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A Fuzzy Rule-Based Expert System to Determine Propofol Drug Dosage in Anesthesia

Melika Babaei, Sharareh R. Niakan Kalhori, Shima Sheybani, Hesam Karim
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Abstract

Introduction: Inadequate anesthetic, including under or over dosage, may lead to intraoperative awareness or prolonged recovery. Fuzzy expert systems can assist anesthesiologist to manage drug dosage in a right manner. Designing a fuzzy rule-based expert system to determine the Propofol anesthetic drug dosage was the main objective of this study.

Material and Methods: This is a retrospective study. Fuzzy IF-THEN rules were defined based on evidences and experts’ linguistic rules for Propofol dose determination. Fuzzy toolbox in MATLAB software was used to design the system. Validation of system conducted with calculation of mean absolute error (MAE) and root mean squared error (RMSE). Also, difference mean between actual and predicted doses was tested with paired t-test in SPSS V.26 software. Data from 50 ENT (ears, nose, and throat) surgeries were used to validate the fuzzy system.

Results: MAE for induction and maintenance doses was 0.128 and 1.95 respectively. RMSE for induction and maintenance doses was 0.228 and 3.383 respectively. Based on paired t-test result, there was no significant correlation between actual and predicted values (P>0.05).

Conclusion: Obtained value from test and validation of system demonstrated a high performance and satisfying accuracy of the system. Therefore, this expert system can be used as a decision support system to determine initial dosage of anesthetic drugs. It can also be used for anesthesia students to learn drug administration.

References

Diwase DS, Jasutkar RW. Expert controller for estimating dose of Isoflurane. International Journal of Advanced Engineering Sciences and Technologies. 2011; 9(2): 218-21.

Kashipara HT, Bhatt TV. Fuzzy modeling and simulation for regulating the dose of anesthesia. International Conference on Control, Automation, Communication and Energy Conservation. IEEE; 2009.

Lan J-Y, Abbod M, Yeh R-G, Fan S, Shieh J. Review: Intelligent modeling and control in anesthesia. Journal of Medical and Biological Engineering. 2012; 32(5): 293-308.

Hasani V, Farhadi M, Mirsadraee A. A comparative study of the effect of paracetamol and remifentanil on the depth of BIS-guided anesthesia in endoscopic sinus surgery. Razi Journal of Medical Sciences. 2007; 13(53): 67-72.

Zoughi T, Boostani R. Proposing the new methods to determine depth of anesthesia. Journal of Control. 2011; 4(4): 39-50.

Kalili GR, Sajedi P, Afsharyzadeh F. Depth of anesthesia determination by using bispectral index in patients underwent inhalation or total intravenous general anesthesia. Journal of Isfahan Medical School. 2008; 25(86): 78-80.

Musizza B, Ribaric S. Monitoring the depth of anaesthesia. Sensors (Basel). 2010; 10(12): 10896-935. PMID: 22163504 DOI: 10.3390/s101210896

Vinogradov VL, Likhvantsev VV, Subbotin VV, Larionov II, Petrov OV, Dulub VG. Bispectral index (BIS)--a new ideology in solving an old problem. Anesteziol Reanimatol. 2002; 1: 49-53. PMID: 11998389

Zarandi MF, Zolnoori M, Moin M, Heidarnejad H. A fuzzy rule-based expert system for diagnosing asthma. Scientia Iranica. 2010; 17(2): 129-42.

Baig MM, Gholamhosseini H, Harrison MJ. Fuzzy logic based smart anaesthesia monitoring system in the operation theatre. WSEAS Transactions on Circuits and Systems archive. 2012; 11: 21-32.

Shalbaf A, Saffar M, Sleigh JW, Shalbaf R. Monitoring the depth of anesthesia using a new adaptive neuro-fuzzy system. IEEE Journal of Biomedical and Health Informatics. 2018; 22(3): 671- 7.

Phuong NH, Kreinovich V. Fuzzy logic and its applications in medicine. Int J Med Inform. 2001; 62(2-3): 165-73. PMID: 11470619 DOI: 10.1016/s1386-5056(01)00160-5

Mirza M, Gholamhosseini H, Harrison MJ, editors. A fuzzy logic-based system for anaesthesia monitoring. Annu Int Conf IEEE Eng Med Biol Soc. 2010; 2010: 3974-7. PMID: 21097272 DOI: 10.1109/IEMBS.2010.5627987

Esmaeili V, Assareh A, Shamsollahi MB, Moradi MH, Arefian NM. Estimating the depth of anesthesia using fuzzy soft computation applied to EEG features. Intelligent Data Analysis. 2008; 12(4): 393-407.

Zadeh LA. Fuzzy sets. Information and Control. 1965; 8(3): 338-53.

Zadeh LA. Fuzzy sets, neural networks, and soft computing. Communications of the ACM. 1994; 37(3): 77–84.

Qaempanah Z, Arab-Alibeik H, Ghazi Saeedi M, Sadr-Ameli MA. A decision support system for boosting warfarin maintenance dose using fuzzy logic. Tehran University Medical Journal. 2015; 73(4): 271-80.

Miller RD, Pardo M. Basics of anesthesia. 6th ed. Elsevier Health Sciences; 2012.

Miller RD, Eriksson LI, Fleisher LA, Wiener-Kronish JP, Cohen NH, Young WL. Miller's anesthesia. 8th ed. Elsevier Health Sciences; 2014.

Eger EI. Characteristics of anesthetic agents used for induction and maintenance of general anesthesia. Am J Health Syst Pharm. 2004; 61(Suppl 4): S3-10. PMID: 15532143 DOI: 10.1093/ajhp/61.suppl_4.S3

Linkens DA, Abbod MF, Backory JK, Shieh JS. Closed-loop control of anaesthesia using fuzzy logic. In: Szczepaniak PS, Lisboa PJG, Kacprzyk J (eds.). Fuzzy systems in medicine. Physica-Verlag HD; 2000.

Durbin K. Propofol [Internet]. 2017 [cited: 15 Mar 2021]. Available from: https://www.drugs.com/propofol.html

Ilyas M, Butt MFU, Bilal M, Mahmood K, Khaqan A, Riaz RA. A review of modern control strategies for clinical evaluation of Propofol anesthesia administration employing hypnosis level regulation. Biomed Res Int. 2017; 2017: 7432310. PMID: 28466018 DOI: 10.1155/2017/7432310

O’Keefe RM, Balci O, Smith EP. Validation of expert system performance.Virginia Tech; 1986.

Yardimci A, Ferikoglu A, Hadimioglu N, editors. Depth control of sevofluorane anesthesia with microcontroller based fuzzy logic system. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2001.

Esmaeili V, Assareh A, Shamsollahi MB, Moradi MH, Arefian NM. Designing a fuzzy rule based system to estimate depth of anesthesia. IEEE Symposium on Computational Intelligence and Data Mining. IEEE; 2007.

Rabbani H, Mehri Dehnavi A, Ghanatbari M. Estimation the depth of anesthesia by the use of artificial neural network. In: Suzuki K (ed.). Artificial neural networks: Methodological advances and biomedical applications. INTECH Open Access Publisher; 2011.

Kumar A, Anand S, Yaddanapudi LN. Fuzzy model for estimating induction dose for general anesthesia. Journal of Scientific & Industrial Research. 2006; 65: 325-8.




DOI: http://dx.doi.org/10.30699/fhi.v10i1.304

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