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Fuzzy Logic-Based Anesthetic Depth Control

This paper proposes an objective approach to administering anesthesia using fuzzy logic in surgical operations. Fuzzy logic input sets represent blood pressures and pulse rates obtained from patients during anesthesia. Utilizing fuzzy logic systems, this method enhances the safety and comfort of anesthetic operations by providing interval values. The study examines blood pressure and pulse rate data from patients, categorizing them into membership sets based on specific ranges to improve anesthesia control. Fuzzy rules are designed with inputs from anesthetists to determine anesthesia outputs, ensuring precise anesthetic depth control.

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Fuzzy Logic-Based Anesthetic Depth Control

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  1. Fuzzy Logic-Based Anesthetic Depth Control

  2. In most surgical operations, to anesthetize patients, manual techniques are used in hospitals. The manual systems work either ON or OFF situations. Because of not having interval values between ON and OFF in manualsystems,anesthetic operations could not be safety and comfort. For this reason,Fuzzy logic control is applied to control anesthesia. In this paper, an objectiveapproach of giving anesthetic to patients during surgical operation using Fuzzy logic is proposed. Fuzzy logic system inputs T and N represent blood pressures (mmHg) and pulse rates (p m−1), which are respectively obtained from patients during anesthesia. Anesthesia output (AO) represents fuzzy logic system output. Fuzzy Logic-Based Anesthetic Depth Control

  3. Two fuzzy logic input sets are used. One of them is the systolic bloodpressure of the patients, which are obtained in operation. The second input offuzzy logic set is pulse rates. The minimum and the maximum values (systolicblood pressure and pulse rate) are obtained from surgical operations in 10 min intervals from 27 patients Fuzzy Logic-Based Anesthetic Depth Control

  4. Blood pressure data membership sets between 80 and 194mmHg are examined in groups as named T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, and T11. Pulse rate data membership sets between 50 and124 are examined in groups named as N1, N2, N3, N4, N5, N6, and N7. Anesthesia output data membership sets between 0 and 4 are examined in groups named as A1, A2, A3, and A4. Fuzzy Logic-Based Anesthetic Depth Control

  5. Blood pressure data membership sets between 80 and 194mmHg are examined in groups as named T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, and T11. Fuzzy Logic-Based Anesthetic Depth Control Membership sets for blood pressure data

  6. Pulse rate data membership sets between 50 and124 are examined in groups named as N1, N2, N3, N4, N5, N6, and N7. Fuzzy Logic-Based Anesthetic Depth Control Membership of pulse rate

  7. Anesthesia output data membership sets between 0 and 4 are examined in groups named as A1, A2, A3, and A4. Fuzzy Logic-Based Anesthetic Depth Control

  8. Fuzzification is based on the rules that T and N inputs result in certain outputsaccording to the rule base. Anesthetist is consulted about input and output data in rule base. Fuzzy Logic-Based Anesthetic Depth Control Rule base for T and N fuzzy inputs

  9. The data given in below table have impossible conditions in human beings. These values are accepted as invalid conditions Fuzzy Logic-Based Anesthetic Depth Control

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