Abstract
Methods We compare nine index values, select CNN+EEG, which has good correlation with BIS index, as an anesthesia state observation index to identify the parameters of the model, and establish a model based on self-attention and dual resistructure convolutional neural network. The data of 93 groups of patients were selected and randomly grouped into three parts: training set, validation set, and test set, and compared the best and worst results predicted by BIS. Result The best result is that the model's accuracy of predicting BLS on the test set has an overall upward trend, eventually reaching more than 90%. The overall error shows a gradual decrease and eventually approaches zero. The worst result is that the model's accuracy of predicting BIS on the test set has an overall upward trend. The accuracy rate is relatively stable without major fluctuations, but the final accuracy rate is above 70%. Conclusion The prediction of BIS indicators by the deep learning method CNN algorithm shows good results in statistics.
Highlights
IntroductionAs the depth of propofol anesthesia increases, explicit memory and implicit memory disappear one after another [1]
In clinical anesthesia operations, as the depth of propofol anesthesia increases, explicit memory and implicit memory disappear one after another [1]
We take the 8 characteristic parameters of SPE, ApEn, sample entropy (SampEn), synch fast slow (SFS), α ratio, SEF95, MPF, SpEn, height, weight, age, gender, and BIS indicators extracted from the clinical EEG data of the patient as the input of the network model for training
Summary
As the depth of propofol anesthesia increases, explicit memory and implicit memory disappear one after another [1]. BIS parameters are suitable for anesthesia monitoring, combined with a new method of convolutional neural network based on self-attention and residual structure to build a model, and combine different EEG signal parameters with machine learning algorithms to evaluate the anesthesia status can accurately quantify the patient’s anesthesia status. This method does not need to refer to the age of the patient and the anesthetic drugs used and can reliably predict the depth of anesthesia. The experimental results evaluated on the data set show that the method proposed in this paper provides better performance compared with ranking entropy, the ratio, and other traditional methods
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