This study will collect hemodynamic and physiological data in conjunction with raw EEG and Auditory Evoked Responses from patients undergoing general anesthesia for surgical procedures. This data will be analyzed off-line for correlation of EEG and hemodynamic changes that would be interpreted clinically as changes in anesthesia depth. Utilizing this information, digital signal processing techniques, adaptive control theory and fuzzy logic concepts will be applied to develop intelligent fuzzy adaptive control strategies for anesthesia. These control strategies will be evaluated by their ability to achieve the desired depth of anesthesia while maintaining patient safety. Quadratic cost functions related to the deviation of the hemodynamic and physiological/neurological indices from their safe and desired clinical values will be developed and used to determine anesthetic induced insult/trauma to the patient. Fuzzy cost functions will determine deviation of the estimated depth of anesthesia from the desired depth. These cost functions will form a basis for comparing the performance of the implemented control strategy to that of the attending anesthesiologist. The difference between the estimated and desired depth of anesthesia and the patients weighted Physiological/Neurological trends will be used to calculate the recommended anesthesia. The difference between the estimated and desired depth of anesthesia and the patients weighted Physiological/Neurological trends will be used to calculate the recommended anesthesia management scheme. Anesthetic doses will be monitored for safety and then implemented by the attending anesthesiologist. This study will enroll thirty adult patients of both sexes who are ASA physical status II or I and are scheduled for lumbar discectomy and/or laminectomy under general anesthesia. Surface EEC electrodes and ear phones for auditory stimulus will be placed and standard monitors of ECG, blood pressure, pulse oximetry and respiratory gas analysis will be connected. Baseline awake values will be obtained. Each patient's anesthetic induction will be conducted as determined by the staff anesthesiologist. The EEG, Evoked potentials, ECG, Pulse Rate, Blood Pressure, and the respiratory information will be collected from the OR instrumentation using a high-speed real-time computer.
The specific aims and benefits will be reduced workload instrumentation using a high-speed real-time computer.
The specific aims and benefits will be reduced workload through: 1) Intelligent and clinically relevant presentation of patient status information; 2) Real-time presentation/recording of succinct clinically relevant presentation of patient status information; 2) Real-time presentation/recording of succinct context sensitive patient/anesthetic status alarms and trend analysis: 3) interpretation of patient status into high-level recommendations on patient well-being and anesthetic management and 4) integrated control of anesthetic gents and a patient gases by the IAM/MS. Also, recorded surgical time histories and embedded reasoning could form the foundation of a computerized anesthesiologist patient simulator.
