Automatic Control of Anesthesia During Surgery Using Fuzzy Controller

محورهای موضوعی : Electrical Engineering

Maryam Goodarzian ¹ , Mohammad Reza Yousefi ² , Neda Behzadfar ³

1 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
3 - Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

تاریخ دریافت : 1401/06/25 تاریخ پذیرش : 1401/10/09 تاریخ انتشار : 1401/09/10

کلید واژه: Fuzzy controller, surgery, Depth of Anesthesia, Disturbance Effect, Drug Dose Adjustment,

چکیده مقاله :

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.

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