基于深度学习的患者麻醉复苏过程中的头部运动幅度分类方法

Deep learning-based classification of head movement amplitude during patient anaesthesia resuscitation

头部姿态估计在很多领域都有广泛研究,然而在医学领域,利用头部姿态估计去监测麻醉恢复室(PACU)中患者复苏的研究很少。现有的从单个图像中学习用于头部姿态估计的细粒度结构聚合网络(FSA-Net)存在收敛效果差、参数过拟合的问题。针对以上问题,利用300W-LP、AFLW2000和BIWI共3个公开数据集,对患者麻醉复苏过程中的头部运动进行监测,基于头部姿态估计提出一种患者头部运动幅度分类方法。首先,将FSA-Net其中一个stream的激活函数线性整流单元(ReLU)替换为带有泄漏修正线性单元(LeakyReLU),从而优化模型的收敛效果,同时用AdamW(Adam Weight decay optimizer)优化器替换Adam优化器,解决参数过拟合问题。其次,对患者麻醉复苏中头部运动幅度进行分类,分为小幅度、中幅度以及大幅度运动。最后,利用PHP(Hypertext Preprocessor)、Echarts(EnterpriseCharts)以及PostgreSQL实现数据可视化,绘制患者头部运动实时监测图。实验结果表明,在AFLW2000数据集和BIWI数据集上,改进的FSA-Net的平均绝对误差比原FSA-Net的平均绝对误差分别减小了0.334°和0.243°。改进模型在麻醉复苏检测中具有实际效果,能够辅助医护人员对患者进行麻醉复苏判定。

Head pose estimation has been extensively studied in various fields.However,in the medical field,the research on utilizing head pose estimation for monitoring patient recovery issues in the Post-Anesthesia Care Unit(PACU)is limited.Existing approaches,such as Learning Fine-Grained Structure Aggregation(FSA-Net)for head pose estimation from a single image,suffer from poor convergence and overfitting problems.To address these issues,three publicly available datasets,300W-LP,AFLW2000 and BIWI,were used to monitor the head movements of patients during anesthesia resuscitation,and a method for classifying the amplitude of patient head movements based on the estimation of head posture was proposed.Firstly,the activation function Rectifier Linear Unit(ReLU)of one of the streams of FSA-Net was replaced with a Leakage-Rectifier Linear Unit(LeakyReLU)to optimize the convergence of the model,and Adam Weight decay optimizer(AdamW)was employed instead of Adaptive Moment Estimation(Adam)to mitigate overfitting.Secondly,the magnitude of head movements during patient anesthesia resuscitation was classified into three categories:small,medium,and large movements.Finally,the collected data was visualized using Hypertext Preprocessor(PHP),EnterpriseCharts(EChart),and PostgreSQL to provide real-time monitoring graphs of patient head movements.The experimental results show that the mean absolute error of the improved FSA-Net is reduced by 0.334°and 0.243°compared to the mean absolute error of the original FSA-Net on the AFLW2000 dataset and the BIWI dataset,respectively.Thus,the improved model demonstrates practical effectiveness in anaesthesia resuscitation monitoring and serves as a valuable tool for healthcare professionals to make decisions regarding patient anaesthesia resuscitation.