Artificial Intelligence and Electroencephalogram Analysis Innovative Methods for Optimizing Anesthesia Depth - Lin, Sifang; Hu, Ke; Ye, Tiehu; Wang, Yong; Shen, Zepeng (Journal of Theory and Practice in Engineering and Technology, 2024)
The purpose of this paper is to explore the potential of artificial intelligence and electroencephalogram (EEG) analysis in optimizing anesthesia depth monitoring. research methods include data collection and quality improvement, multimodal data fusion, algorithm generalization and robustness enhancement, and model interpretability improvement. The results show that the combination of AI and EEG significantly improves precision monitoring of anesthesia depth and personalized anesthesia management, reduces postoperative complications, and improves patient safety. The study shows that AI and EEG analysis have great potential for use in deep anesthesia monitoring, but further research and validation are needed in key areas such as data quality, algorithm generalization, and ethical safety. In the future, increasing the interpretability of models through multimodal data fusion is expected to further advance the widespread use of AI in anesthesia medicine to improve clinical outcomes and patient safety.