of synergistic movement of the hip and knee joints (air-stepping) using functional electrical stimulation: A simulation study”, Biomedical Signal Processing and Control, vol. 66, Article Number: 102445, 2021.