Towards Intelligent Monitoring of Anesthesia Depth by Leveraging Multimodal Physiological Data
Keywords:
Multimodal physiological data, Deep learning, Anesthesia depth monitoring, Feature fusion, Intraoperative awareness, Intelligent medical platformAbstract
Anesthesia depth monitoring relates directly to patient surgical safety and postoperative recovery quality. The traditional single-modality monitoring method has disadvantages such as being interfered with, and the differences are very large for different people. Therefore, this article would like to use electroencephalogram, electrocardiogram, invasive blood pressure, pulse waveform and other physiological data as the research object to develop an intelligent monitoring platform which can monitor the physiological parameters of the patient. it utilizes deep learning algorithms, such as long short-term memory networks and sparse denoising autoencoders, to carry out multimodal feature fusion and dynamic analysis so as to improve the accuracy and robustness of anesthesia depth assessment. Experimental data proves that this platform’s accuracy in anaesthesia depth classification is 95%, which is far more accurate than using traditional methods. Reduce the dangers people are unaware of when doing the operation, and help the patient to recover faster after the operation as well as proving its importance in anesthettic safety for clinic.
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Copyright (c) 2025 Xiangang We, Sifang Lin, Katarzyna Pruś, Xu Zhu, Xiaoqing Jia, Rui Du
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