Application of Intelligent Detection of Neural Signal in Depth Evaluation of Obstetrics and Gynecology Anesthesia.

Abstract

In order to evaluate the application of EEG intelligent detection in gynecological anesthesia depth, the application of ANGEL-6000 EEG depth monitor in laparoscopic gynecological anesthesia was proposed. This method was applied to 60 patients who underwent elective laparoscopic gynecological surgery in our hospital from February to August 2016. Inclusion criteria were ASA i ∼ ii; the average age was (37.8 ± 6.6) years from 20 to 50 years old; the average body weight was (51.53 ± 3.87) kg; conscious and no communication barriers; and patients without instrument ventilation. The patients were divided into observation group and control group according to the random number table method, with 30 patients in each group. The two groups were anesthetized with the same anesthetic drugs, and their consciousness index was monitored. IoC values were recorded before induction of anesthesia (T0), 5 min after intubation (T1), 5 min after operation (T2), intraoperative exploration (T3), at the end of operation (T4), 1 min before extubation (T5), and 5 min after extubation (T6). The dosage of anesthetic drugs, operation time, extubation time, and operation time of the two groups were statistically analyzed. Compared with the operation time of patients in the two groups, the extubation time, awake time, and time out of the operating room of patients in the control group were longer than the observation group. The IoC values of patients in the control group at T0 and T6 time points were lower than those in the observation group at each time point from T1 to T5. Comparison of perioperative dose of remifentanil and atracurium between the two groups was performed. The control group used more propofol dose in perioperative period. The application of neuroelectric signal in laparoscopic gynecological surgery to detect changes in perioperative IoC value can well reflect the level of consciousness of patients and reflect the effect of perioperative stimulation at different time points on the EEG of patients in real time.