Personalization anesthesia closed-loop control system

Abstract

The invention discloses a personalization anesthesia closed-loop control system. The brain electricity data are obtained by a signal collection end of a brain electricity data acquisition device, then the collected brain electricity data are transmitted to a brain signal pretreatment device for the removal of noises and outliers; then the pretreated brain electricity data are sent by the brain electricity signal pretreatment device to a brain electricity signal analysis device; the brain electricity signal analysis device and the closed-loop control system are connected and the pretreated data are analyzed, the expected anesthesia depth index RPE (Renyi permutation entropy) values are obtained, and the RPE values are transmitted to a closed-loop control system; the closed-loop control system is connected with an injection device and at the same time controls it to work, the next-step anesthetic infusion speed is calculated by the closed-loop control system, after the signals are received by the injection device, and the real time control of the anesthetic infusion speed is completed by the injection device. The system has the advantages of real-time monitoring, automatic control, and high accuracy and the like.

Description

technical field [0001] The invention relates to the field of medical anesthesia control, in particular to a personalized anesthesia closed-loop control system. Background technique [0002] In clinical operations, general anesthesia is a very important step to ensure the smooth implementation of the operation. In traditional anesthesia, anesthesiologists determine the injection dose and speed of anesthesia based on their own experience, but there may be overdose or underinjection of anesthesia, which may lead to serious complications, such as intraoperative awareness, Vomiting, cardiovascular disease, abnormal neural activity, and postoperative cognitive impairment. [0003] Most of the patient models of the present closed-loop control system of anesthesia are built based on the group pharmacokinetic model, but due to the individual differences among patients, such systems will have certain errors. At the same time, most of these systems use bispectral index (BIS) as the i...

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Problems solved by technology

[0004] After searching the existing literature, it was found that the paper was named FPGA Implementation of Explicit Model Predictive Control for Closed Loop Control of Depth of Anesthesia. The part about the anesthesia closed loop in this paper used a four-compartment pharmacokinetic model to regulate Drug input, and BIS as the most important index value of anesthesia depth, but there is no identification process for parameter optimization of the patient model, and it is not an individualized closed-loop control of anesthesia, which makes it difficult to solve the problem of individual differences in patients

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