Fuzzy logic control theory in clinical anesthesia. Issue 3 (15th July 2021)

• Record Type: Journal Article
• Authors: Tian, Ye; Chu, Zheng; Ma, Gang
• Other Names: Gupta Deepak guestEditor.; Kose Utku guestEditor.; Castillo Oscar guestEditor.; Al‐Turjman Fadi guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-15
• Subjects: clinical anesthesia -- depth of anesthesia -- fuzzy logic control theory -- monitoring technology; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12761
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: In the context of big data, the application of fuzzy logic control theory to clinical anesthesia will produce more research directions and new technologies. This article mainly studies the application of fuzzy logic control theory in clinical anesthesia. First, after introducing the basic content of fuzzy logic control theory, the determination method of commonly used membership functions, the relevant knowledge of clinical anesthesia, and the fuzzy logic code rate control model, this article describes in detail the basic principle diagram of the clinical anesthesia control system and the clinical anesthesia process Mechanism model. The mathematical model of clinical anesthesia control system is constructed based on the data fusion technology of the parameters of anesthesia depth monitoring. The parameters in the model are adjusted by the time domain analysis method to measure the dynamic characteristics, and the stability of the system is analyzed by root locus method. The heart rate does not change significantly when it is lower than 1 MAC, and the heart rate increases when it reaches 1.5–2 MAC. Experimental results show that the application of fuzzy logic control theory to clinical anesthesia can reduce the risk of clinical anesthesia.