Abstract
The quality of anesthesia is of great value to the risk of surgery and the recovery of postoperative patients. The evaluation of depth of anesthesia through EEG signals is an effective method to improve the quality of anesthesia. The difficulty of anesthesia depth evaluation lies in the lack of a relatively stable algorithm. In this study, the permutation and entropy algorithm are used as the system algorithm. Meanwhile, this study combines with the anesthesia depth assessment needs and actual conditions to improve the algorithm and realize the anesthesia depth detection function by extracting the feature parameters of different fields of EEG signals. In addition, this study analyzes multiple channels of EEG signals through adaptive neural networks to obtain quantified anesthesia depth values and compares this value with IoC monitors. Through experimental research, we can see that the method proposed in this paper has a certain practical effect.
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