Design And Implementation Of Anesthesia Depth Monitor System

Anesthesia is mainly in the process of medical diagnosis or treatment,use of drugs to suppress the patient’s discomfort,so as to achieve the purpose of painless surgery,implement effective anesthesia process can be thought of as patients can reverse the loss of consciousness,no intraoperative awareness,no pain stimulation reactions,so the anesthesia has become one of the key link of success operation.In order to reduce the influence of improper anesthesia on patients,precise anesthesia has become an important research content in the medical field and has important clinical application value.At home and abroad mainly through vital signs parameters and EEG(Electroencephalography,EEG)to monitor the depth of anesthesia,due to the depth of anesthesia monitoring vital signs parameter EEG information,lack of anesthesia and the anesthetic depth monitoring based on EEG signal is foreign monopoly products more,thus developed with independent intellectual property rights of anesthesia depth monitoring is of great significance.This paper designed a set of lead based on EEG signal depth of anesthesia monitoring,mainly divided into hardware design and algorithm design,hardware design is the core of the EEG signal acquisition,due to the separation of components of circuit components of many,used in construction do not facilitate carrying,the power consumption of the device is more big and the moderating effect of high frequency signal under anesthesia,seriously affect the gathering effect,this paper chose to use dedicated acquisition chip ADS1299,it integrates the basic function of traditional brain electrical signal acquisition circuit,can better realize the isolation high frequency interference,the hardware part is mainly to complete the circuit debugging and performance test,The performance test mainly includes gain,common mode rejection ratio,voltage,noise,bandwidth and power consumption.After the test,the parameters of EEG signal acquisition circuit accord with the standard.The algorithm mainly includes two parts: offline data processing and online data acquisition and processing.Offline data processing mainly involves pretreatment of EEG signals collected,multi-domain characteristic analysis of EEG signals after processing,and time-domain calculation of burst suppression ratio(BSR)related to deep anesthesia.Characteristic parameter of the proportion of high frequency energy and SynchFastSlow can obtain the phase coupling information of EEG components and reflect the deep anesthesia.Finally,the combination parameters are calculated by using the adaptive fuzzy neural network model to calculate the anesthesia depth index.Online data processing mainly USES the model of offline processing for online analysis,and compares with the IoC monitor of reference equipment.In this paper,the correlation between anesthesia depth index and IoC monitor index is 68.60%,which proves the effectiveness of this algorithm to some extent.