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 manual systems, anesthetic operations could not be safety and comfort. For this reason, Fuzzy logic control is applied to control anesthesia. In this paper, an objective approach 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.

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

4. Fuzzy Logic-Based Anesthetic Depth Control 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 and 124 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.

5. Fuzzy Logic-Based Anesthetic Depth Control 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. Membership sets for blood pressure data

6. Fuzzy Logic-Based Anesthetic Depth Control Pulse rate data membership sets between 50 and 124 are examined in groups named as N1, N2, N3, N4, N5, N6, and N7. Membership of pulse rate

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

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

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