Anesthesia Depth Monitoring Based on Anesthesia Monitor with the Help of Artificial Intelligence

Objective To use the low-cost anesthesia monitor for realizing anesthesia depth monitoring, effectively assist anesthesiologists in diagnosis and reduce the cost of anesthesia operation. Methods Propose a monitoring method of anesthesia depth based on artificial intelligence. The monitoring method is designed based on convolutional neural network (CNN) and long and short-term memory (LSTM) network. The input data of the model include electrocardiogram (ECG) and pulse wave photoplethysmography (PPG) recorded in the anesthesia monitor, as well as heart rate variability (HRV) calculated from ECG, The output of the model is in three states of anesthesia induction, anesthesia maintenance and anesthesia awakening. Results The accuracy of anesthesia depth monitoring model under transfer learning is 94.1%, which is better than all comparison methods. Conclusion The accuracy of this study meets the needs of perioperative anesthesia depth monitoring and the study reduces the operation cost.